From 206e5cbfed531aac36abbf742a747eb6468ec34a Mon Sep 17 00:00:00 2001
From: Hudson Yeo <44338416+huddyyeo@users.noreply.github.com>
Date: Tue, 31 Aug 2021 15:34:17 +0100
Subject: [PATCH] first commit
---
.Rhistory | 0
README.md | 16 +-
code/.ipynb_checkpoints/mdp-checkpoint.ipynb | 951 ++++++
code/DQN | 1799 +++++++++++
code/IQN.ipynb | 862 +++++
code/QRDQN | 603 ++++
code/Risky_MDP.ipynb | 926 ++++++
code/blackjack.ipynb | 2116 ++++++++++++
code/c51.ipynb | 1395 ++++++++
code/mdp.ipynb | 951 ++++++
code/polgrad_pacman_combined.ipynb | 852 +++++
code/polgrad_risky_mdp.ipynb | 554 ++++
code/polgrad_wcw.ipynb | 1009 ++++++
code/safety_classifier_investigation.ipynb | 3021 ++++++++++++++++++
14 files changed, 15054 insertions(+), 1 deletion(-)
create mode 100644 .Rhistory
create mode 100644 code/.ipynb_checkpoints/mdp-checkpoint.ipynb
create mode 100644 code/DQN
create mode 100644 code/IQN.ipynb
create mode 100644 code/QRDQN
create mode 100644 code/Risky_MDP.ipynb
create mode 100644 code/blackjack.ipynb
create mode 100644 code/c51.ipynb
create mode 100644 code/mdp.ipynb
create mode 100644 code/polgrad_pacman_combined.ipynb
create mode 100644 code/polgrad_risky_mdp.ipynb
create mode 100644 code/polgrad_wcw.ipynb
create mode 100644 code/safety_classifier_investigation.ipynb
diff --git a/.Rhistory b/.Rhistory
new file mode 100644
index 0000000..e69de29
diff --git a/README.md b/README.md
index c36f414..d0f3acf 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,17 @@
# DRL Risk
-Repository for the individual projection: Distributional Reinforcement Learning and the meaning of uncertainty in the return distribution
\ No newline at end of file
+Repository for the individual project: Distributional Reinforcement Learning and the meaning of uncertainty in the return distribution.
+In the folder code, there are several Jupyter notebooks that contain the full code for various aspects in the project. Below, we describe the main files used in the report.
+
+| File | Description |
+| ----------- | ----------- |
+| c51 | Main c51 file for WCW env |
+| mdp | c51 agent for MDP env in Risk-aware action selection |
+| safety_classifier_investigation | All code used for investigating various SCs in report, except QSC |
+| DQN | QSC investigations |
+| blackjack | SC investigations on Blackjack |
+| Risky_MDP | Risky MDP env with c51 agent |
+| polgrad_risky_mdp | Multi-Agent SC on Risky MDP env |
+| polgrad_wcw | Multi-Agent SC on WCW env, including its variants such as Inverted WCW and MWCW |
+| IQN | IQN code, inclusive of monotonically increasing quantile function |
+| QRDQN | QRDQN code, for graphs |
\ No newline at end of file
diff --git a/code/.ipynb_checkpoints/mdp-checkpoint.ipynb b/code/.ipynb_checkpoints/mdp-checkpoint.ipynb
new file mode 100644
index 0000000..d7656c0
--- /dev/null
+++ b/code/.ipynb_checkpoints/mdp-checkpoint.ipynb
@@ -0,0 +1,951 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zp9phBMGIOvD"
+ },
+ "source": [
+ "code for investigating risk-aware action selection on a 4 state MDP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "U4LL8VVQmz5L"
+ },
+ "outputs": [],
+ "source": [
+ "from gym import spaces\n",
+ "class random_mdp():\n",
+ " def __init__(self):\n",
+ " self.s=0\n",
+ " self.action_space=spaces.Discrete(2)\n",
+ " def reset(self):\n",
+ " self.s=0\n",
+ " return self.s\n",
+ " def step(self,a):\n",
+ " if int(a)==0:\n",
+ " reward=np.random.normal(1,1)\n",
+ " else:\n",
+ " reward=np.random.normal(0.8,0.1)\n",
+ " self.s+=1\n",
+ " if self.s>=3:\n",
+ " isdone=True\n",
+ " else:\n",
+ " isdone=False\n",
+ " return self.s,reward, isdone, None\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "outputs": [],
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBE-ev78Hvmi"
+ },
+ "source": [
+ "## Network\n",
+ "\n",
+ "The parametrized distribution can be represented by a neural network, as in DQN, but with atom_size x out_dim outputs. A softmax is applied independently for each action dimension of the output to ensure that the distribution for each action is appropriately normalized.\n",
+ "\n",
+ "To estimate q-values, we use inner product of each action's softmax distribution and support which is the set of atoms $\\{z_i = V_{min} + i\\Delta z: 0 \\le i < N\\}, \\Delta z = \\frac{V_{max} - V_{min}}{N-1}$.\n",
+ "\n",
+ "$$\n",
+ "Q(s_t, a_t) = \\sum_i z_i p_i(s_t, a_t), \\\\\n",
+ "\\text{where } p_i \\text{ is the probability of } z_i \\text{ (the output of softmax)}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "outputs": [],
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 200), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200, 200), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(200, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "outputs": [],
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.95,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 8,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim = 1\n",
+ " action_dim = 2\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.tensor([state]).to(self.device).float() \n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ "\n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.tensor([state]).to(self.device).float()\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action\n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 200):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ "\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " def test_(self,printout=False):\n",
+ " self.is_test=True\n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ "\n",
+ " while not done:\n",
+ "\n",
+ " action = self.greedy_action(state)\n",
+ " if printout:\n",
+ " print(state,action)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " self.is_test=False\n",
+ " return score \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EMEkNhyJHvmk"
+ },
+ "source": [
+ "## Environment\n",
+ "\n",
+ "You can see the [code](https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py) and [configurations](https://github.com/openai/gym/blob/master/gym/envs/__init__.py#L53) of CartPole-v0 from OpenAI's repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jsi7CspeHvml"
+ },
+ "source": [
+ "## Set random seed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "outputs": [],
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gw0lyYO0dmaq",
+ "outputId": "63401ec4-cd4d-4713-ac2e-80280445ba03"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7EhNf_08dxoM"
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m_ELje9LE150",
+ "outputId": "f2eb6db6-650f-4af7-f00f-605be3943979"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ]
+ }
+ ],
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aChSfk4uHvmn",
+ "outputId": "66a797b9-8e79-456d-fc1c-b7fc2472b91c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "# parameters\n",
+ "env=random_mdp()\n",
+ "num_frames = 100000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay,gamma=0.9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "mC5KKHXLHvmo",
+ "outputId": "11118e76-fb10-49f7-841d-a03be989e0b8"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "agent.train(num_frames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ehzu1VRAgNPI",
+ "outputId": "3b151381-19db-4ef4-d82d-e574f2fcd3c6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agent.dqn.load_state_dict(torch.load('normal_mdp.pt'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "swY9x_K4d1hl"
+ },
+ "outputs": [],
+ "source": [
+ "torch.save(agent.dqn.state_dict(),'normal_mdp.pt')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 815
+ },
+ "id": "jEJ-blyFtVIJ",
+ "outputId": "c0b8bab9-bfcc-4860-ce18-7a44c89956e3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [2.8445053 2.6744578]\n",
+ "std dev: [1.6104404 1.347314 ]\n",
+ "Entropy: [3.515364 3.3332715]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1.2340649 1.3271438]\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=[6,3],dpi=300)\n",
+ "#plt.suptitle('State $[1,2]$ with $\\gamma=0.9$')\n",
+ "pos=torch.tensor([0])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('std dev:',np.sqrt(variance))\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(2):\n",
+ "\n",
+ " plt.subplot(1,2,i+1)\n",
+ " text='Action '+str(i)\n",
+ " plt.title(text)\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "plt.show()\n",
+ "#print('Argmax:',pol[tuple(pos)],'for pos:',str(pos.numpy()))\n",
+ "print(m-np.sqrt(variance))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "uZL_GVlhqEq-",
+ "outputId": "85aa02a6-7a5a-42af-84ad-128f89aeff6e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [2.8445053 2.6744578]\n",
+ "std dev: [1.6104404 1.347314 ]\n",
+ "Entropy: [3.515364 3.3332715]\n",
+ "Mean Q: [2.0731869 1.8455882]\n",
+ "std dev: [1.4629111 1.1183099]\n",
+ "Entropy: [3.4160085 3.074432 ]\n",
+ "Mean Q: [1.1683384 0.94158494]\n",
+ "std dev: [1.2219588 0.83429414]\n",
+ "Entropy: [3.1453402 1.2751778]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 576x864 with 0 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2400x3600 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig=plt.figure(figsize=(8, 12))\n",
+ "\n",
+ "_, ax = plt.subplots(3, 2,\n",
+ " sharex=False, \n",
+ " sharey=False,figsize=(8,12),dpi=300)\n",
+ "for i in range(3):\n",
+ " pos=torch.tensor([i])\n",
+ " p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " m_u=(p*u(agent.support.cpu().numpy())).sum(1)\n",
+ " y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ " ent=-(p*np.log(p)).sum(axis=1)\n",
+ " variance=np.sum(p*y.T,axis=1)\n",
+ " ra=m-np.sqrt(variance)\n",
+ " print('Mean Q:',m)\n",
+ " print('std dev:',np.sqrt(variance))\n",
+ " print('Entropy:', ent)\n",
+ " for j in range(2):\n",
+ "\n",
+ " \n",
+ " title='State $s_'+str(i)+'$: Action '+str(j)\n",
+ " ax[i,j].set_title(title)\n",
+ " ax[i,j].plot(agent.support.cpu().numpy(),p[j])\n",
+ " #ax.annotate(str(i)+str(j), xy=(0.75, 0.75), textcoords='axes fraction', size=12)\n",
+ " text='$Q$: '+str(np.round(m[j],2))+'\\n$Q-\\sigma$: '+str(np.round(ra[j],2))+'\\n$Q_U$: '+str(np.round(m_u[j],2))\n",
+ " ax[i,j].text(0.8,0.8,text, bbox = dict(facecolor = 'None', alpha = 1),size=12,horizontalalignment='center',verticalalignment='center',transform=ax[i,j].transAxes) \n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eqNJxk5Pur24"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "mdp.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/DQN b/code/DQN
new file mode 100644
index 0000000..67f4c4d
--- /dev/null
+++ b/code/DQN
@@ -0,0 +1,1799 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "colab": {
+ "name": "SC DQN investigation.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IkUT0AaOpms5"
+ },
+ "source": [
+ "Comparing DQN to DRL, using SC"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mptOyMKizAHW"
+ },
+ "source": [
+ "import torch"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U4LL8VVQmz5L"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " \n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IKz7cJfYo5e-"
+ },
+ "source": [
+ "class inv_cliff_walking():\n",
+ " def __init__(self,start_pos=[0,0],e=0.1,windy_x=[1,4],windy_y=[1,5],pos_max=[4,9],reward_region=5):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " \n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[0] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==0 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n",
+ "\n",
+ "Please see *01.dqn.ipynb* for detailed description."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " action_dim: int\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 200), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200, 200), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(200, action_dim)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " return self.layers(x)\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.95,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 2,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim =2# env.observation_space.shape[0]\n",
+ " action_dim = env.action_space.n\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0005)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ "\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 200):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " \n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ " #if reward>=0:\n",
+ " #self.epsilon=max(self.min_epsilon,self.epsilon*0.993)\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " \n",
+ " current = self.dqn(state)\n",
+ "\n",
+ " current = torch.gather(current,dim=1,index=action.unsqueeze(1))\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ "\n",
+ " target = self.dqn(next_state)\n",
+ "\n",
+ " target = torch.gather(target, dim=1,index=next_action.unsqueeze(1))\n",
+ " target = reward + (1-done)*self.gamma*target\n",
+ "\n",
+ " loss = torch.mean((target - current)**2)\n",
+ "\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JB9OrdL-vXOu"
+ },
+ "source": [
+ "Creating reference policy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ELSYBl1sab-p",
+ "outputId": "e8543e3e-0888-4b57-9ea6-4d947fd1d5bd"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "T7R34NVYag5W"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6BQf35F6ajJu",
+ "outputId": "a253c06c-69b6-4bb9-b80a-371254516528"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aChSfk4uHvmn",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c1921b4d-fdbc-45c1-e312-7932fdca354f"
+ },
+ "source": [
+ "env=cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "# parameters\n",
+ "num_frames = 100000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay)\n",
+ "agent.dqn.load_state_dict(torch.load('dqn_base.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 32
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zcwOjstJqeEs",
+ "outputId": "10078fb3-7683-41ac-f10b-bee765797a94"
+ },
+ "source": [
+ "env=inv_cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "# parameters\n",
+ "num_frames = 100000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "inv_agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mC5KKHXLHvmo",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "outputId": "78711346-6192-4d0d-8e2b-89c8ad326e30"
+ },
+ "source": [
+ "agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "ULWoA7PG1A2P",
+ "outputId": "74a12d87-1b3e-434f-e0e6-7d1b0340fa23"
+ },
+ "source": [
+ "inv_agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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zRBYev6Zh+ysqLQdgSTjUJTohE5vi0vHeL7EY3rsDvnxsSK02czYcQ/9u7TCoZ/sGxVDTmbyiOteXllfgtR8P4S9Dzsf8LQl4+bZ+aBLye9XN1f9ZD8CS3CgqqwAAvL36aFXS6OHPdmDniSwce/MWBAcJhk//PdkTnZAFAHjtx0MoLqvA1X06Ysofe6OsvALf7koCACzefhJRxzKqjgEAh09X707w9qojuOuqrrjh3Q2oz2PzolFYWo7MsyXVlt986bn1bmuttFyrEkYAqiWMAGDEjPUN2h+Aar+bStbHaAxHE0YAMOu3Y0waEREROcbhiXWcFRwkePnPfdGtfTNM+/EQxn68FZ88GIGOLZu643BERF6NSSMvdywtHy2bhuAco+LDEVlnS3A6pwh9O1uqO/KLy1we1x9nrEeHlk3x7eRhNtffbVR71Ewa7U3MRu9OLdGiad3/9AqNZM/BU7kAUFU90tCkUYmRVDl8Og/NQoPtVhBZV6fsS8yx2ebNlYcBWBIpqoot8RkYekGHBndxuuV/m+yuW7T9JGJO52L+lhOYv+UEAOCLrSex6ukRuOjcVtXa7knMxh2Rm6u9792pJXadzKpadiq7egJjX3I2Zv12DPHpZwEAUccycNtlnfHDvlNVbSoTRoD9BMrsDfGYvSG+vo8KwFLpZMtPB1Ic2p6IiIgIlol1nhSRxQAGwwMT6zw8vCc6t22Gvy3ejTEzo/DZwwPRy07FNhGRv/LU7GnUSNf99zcMfnNtg7a5PXITbnl/o5siskjIKMDOE1lIza27asZaXlEpRkVuxhMLd7kxshrHNBJmD3y6HY/Ni3bZfpfvPYVxH2/DkujE+htb2ZeUjfT8Ypvrfj1yBs8t3V+VLLK2OS4d5RWKf/1wsGrZ2pjUam3uiNyMSQt2Qq0KsYfVqKT5YutJlFVUr9QeMWM9Zqw60qDPQURERORK3jqxzk39zsXiiUNRUFKGOz+KwvbjnAiDiAILk0Z+KDHT8e4xzvrnN/scbltZ9bMvyXYlj6vkFZWix9QV+O/q6omQhh43OiETZeUVNtclZhYAALYfz6q2/ExeEeLO5Nvdp72EEQCczrGfgJu94Rh6Pb8Sn21OsNpXSa12m+LSq16r2u7Gf9KInYiIiMhbqOpYVT1PVUNVtauqzlXVWao6y1ivqvqEqvZS1ctU1XVPA+vRv1tbLJ08HO1bNMFfPtmGH/aeqn8jIiI/4fdJo8j1cThkdHEi19twNM1ucsKeClW8uTIGKXUkSXILLRVCxWXlDY6pMvnywbq4assru7w5Ym9iNu6atQX/XXO0znaV4wBVGvrvdbj+nd8cPs6Crb9XFdXVyS01t3ayadF2TgXr6/p0Yok7ERGRL+jeoTmWTh6G/t3b4q+LduOjX481+BqYiMgX+X3SaMaqI7jtA/d21Qp0lUmaP3+wCaNnbq6nNZBdUIo5G+Jx/Tu/YeL8aAyx0f3u3V8syZqa1TSncwqrqnzcRQGMMsYKOpqSV3djw7LdSegxdQXKa3T96jF1BV778RD2Jmajx9QVeOTz6g/FXvruANYfOQNVxdSl+10SfyVexni/Lu3qn9GPiMjb3fy/jZjy5U6zw3CZBVsS8H9f7TE7DPJCbZs3wYJHB+H2KzrjPz8fxgvfHbBblU5E5C8CYiDsCh+9e86ooyuTN9qf3LDuX/nFZVh9yDIuz/trY/HUdX1QWl6B6T8dRlre7589y2rWrcpp323NBLdsdxLaNmvi9I14YwYO/9yq21hNczcdR1io/fzsw5/twMz7r2zwMcn33dD3HLNDICJyWszpXMSc9p+q7pe+t4wf+O69/U2OhLxR05BgvHdvf3Rt1wwzfz2GU9mF+HDclWhZzyQvRES+KmC+3Q6dykWPjs3RvInvfOSXvz9YfyMvF7k+DjNWHcHxf99SZ7t31hzFI1f3xIajaZi76Xi1da+tOFSr/f6kHFzWtU3V+8tfXYXcooYlew4k5+C2DzbhlT/3rbetdRLLlpBg54r23FE9xYpp7zeqfxezQyAiIqIGCgoSPDvyYnRt1xwvfX8A987egk8fGtig2Y6JiHyF33dPq3TL+xvxxJeOzdqlqnh71REkZTX+Rn7XySzM35Jgc92P+07hl0OpNtclZRXg7VVHoKoo9YNyV+tZuTZbTeVuz29H0motq9nlCwD+/OGmqqeaH6yNdThh9Oryg3jk8x0AgNs/3AQA+NcPtZNSNQ1845eq15UDelsLqmtAIpMoO6h5vSZOJhu9kYiMFJEjIhInIlNtrP+7iBwSkX0islZEzrda96CIxBo/D3o2ciIiooYZN7g7PnkwAgnpZzE6cjOOODisARGRL/G/O5Y6rD+ShoT0szbXxabmIa+oFABwJDUPH66PczjJZMuYmVF2K4WeXLgbj823PeHDlC934cP1cVi6K7mq65Y9ZeUV2JuY3egYPWnmr8fw1KLd9bb7qgFT2D+/bL9llrR6Bqu29nlUAtYdPoM/f7Cp0d0W37BR+bQ30bkZ4dxR1s8B4L1bjw7N0STEv76CRSQYQCSAmwH0BTBWRGqW8u0GEKGqlwP4BsBbxrbtAbwCYDCAQQBeEZF27ohz/ZEz6DF1RbWur0Tk/fKLy/j/lrzOtRd1wpJJQ1Guirs+isJmq5lsiYj8gX/dsTjgj2//anP5De9uwNiPtwIAKoxCkmIbFSXuVlxqOeaK/afrbfveL7EYFbkZBxo4lpA1VcV/Vx+pcyYzV7CuOHKVsvLGV9I0dPwla/O2nMDbq39PVO06mYUSJ6vCvtvj+qlbR8+Mcvk+yXXCQoPNDsEdBgGIU9V4VS0BsBjAKOsGqrpeVSvLOLcC6Gq8vgnAGlXNVNUsAGsAjHRHkLN/OwYAiElhYpXIl4x4az0GvLbG7DCIaunXuQ2WTRmOzm2b4cFPt+ObnUn1b0RE5CMCLmlUlwPJnr+ByDxbgpMZtbvBrTt8pt5tD56yJD7O5DU84XPZq6sw7YdD2JuUgw/WxeFvi6tXAUW8/gteWGaZzWvqt/sw5M21WBuTih5TVyCqxhOU8gp1KnHl6+ob74gogHQBYF0umGQss+dRAD81clsiCjCZrDIiL9a5bTN8PXkohlzQAf/4ei/eXXMUygEmicgP+G3SqLxCMWPVYbPDqNfV/1mHETPWe/y4eUVl+HTz8arxgpKyCvHO6iNVJ7f0/GJ8ue0kAGDxjkSk5Bbh86gEAMC4T7ZV29eMVUdw2webPBe8lwmW2gMaqSo+XBdrQjREvkFE/gIgAsCMBm43UUSiRSQ6La32GGhERERmaR0Wis8eHoi7ruqK/62NxT++3mdzLEwiIl/it0mjTXHpiFx/zOwwAAALtiSgx9QVNge2Ligpd3r/p7KLkJ7fuGqXyi55ydmFeH9dHHadrD5GUo+pK+rdx4ZY/75xa0wV1emcompd2IgCRDKAblbvuxrLqhGR6wG8AOB2VS1uyLaqOkdVI1Q1Ijw83GWBExERuUJocBBm3HU5/n7Dhfh2VxIe+mw7cgpLzQ6LiKjR/DZpVF7hPVn9t362jOdTWOp8gqiotBzTfzqMgpLfZwt78bsDiHj9lzq2sq/m04/GlNFmF7jmRFhuZ4yio6n5Ltl/Y2yJz2hUFVUFy5EpMO0A0EdEeopIEwD3AVhu3UBEBgCYDUvCyLof7ioAN4pIO2MA7BuNZS53Y99zAYBTIxMRkVuICJ66rg/euecK7EjIxN2zopCcXWh2WEREjeK3SaOJ83eaHUKD/bD3FI6k1j1V5xdbT2DWb8fw0a+Nr6K69BW33Ic5bU2M7dnizBwzqLGVYOn5HHeBAo+qlgF4EpZkTwyAJap6UESmicjtRrMZAFoC+FpE9ojIcmPbTACvwZJ42gFgmrHM5Vo3CwUANAn221MgERF5gTFXdsW8hwfhdE4RRjs5eQ0RkVlCzA7AXcoaO5+6iT5cF1dvm1KjGseZ2bryi8vqb2SCMidnIPMmd0RuNjsEIlOo6koAK2sse9nq9fV1bPspgE/dFx0REZFnDevdEd9OHoaHP9uBe2ZvQeS4K3HtxZ3MDouIyGF8zErkAeytRkRERBSYLjynFZZNGYYLwlvgsfnR+HLbCbNDIiJyGJNGREREREREbtSpdRi+mjgUI/p0xAvLDmD6T4dR4YM9I4go8DBp5EI5haU4Vccgdy9/d8CD0ZA3ETE7AiIiIiIyU4umIfj4gQjcP7g7Zv12DH/7ag+KXDBRDhGROzFp5EI3vvsbhk1fV3uFkTD4bs+pRu87MbOg3jZrDtkeSNpfKfh0hoiIiIh8R0hwEF6/41JMvfli/LD3FMbP3Yass5xAhYi8F5NGLpSa675Zvu6bs7XeNhPmR7vt+N7Il8YJilzf+NnuiIiIiMh/iAgmXdMLH44bgL1JObjzoyiczKj/ATERkRmYNPIReUWlZodAREREREQuctvlnfHlY4ORWVCC0TM3Y/fJLLNDIiKqxSVJIxEZKSJHRCRORKbaWP+QiKSJyB7j5zFXHDeQ5BaV4bF5gVVJVB+OE0REREREvmxgj/ZYOnkYWjQNwdiPt+LnAylmh0REVI3TSSMRCQYQCeBmAH0BjBWRvjaafqWq/Y2fT5w9biD6JSawxiwiIiIiIvJ3F4S3xLIpw3DJea0x+cudmLvpuNkhERFVcUWl0SAAcaoar6olABYDGOWC/RIREREREfm9Di2bYtGEIbip77l47cdDeHX5QZRX+NAAnkTkt1yRNOoCINHqfZKxrKY7RWSfiHwjIt1ccFyfwV5UjinjiZGIiIiIAlRYaDAi778Sj17dE59HJWDyFztRWFJudlhEFOA8NRD2DwB6qOrlANYAmGevoYhMFJFoEYlOS0vzUHjuxVSIY8rKK8wOgYiIiIjINMFBgpdu64tX/9wXa2JScd/HW5Ge774ZmomI6uOKpFEyAOvKoa7GsiqqmqGqld92nwC4yt7OVHWOqkaoakR4eLgLwiMiIiIiIvIdDw3vidl/uQpHUnIxeuZmHEvLNzskIgpQrkga7QDQR0R6ikgTAPcBWG7dQETOs3p7O4AYFxyXiIio0ZRloERE5MVu7HcuFk8cisKScoyZGYXtxzPNDomIApDTSSNVLQPwJIBVsCSDlqjqQRGZJiK3G82eEpGDIrIXwFMAHnL2uERERI3BceaIiMhX9O/WFsumDEeHlk3wl0+2YfneU2aHREQBxiVjGqnqSlW9UFV7qeobxrKXVXW58fo5Ve2nqleo6rWqetgVx6Xavtv9e8/AxdtPmhgJERERERE5q1v75lg6eRj6d2+Lpxbtxsxf46AslyUiD/HUQNgBzZNPtZ/9Zl/V66lL93vwyJ7HcyURERERBYK2zZtgwaODMKp/Z7z18xE8v+wAJ5EhIo8IMTsAosbylqSRsK8LEREREblZ05BgvHtPf3Rt1wyR64/hVHYhIu+/Ei2b8paOiNyHlUYucjgl1+wQ6lVcVm52CERERERE1EhBQYJ/3nQx/j3mMmyKS8c9s7YgNbfI7LCIyI8xaeQiI9/baHYI9YpcF2d2CERERERE5KSxg7pj7oMROJFxFndEbvaJB9hE5JuYNAJwJCXP7BAcNi8qodHb5haVuS4QL8BuYURERIHtdE4hxn28FTkFpWaHQuRxf7yoE5ZMGooKVdz90RZsik03OyQi8kNMGgFIzCxw6/7FhdmNFA+Un46Zudntx/An3jK2EhERUaCZuf4Yoo5l4Pu9yfU3JvJD/Tq3wbIpw9GlXTM89Nl2fB2daHZIRORnmDQCEBzMkhVru05mmx0CERERERE5oHPbZlgyaSiGXNAB//xmH95ZcxTKp5pE5CJMGjVAYUk58opY/kxERERERN6jdVgoPnt4IO66qiveXxuLZ77ei5KyCrPDIiI/wPkZG+APb61Hen4xEqbfanYoREREREREVUKDgzDjrsvRvX1zvLPmKFJyivDRX65Cm2ahZodGRD6MlUYNkJ5f3KjtPFkeqmApKhERERFRIBIRPHVdH7xzzxXYkZCJu2dFITm70OywiMiHMWlERERERETkR8Zc2RXzHh6E0zlFuCNyMw4k55gdEhH5KCaNPMCVs6fVeyxwUG9P8+BfLxEREfmQ7IISs0OgADasd0d8O3kYmpfQzXMAACAASURBVAQH4Z7ZW7D+8BmzQyIiH8SkkRss3n7S7BB8UkM71nFSCCIiInKFjbFpWLHvtEv3uSMhE/2nrcHPB1Jcul+ihrjwnFZYNmUYLghvgUfn7cAXW0+YHRIR+RgmjYCGZyvqMXXpftfukGxizoiIiIhcYfzc7Xhi4S6X7nNvYjYAYPvxTJful6ihOrUOw1cTh+KPF3XCi98dwL9/ikFFBa+kicgxTBqR12AvLyIiIiIi12vRNARzxl+Fvwzpjtm/xeOpxbtRVFpudlhE5AOYNCIiIiIiaoDVB1Pw+IJos8MgapCQ4CC8NupSPHfzxfhx32mMn7sNWWc57hYR1Y1JIyIiIiKiBpi4YCdWHUw1OwyiBhMRPH5NL3w4bgD2JuXgzo+icCLjrNlhEZEXY9IIcHu/KHfNrlUY4CWl7M5GRERE5B9EZKSIHBGROBGZamN9dxFZLyK7RWSfiNxiRpz+4rbLO2PhY4ORVVCCMTOjsOtkltkhEZGXYtLIh22Nrz2wonJ4aI/jLG5Evonfl0T+g+di3yYiwQAiAdwMoC+AsSLSt0azFwEsUdUBAO4DMNOzUfqfiB7tsXTKcLQMC8HYOVs50x8R2cSkERERBRR3VX8Skefx/7PfGAQgTlXjVbUEwGIAo2q0UQCtjddtAJzyYHx+q2fHFlg6eRj6dm6NyV/uxNxNx80OiYi8DJNGRERERERkpi4AEq3eJxnLrL0K4C8ikgRgJYC/eiY0/9ehZVMsmjAEI/udi9d+PIRXlx9EeQXL94jIgkkjIifxKScRERGR240F8LmqdgVwC4AFIlLrXkZEJopItIhEp6WleTxIXxUWGozIcVfisat74vOoBEz6YicKSwJ7/FQismDSyM+IDw8PLcy+EBERkQMSMwtQZNKEIHsSs005rp9LBtDN6n1XY5m1RwEsAQBV3QIgDEDHmjtS1TmqGqGqEeHh4W4K1z8FBQlevK0v/nV7P6yNScV9c7YgLa/Y7LCIyGRMGgHgWKjeQTmKJREREdWjtLwCf3hrPZ5evMeU498RudmU4/q5HQD6iEhPEWkCy0DXy2u0OQngOgAQkUtgSRqxlMgNHhzWA7PHR+BIah7GfLQZcWfyzQ6JiEzEpBH5LKaYiIiIAk9ZueUK4NejZ+y2KS4rx0vfHUDW2RJPhVWvi178CY/N22F2GF5JVcsAPAlgFYAYWGZJOygi00TkdqPZMwAmiMheAIsAPKR84ug2N/Q9B19NHIrCknLc+VEUtsVnmB0SEZnEJUkjERkpIkdEJE5EptpY31REvjLWbxORHq44rrtwGmbfwOsEIiIisuWHvaexYOsJvLkyxuxQqhSXVeCXGPuJrkCnqitV9UJV7aWqbxjLXlbV5cbrQ6o6XFWvUNX+qrra3Ij93xXd2mLZlOHo2LIJxs/dju/31OwxSESBwOmkkYgEA4gEcDOAvgDGikjfGs0eBZClqr0BvAvgP84el/wPxzQiIiIiV6h8sMQJoIic0619cyydPBwDurfF3xbvQeT6OD64JQowrqg0GgQgTlXjVbUEwGIAo2q0GQVgnvH6GwDXiRdnCHx5MGlfxhMQERER+TpVRVRcut31qw+meDAaIue1aR6K+Y8Owqj+nTFj1RE8v2w/ysorzA6LiDzEFUmjLgASrd4nGctstjH6LOcA6OCCY7uGm3NEnkxBsWsdERERBZqlu5K8ZnrwL7aewLhPtmHl/tM2109csBPbj2d6OCoi5zQNCcZ79/bHk9f2xqLtiXh0XjTyi8vMDouIPMDrBsIWkYkiEi0i0WlpnBCB7Duckmd2CEREROQF9ibl4LUVh8wOAwBwIqMAAHAqu8hum+wC7xmgm8hRIoJ/3HQR/j3mMmyKS8c9s7YgJcf+v3Mi8g+uSBolA+hm9b6rscxmGxEJAdAGgM0h+FV1jqpGqGpEeHi4C8IjX+HFPRaJiIjIC83fcqLq9ZncYhMjIQocYwd1x9wHI3Ai4yxGz9yMwym5ZodERG7kiqTRDgB9RKSniDQBcB+A5TXaLAfwoPH6LgDrOEUm1cR/EkRERAQAWWdL8NzSfSgq9Y4uZ0RU3R8v6oQlk4aiQhV3f7QFm2Ltj+NFRL7N6aSRMUbRkwBWAYgBsERVD4rINBG53Wg2F0AHEYkD8HcAU509rkt5Sa6ChTZEREREwFurDmPR9kQs280pvom8Vb/ObbBsynB0adcMD322HV9HJ9a/ERH5HJeMaaSqK1X1QlXtpapvGMteVtXlxusiVb1bVXur6iBVjXfFcf2NKwptOPMbERER+boKY2Ims69qysorsHDbSZRXeMkTRiIv07ltMyyZNBRDe3XAP7/Zh3fWHGXvASI/43UDYfsjjtXjGP6eiHyfiIwUkSMiEicitapKRWSEiOwSkTIRuavGunIR2WP81OzmTETkcfO2nMDzy/bji60n6m9sh0JRXMZuduS/WoeF4tOHBuLuq7ri/bWxeObrvSgpqzA7LCJyESaNyGvwqQSRbxORYACRAG4G0BfAWBHpW6PZSQAPAVhoYxeFqtrf+Lndxnoi8hFb4zMw6I1ffH5K7spZznIKSxu8rfWzsP1JOa4KicgrhQYH4a27LsczN1yIpbuS8dBn2xv1/4aIvA+TRjaoiwc5YjKEiALEIABxqhqvqiUAFgMYZd1AVRNUdR8A0x9Bro05wy4nRG4yY9URnMkrxuHTnFUJAO6atcXsEIjcTkTw1+v64N17r8COhEzcPSsKSVkFZodFRE5i0oiIiFylCwDrUTCTjGWOChORaBHZKiJ3uDa02qb9eAifbjru7sMQ+aW9idlYtP2k2WF4JUdS0UxXkz8bPaAr5j0yCKdzijB6ZhQOJLPSjsiXMWnkZ1xdJeVJHNOIKOCdr6oRAMYBeE9EetVsICITjcRSdFpamtMHTM4udHofRIFoVORmPLd0v1v2HXcmDyv3n27Utg98uh2XvPRz1Xszr4t4VUOBbFivjlg6eRiaBAfhntlbsO5wqtkhEVEjMWkE1DqrcwYyc7AbH5HPSwbQzep9V2OZQ1Q12fgzHsCvAAbYaDNHVSNUNSI8PLxRQTI/TeTdrn9nA/IaORbShqNpKCwt99iDqI2xziWvl+5KwicbOakw+ac+57TCsieGoVd4Szw2LxoLnBhQnojMw6SRB7CChogCxA4AfUSkp4g0AXAfAIdmQRORdiLS1HjdEcBwAIfcFikRuVWF8SDI38cNGz93O9Lyihu9/d+X7MXrK2JcGBGRd+nUKgyLJw7BtRd1wkvfHcC/V8agws+/F4j8DZNGQMB0LGfuiojcSVXLADwJYBWAGABLVPWgiEwTkdsBQEQGikgSgLsBzBaRg8bmlwCIFpG9ANYDmK6qTBoR+ajdJ7MBICAqC4rLyl22r4T0sy7bF5G3aNE0BLPHX4XxQ87H7A3x+Ovi3Sgqdd3/GyJyrxCzAyDP8fbeX75akeWjYRO5haquBLCyxrKXrV7vgKXbWs3togBc5vYAicij0vMbX4UD+OY5dm4jB9j/+UAKJn2xE7PHX4Wb+p3r4qiIzBUSHIRpo/qhW/tmeHPlYaTmFOHjByLQrkUTs0Mjonqw0sjPcDwmIiIiIt9z6HQuACDG+JPI34gIJo7ohchxV2Jfcg7GfBSFExmsriPydkwaeRFffJrmShwIm4iIiBqP1xFEvuDWy8/DwscGI7ugBKNnRmHXySyzQyKiOjBp5EWYM/FN/HsjIiKimnh5QGRfRI/2WDplOFqFhWDsnK34+cBps0MiIjuYNPKAAC8gIiIiogBwKrvQ5Ah+v+JytItXfnEZouLS3RUQANtDB9R84FRYUo6jKXlujYPI2/Ts2AJLJw9Dv86tMfnLXfhkYzx7HhB5ISaN/IzyuRYRERGZ4M2V3jN1/LbjGQ61e3rxHoz7ZFu97corFLsb2IWmIQ8Nn1i4Cz8fTGnQ/on8QYeWTbFwwhCM7HcuXl8Rg3/9cAjlFbyfIfImTBrZwMSLOfhbJyIiIk86mupYdc//1sZi9MwofLszqc52ja222hrvWJKLyB+FhQYjctyVmPCHnvg8KgGPL9iJgpIys8MiIgOTRkREFLACfQICIndraE+TCi+qMNgWn4EP1sVVW/bM13vr3GbY9HWNOlZBSXmjtiPyF0FBghdu7Ytpo/ph3eFUjJ2zFWl5xWaHRURg0sgiQG4aHLk5MvMGyt6heVNHRO7CoROI3Cs+/SzmbjrucPvtCZlujKZhlu1ONjsEooDzwNAemD0+AkdS8zB65mbEneFYX0RmY9IIqNUvytaAhYHCG2+gvDEmIiIiql9aXjFe+/EQSssrHGpfs9LI6WsyE68hGntoXvdQoLuh7zn4auJQFJWWY8zMKHbfJDIZk0YBhBch7sFKKCIiIu+SmstuLUS+7IpubbFsynCEt2qKB+Zux/d7WPlHZBYmjYiIiIjIaeKhpyiXvPwzgLofhtUcC2XWb8dwzYz1tdrlF3OwXSJv1a19cyydPBwDurfF3xbvQeT6OCifghN5HJNGfiaQu9YRERGReSqvQBy5qdtyLAPH0886dbziMse6vAHA9J8O40RGQa3lmWdLnIoBYCU3kTu1aR6K+Y8Owqj+nTFj1RE8t3S/w91dicg1QswOgKgSr7mIiIh833cOdCMZ+/FWAEDC9FvdHY6X4lUPkaOahgTjvXv7o1u75vhwfRxO5RRh5v1XomVT3soSeQIrjbwIx8YhIiIiX5ecVWh2CF6hqvKqgQmi/62Nxd7EbNcHROTDRAT/uOkiTB9zGTbHpePuWVuQklNkdlhEAYFJIw/wZDKooRcmRERk3/rDZ3DXR1G1ZnQiIqqp1sxvTlwAvvDdfmfDIfJL9w3qjk8fGoiTGWcxeuZmHE7JNTskIr/nVNJIRNqLyBoRiTX+bGenXbmI7DF+ljtzTH/GPvFERN7liYW7EH0iC4Wl5WaHQkRe7rUVh6q9T85mxRWRO1xzYTi+njQMqsBdH23Bxtg0s0Mi8mvOVhpNBbBWVfsAWGu8t6VQVfsbP7c7eUwir8JkH5Fv4YQBRP7K3BPyZ5sTTD0+USDp27k1lj0xDF3bNcPDn+3AkuhEs0Mi8lvOJo1GAZhnvJ4H4A4n9+cV/LWLF8dMIiKqjt+LRK7jlv9PLt7nmby6x0BJzPx9hjU+FCLybue1aYavJw3F0F4d8Ow3+/DO6iMOzd5IRA3jbNLoHFU9bbxOAXCOnXZhIhItIltFxGcSS870RfdGvvsd6rOBExERBQxfuGoa9MZanMm1nzgaFbnZg9EQkbNahYXi04cG4p6Irnh/XRyeWbIXJWUVZodF5FfqnadQRH4BcK6NVS9Yv1FVFRF7d/fnq2qyiFwAYJ2I7FfVY3aONxHARADo3r17feG5VSBmqr0zT+aVQVXxzt8ZERGR9/CmU2V6fgnWHj6DS85rXWtd5tkSEyKqLQAvQYkaLTQ4CP+583J0a9cc/11zFKdzijBr/FVo0yzU7NCI/EK9SSNVvd7eOhFJFZHzVPW0iJwH4IydfSQbf8aLyK8ABgCwmTRS1TkA5gBARESEKadM14934fpLJXeNyWHmRYq9YzMpQ0REFBjsXYacLS5zYGvHLxieW8rZyYj8iYjgr9f1Qdf2zfDsN/tw10dR+OzhgejarrnZoRH5PGe7py0H8KDx+kEA39dsICLtRKSp8bojgOEADtVs599cn4nx13GXbOHTNiJyl/q+X/j9Q/6otLwCI9/bgA1HfWfGoffXxZodAhH5gNEDumL+I4ORmluE0TOjsD8px+yQiHyes0mj6QBuEJFYANcb7yEiESLyidHmEgDRIrIXwHoA01U1wJJGnuPLswLx3oyIvFVjKx4rKhQ5haWuDYbISSk5RTickofnl5lTbRObmtfgbUrLGneV4My1hTsrnZmQJnKfob064NvJw9AkOAj3zN6CtTGpZodE5NOcShqpaoaqXqeqfVT1elXNNJZHq+pjxusoVb1MVa8w/pzrisDJtkCqQCIi8nZvrz6CK/61GtkF3jFOCpE7VU4gUl9CZML8aA9E41kxp3PNDoGIrPQ5pxWWPTEMvTu1xIT50Viw9YTZIRH5LGcrjYiIiHzWsbR8t+5/5X7LBKNZBaw2Iv9XWFJud50nHmn9YmI1wc3/2+jS/R08lYvcIn5vEDmjU6swfPX4EPzp4k546bsDeHNlDCoq+ICdqKGYNPIiHPCZiMizNsammx0Ckd8oLLWfNGosZy+NfvOhcZtqmv2bzTljiKgBmjcJwezxEXhg6PmYsyEef120G0Vu+K4i8mdMGsETXbq8IxvEpBQREVFgOnQqFxqAA+lknmXXUKJAFxwk+Nft/fDCLZdgxf7TuP+TbfxuIGoAJo28SABey/mF9HyedIj8FceJI3+w5VgGbnl/I+ZFJQDwresNccMTLz5EIwo8IoIJIy7AzPuvxP7kHNz5URQS0s+aHRaRT2DSKID40kWiL3n2m31mh0BEbubLM1MSnciw3BjFnM7D8r2nMGLGegCuT578djTNbrePZbuSXXswDzD7uik5q5DdaIhc7JbLzsOiCYORXVCCMR9FYeeJLLNDIvJ6TBqh9s0Anyx7G/59EBERucIXW9w7g9C0Hw/ZXP7st3zA0lDf7TmFx+b530xz9ojISBE5IiJxIjLVTpt7ROSQiBwUkYWejpH8w1Xnt8fSKcPROiwE4z7eip+MSSuIyDYmjergjpJobxeAH5mIiIhc5GRGQb1t+CjIcZviAmOwfhEJBhAJ4GYAfQGMFZG+Ndr0AfAcgOGq2g/A0x4PlPxGz44t8O3kYejXuTWmLNyFTzbGB+S4b0SOYNLIA3wpEeOd35U+9AskIiIilzmZWX8Syl3WHTnjtn175eWWuQYBiFPVeFUtAbAYwKgabSYAiFTVLABQVff9BVFA6NCyKRZOGIKR/c7F6yti8Orygyiv4P9OopqYNKoDs82eZe/37UtJNyIiIn+yYEsCekxdYdrYOu+vjcXamFRTjp2WV2zKcQNUFwCJVu+TjGXWLgRwoYhsFpGtIjLSY9GR3woLDUbkuCsx4Q89MW/LCTy+YCcKSsrMDovIqzBpZAMHPPUuzN0Rka/jQwgykzP/+j5cHwcAyC4odU0w9bD1X+VwSp5Hjk1eLwRAHwB/BDAWwMci0rZmIxGZKCLRIhKdlpbm4RDJFwUFCV64tS+mjeqHdYdTcd+crTiTV2R2WEReg0kjIiIiO5zN9QTi2HjkvWr+c3THQ7JNcen475qjLt+vL8srYtWCA5IBdLN639VYZi0JwHJVLVXV4wCOwpJEqkZV56hqhKpGhIeHuy1g8j8PDO2BOeMjEJuajzEzoxB3hglrIoBJIwDuny3NWx4w896FiKhx+P1JZFtZeYXZITSYp/8/V9i5EMwvZjLJyg4AfUSkp4g0AXAfgOU12nwHS5URRKQjLN3V4j0ZJPm/6/ueg68eH4Ki0gqMmRmFrfEZZodEZDomjeA9SR0iInI/JoCIXOex+YEzJbyrXfrKKrND8BqqWgbgSQCrAMQAWKKqB0VkmojcbjRbBSBDRA4BWA/gn6rKO3pyucu7tsWyKcPQqXUYxs/dhu921yx6IwosIWYHEAgcvUHhjQwRERH5kl+PuGbMmBvf/Q1JWYWYMz7C5np3V4WT+VR1JYCVNZa9bPVaAfzd+CFyq27tm+PbScMwcUE0nv5qD5KzCzHlj73Y7ZwCEiuNAggrqoiIiMhsJzIKai07mpqPghJzZmgjIrKlTfNQzH90EO7o3xkzVh3Bc0v3o9QHu+QSOYuVRl6ESR0iIiJytWrXF1YPyU9mFkBVvf7JuZeHR0R+rGlIMN69tz+6tW+OD9bF4VROEWbefyVaNuVtNAUOVhqBFyNERGQbc/nkT0RQa760hjywyjxbgthUz88mlJRViNjUfJftb/zc7S7bF9C4h358UEjkO0QEz9x4Ef5z52XYHJeOu2dtQUpOkdlhEXkMk0Y2sN88EREBqMoa8eECEXDjuxtww7sbPH7chdtOYlNcusv2l5ZX7LJ9EVHguHdgd3z20EAkZhZg9MzNiDmda3ZIRB7BpFEdvL1c2x3M/Mh86kZERGSeNYdScekrq1BoZ2yh9HwmW2xZfSilzvVLd3HmJSJ/MeLCcCx5fChUgbtnbcGGo66ZDIDImzFp5AHuyMOIW/bKxA0RkTvwq5XM5GgF9fSfYpBfXIbk7NoDVbMK277XV8SYHQIReVDfzq2x7Ilh6NquGR75fAeW7Eg0OyQit2LSqA7qxRmUxly8BWDhFBGRqfi1S96l4f8i3fWQyp6dJ7I8ejwiosY4r00zfD1pKIb26oBnv92H/64+4tX3jkTOYNIItatrPH2BRHVjsouIXMnfuh6n5hbh2rd/RWJm7eoQImurD6YgKauw2jJv++/wzS4+sSci39AqLBSfPjQQ90Z0wwfr4vD3JXtRUlZhdlhELsekka/yw0S2veopJu2JyCy+0CXnm51JOJ5+Fgu3nzQ7FPJSlefRjLMlSM4urLtxzW2N/wNHXTh7WZ3Hc8N/uePpZ12/UyIiAKHBQZh+52X4x40XYtnuZDzw6TbkFJSaHRaRSzFpREREBMt04p9sjLdZXs4KVAok17+zASczqleuPfipa6ept6dmJZQrXPv2ry7fJxFRJRHBk3/qg/fu7Y+dJ7Jw56woVv+SX2HSCIFTyRIon5OIqDGe/WYvXl8Rg92J2WaHQmQK68uEpbuTTIuDiMgX3TGgC+Y/MhhncoswemYU9iXxeoL8g1NJIxG5W0QOikiFiETU0W6kiBwRkTgRmerMMf2ZN4wrwLwSEfk7e1+1uYVlAIBSjkdAVKeyctf9H+kxdQX2BmCi1he6vhJRww3t1QFLpwxDWGgQ7p29FWtjUs0OichpzlYaHQAwBsAGew1EJBhAJICbAfQFMFZE+jp5XCIi8kL1PSQQkREisktEykTkrhrrHhSRWOPnQc9FTUQN8fHG4y7d37e7WNVERP6jd6dWWDplGPqc0xIT5kdjwZYEs0MickqIMxuragxQ70w0gwDEqWq80XYxgFEADjlz7LpknS2pc/2B5BxUqCIsNBg5haXVBkiMTc1DQobl/eGUPBxOyUVeURnaNQ+tanPoVC6ahAQhp7AUrcJCcLa4zO6xYk7n4kxecdX7uDN5Va+3xmdUvU7JKUJ8AwZqtN6ntdLy6k+uDqfkVr1Oyy/G9uOZde53zSH72fCUnCKHYrP+jA1x+LTt7dLzbX9WIvIuVg8JbgCQBGCHiCxXVevv+5MAHgLwjxrbtgfwCoAIWIoedxrbcv5tIg+JT3PsOiS7oO7rLCKiQNepVRgWTxyCpxbtxkvfH0RiViGmjrwYQUFe0LWEqIGcSho5qAsA6/lTkwAMducBB7y2ps71t32wye66G96tXjQ18r2Ntdrc8n7tZfbc/L/qba9/5/f93zdna9XrIf9e6/A+AWDZ7mSH2lnHv2LfaazYd7rO9nM2xNtdF3vGsZlTrD9jQyx18DMRebOOLZsgPb/+G6r+3dp6IBqPq/chgaomGOtq9m+5CcAaVc001q8BMBLAIveHXZ07Oo1wTDkyE//5ERF5XvMmIZg9PgL/+uEg5myIR1JWAd65pz/CQoPNDo2oQertniYiv4jIARs/o9wRkIhMFJFoEYlOS0tr8Pb5dVT9EBG52xVdHUsG/WtUPzdHYgpbDwm6eGDbBrFbHGtjudPJHj5QJC+XkMEZfoiI3CU4SPCv2/vhhVsuwcr9Kbj/k23IrKdXDJG3qTdppKrXq+qlNn6+d/AYyQC6Wb3vaiyzd7w5qhqhqhHh4eEOHuJ3S9kvnoh8QNMQPmVqDGcfLDjCVqLIGyYqIHKHpKy6k0b2usMTEZFjRAQTRlyAmfdfif3JORgzczMSGjAsCZHZnB0I2xE7APQRkZ4i0gTAfQCWu+tgYbwRIyIyS4MeEjRmW2cfLNTFmbxQWXkF7pm9BVFx6S6Lh6imigpFQYlnK6ptVtu5OInK7ptEFAhuuew8LJowGDmFpRg9czN2nqh7rFkib+FU0khERotIEoChAFaIyCpjeWcRWQkAqloG4EkAqwDEAFiiqgedC9u+zm2buWvXREQOmTjiArNDMIszDwlWAbhRRNqJSDsANxrLPC46IRPvr41t0Dbp+SXYfjwT/7dkj5uiIgLeXBmDvi+vQmFJucv2GZuab7PaSNi3kojI5a46vz2WThmONs1CMfbjbVi5v+7xZom8gVNJI1VdpqpdVbWpqp6jqjcZy0+p6i1W7Vaq6oWq2ktV33A26LqEBvMih4jMdfOl59a5/sJzWnooEs+y95BARKaJyO0AICIDjYcNdwOYLSIHjW0zAbwGS+JpB4BplYNiu1p9N8P/XXMU76w56o5DEzmlcsIIV1YbTfvxEK7+z3qX7a8xVvCmiYgCSM+OLbB0ynBc2rk1nli4Cx9viIey5JK8mCe6p3lUSLDffSQicsIVDZylrEWThndxvaJbW3z/xHAAwIXntsKA7u3qbB/Ro32Dj+ErbD0kUNWXVXW58XqH8bChhap2UNV+Vtt+qqq9jZ/PPB27M+MWVU7CUF5zTjgib+DAzcj0nw57IBDbAnFQ2DgHZ6QlIv/UvkUTLJwwBDdfei7eWBmDV5YfRHkFE0fknfwuw9IrvIXZIRCRF1k6eViD2k+5trfN5Z3bhGGQnWTPgkcH4YpubfHNpKF45oYLAQDbn7+u3ooj8n6OXr699bPlhjs9n4MGewtVdWk3LnercOPNgiN7nvXbMYf3x65rznvvl4Z1gSUi/xMWGowPx16JiSMuwPwtJ/D4gmiPj1tH5Ai/Sxq1bd7E7BCIyIsEBwm+fGwwAGD/qzfW2fb9sQPw8PAeeGDo+bXWtW3eBCONJNCtl52HOeOvqlrXOiwUgKWCqLLasVPrMPTuZLsbGm+3fM+Kfafxt8W77a4v8KHkRKD4cttJXPLySczPbwAAIABJREFUz0jM9P4p5RMzC3DB8yvx7U7HZoDls2giIv8QFCR4/pZL8Nqoflh3+Azum7MVZ/KKzA6LqBq/SxoREVX65e8jAADDe3dEwvRb0cpI7thzTZ9wNG8SgmmjLrW5/qFhPbBowhBE3n8lbux3Lv7QpyM6tWpqd3/BQUwPeaPGdEN7+qs9+H7PKdcH4wIHknPMDsEr/XwgBQCQkOH90xrHnskDAPy4r+5/Y/xGISLyT+OH9sCc8RGITc3H6MgoxBnnBSJvwKQREbnUxee2Mu3Yf7uuDxKm31r1vnen2rF0bFk7ydM6LKTWsm7ta8/EGBQkGNqrQ9X7BY8OxvYXrrcbT7Azg+SQx7m3y437akN+MpIjgeZwSm7AjP9wMsP7q6WIiMg51/c9B189PgTFZRUYMzMKW+MzzA6JCACTRkR2hbBKpFF+fnoEFk4YjJhpIx3eZu8rN2Kh0YWssRZNGIK//skyHlGbZqH4Q5+ONtutevoPWPnUH+rd3/Inrq723laXtfpce3GnBm9D5nFHjs+T3yLWYx3nFJbik43+OxvL0dQ8jHxvI9718VnufjmUintmbUF2QandNj8fOI0RM9Zj3eFUD0ZGRERmuLxrWyybMgydWodh/Nxt+M6YNZPITEwaEdnBIpHfdWzZsLHChvXqiGZNgvH23Vfg/sHd62z7yPCeaNMsFMN6207yWLs3ohu+njTU5rqhvTpUjSe095UbseBR20moDi2bom/n1tUqkmxp16JJtTb3Dar7c9hyaZc2aNe87i5xRO7w8vcH8PqKGGyO88+nlKm5lvEe9iRmmxyJcx6bH43tCZl48bsDdtvsS7J0P4w5/XtXhYbmAhuTO6zrHMjzIxGR+3Rr3xzfThqGq85vh6e/2oMP18X67UMg8g1MGhHZEcizw9x62Xku2c9dV3V12b4A4MXbLsFAN0xXH2RUlamNLkTrnrkGG5+9ttH7XvP3a7Diqavrb0g+zdtuonMKLZUrpeUVJkdCjnBkIPWUnCKv+3dGRETu0aZ5KOY9MgijB3TB26uP4rml+3lOJ9PUHsiDiCwC+OK8e4fm1d737dwGG46mNWpfg3rWneSxNXbQuMHdsXDbyar39VUFOWvxxCFYtjsZbZrVrgq6INz2DGiO6tiyqc1xlMg8DfmvzSd75C0WbD3R4KpPZ6TkFnEgViIiEzUNCcY791yBru2a4YN1cTiVU4TIcQPqndiFyNVYaURkh6M3ll3b1U56eJtJ1/TCZw8PbPT2keMGVOsWtvaZaxzetrLLmD0PDu1R9XrplGHY8//bu+/4KOr0D+CfJ5se0mghECBg6C1A6NgAFVDhAAtgwXYo4Hl3XoOfno1DPdudBWliOTsKCAqIWLDQg9IhEHrvHQIk+f7+2NmwSbbvzs7s7uf9euXF7uzMfJ/ZGaY8+y1PXIdB7eq4XGbpmJ6Y98fL/RJ19LP2UdNaKRjTpxmEP+NHNEe7nykjMpo/x+APBYcwdMpSlPqQ/Pxo2S70euUnP0onIiJ/iQj+cn0TvDCoNRYXHsGtE5dg/8nzRodFEYZJIyInPM0fzBrVTZfyq8QFriJgRkocmmem+Lx8cnwMOmRXxfQRXfHHno3QoFoS7u2WXS559OTNzTF1WJ5X682tm1bWNAwA2tVLR1piLHLrprvsC6lWajya2W3PNCf9HJlV3aqJ7mci8gMrSIUmR4lrR/vSUVNaR0Z8sBKLtx7FheLANmlgep2IKLhu61AXb9/TAXuOn8eA8Yuxcf8po0OiCMKkEZETRvdp9KdejfDQ1Ve4nOfbR8vX+GlQPQkA8OItrZFt18RM4PtNvn2yqX39dPz5usaIihI8eXMLXGHXdOvebg3Qs1mGj6WUZ4kS/Ot3LQOyLjP6/ZUNjQ6BHDD6/7wuwnCTwpn75pDm2KElzEoSEQXdVY1rlNX8v3XiEp+7jiDyFpNGJuCoHxUzqJYUvL4TQpmnt84v3drG63W3qO26dlBOzfL97TzTvwVuy8tC/9w6lTuMdvCskRzvuDaT/azRFvcPKY76JXKkYt9E7r67h66+QreaXEayRJnjwS9SeVqLMHv0HNbYIb+s3HkcDcbMweHTF3xex8Qft9q98+2ADPRxPOnHbYFdIREReaRZZgq+GNUNdasm4t53V2Dait1Gh0QRgEkjIic8fbD09GY8I8W7zpB9ucmvmRyPF25pg9joqHKPFs766omp0N/QFTWSvC5z+oiu+GJk4BM7IoLRfZqiTd20gK/bZkhH503giIj8NfWXbVAKWL79WLnpZy4U4+CpIp/X62mtONt8njZnIyIi86uVGo9pD3ZGt5zq+Pv0NXhpfgEH7iBdhWXSaKCbTnQpNM195Er3MwWQp3VB9LwZd5W4urpxDQBA/WqO+8dJjLWUex8Xbak0T8XV/+/+Tl6PVNa+fjqquRkdbMrdefhH76aVPzD4AvfcwFa6j8xGoSNQ/aCXllY+roN9qIf7raPt+/yl8EhI3ijf9NrP6PTsdz4v7+l1x3ZMh+BXRERELiTHx2DqsDwM7lAXb/xQiD9/ugoXikuMDovCVFgmjSLNq4Nz0bNpTa+WmXK34w6Le3i5nmDKTI0PanlGj6TlqPguDauVvbb1XzRjRFeHy9Sy+75ErM0gP/p9J7wwqDUAoHeLWl6V7Y/rmmdgxDWV+2eKpOeYe7pmGx0CBcmID1eWvTb8PGJo6cHx05YjRofg1qPTVmFx4eU4dxw95+OaImGPEhGRJ2IsUXhuYCv87YYm+GLVPgx7ezlOnrtkdFgUhsIzaRRJT6KwPpA742zELGfL2N+OigQ+eeBKnTTX/eKkB7mPJQEwsG3gaq0FopNdR/vDvpZPQkzl2kT2ul5RvVw/Rrb1xVrC81RgJk/1a2F0CFQmsCe2g6eK8LNd4mL++oMBXT+5dvK8+W+QLxSXYuhby7D18BmX8xmdZCQiotAiIhh1bQ7+e3suVu48jkETF2P3MV9/mCByjE+KLnz76FUuP/9Dj5yAlFOxM2NHGlZ33tdMYqzzodnjY7zbxfb3q0oBj/Ro5PGy793X0auyTE+AW/KyjI6iHGeJNVuH2c6Gcrd/DLENVd+3dWbZNKP6uxjUzlzfL5EvXpxfYHQIFCKOn73o0XxTf9mO2av3VZp+5IzWobZ2yp7041Ys2XrU6Xps5/5QbMJHRESe+13bOvjffZ1w6FQRBry5GGv2nDA6JAojYZ00uqJGEn5/ZQOfl8+pmezyc2cP6N6qnZaAxhnuE0euOLsd1PtXS/uR32x97JhR31bOm2I50zjD9f43QtUqjmtbffWH7tj+XF+P1pFdPQnbnu2Lfm1qw1mNC/taUfWred85tie2PdsXd3epr8u6zWrcgJZ4pj9rHJmVJ+fLV7/dEoRI/MckwWWh+FWM/WoDHvn4N7fzPTdvE4ZMWVppeu///oRRH/5a9j4UvwMiIvJOlyuqYcbIroiPicLtk5bi2w2s+UyBEZZJI9u90chrctCvjY6dYgfwJizKx+TOt49eHbggUPnG0l1YoTJy+Jt3tPdq/htbZWLqsLyANCnzR6XynRxzIlLpgddV7FFOdlzFB8129dLw70Gt3Afqg6ioyjF76+e/X4svRgV+5Da93NGpPu7ukm10GOSH/3y72egQvMLmTsYqLTWm3E0HTmPO2v1l+585IyKiyJBTMxkzRnZFo4wqGP5+Pv63ZIfRIVEYCM+kkfbgK1K+2U2vZsHr5Lmel7WQGrhofgY4vuFLjosua9oWqF+V/3J9E+RqQ5yLuP91MiPFdefU2U5G9jK7bjnVkZYY3D6UAq1ckzM3D47OEkzXNKnpsvmj0epWTSw7Xok8FUp5lO82HsT+k+eNDsPU3O1OI/f3mQvFXs3//aZD+HTFroDHMWfN/oCvk4iIzKlmcjw+Gd4ZPZrWxBOz1mPcnA0OR3Yl8lRYJo1sPEl6eOMqL5pfRVsEs0Z1w49/u8aj+V+8tY2PUVk520xvk0m1UuPLRlbzZNHhVzX0av2hwpZw8ehhw8OvOCAPLjo9/FTs00jPh6ymtczX7I/IH3rmJO5/Lx83v75IxxJCk7Nz1Lg5G/DQ+ysdfxgi/jF9bdnrHUfOOpzH0yu77WsqOHjav6CIiCikJMZGY9JdeRjWpT6m/LwdD3/8K4oulRgdFoWosEwa+ZInevLm5m7neaxvs3LvS51kVW5snYnXBrdFm7ppHvcHUyXOdW0Oh8kfD55ULD60H/MmYeBq/Te2zsTrQ9p5vC77/pH81bZeYGqfePPtpSUGLn6nfEyCOtsOI36Bn/PIlSgc1yf4BROFqLLOj8mtKT9vx9frDxgdRsBc89JCo0MgIqIQZYkSPNWvBR6/sRnmrTuAoVOW4ijvKcgH4Zk00h6svemPJtqD5IqnD9jjh7ZDyzqpHpetp7ho50OwTx/RpWwkLRtB4GpnjR/aDq2y3H8Pd3auBwDo0TRwzQcDtQ0e9QeizRIdFfj/Tk0zfa+V483xX/H76nJFNQBApwZVfS7fGUuUINoSlqceClEHTxUB0LfGkCd2Hj2LLX7UCHE0CqLR26QX5XnrW1MpLlV4/bsgdqYeQt8NEREFnojggSsb4s2h7bB+3ykMmrAY253UYiVyxq8nNxG5VUTWi0ipiOS5mG+HiKwVkVUiku9PmZ6w3Uta+zQqF0nZqxcGtfZ+vV52Eu1Ol4bV/FrevnhbbN70pdS+flXUTXc8hDtg3T5Pkmn+siVbjLrx3/wv57VevI3pkZ6Nyr1/+x6n/y08ckWNKij4V2+7gHxbj7PtKBuOucL0rldUR8G/eqOTn8cokRlV/O9gG2XK13OQJ8t5kse++sWFuO4/P/kWRMXybD+eREDSwFGCfOfRs6a4Ka74/SsFvLxA387U7Wsmny7yrk8lIiIKT31aZeKj33fGqaJiDHxzEVbuPGZ0SBRC/P25fx2AgQA8ucu9VimVq5Ty7yk6QG5pn1XuvS8VU/ytzTK4Y12H0xeP7oGO2eVreAxom+VwXps+La1Dyr8U4L6RgvHA0blhVe1fYxIUsdHO/xt4u/mPXte43HtXNb08Zb8O+1H23PVX1SE73e26Xe3fQMROFArOXvSujf/2I2dx7OxFj+aNgJyNKV394kLsPWHtQDwch5t3tU0zf9sbvECIiChktK+fjhkjuiI1IQZDpizjIAnkMb+SRkqpjUqpgkAFo7/Ld1meJkMm3XV5qPaKyzjqhD4QNXNqpyWgt5YEAoBNY3vjkZ45Lpe5vUNdbBrbu1JNI0/6anImUDfardw01evUoBo2je2NbjnVA1Mg/E92pSVYR07zpHmdK4F+WPHm8MrLrooBbesAcN9Urb523LhKoBGR1bUvLcTVL/5gdBhEDrEPLApVItJbRApEpFBERruYb5CIKFetHIjIsezqSZgxshta1UnFqI9+xeSftgZsFG4KX8F6QlQAvhGRlSIyXPfC7A58T/8TOJvthha1UEsbVr7iY3fVpMtDsk+6qz0m3tkeC92Mlvb2PXmY80h3fPWH7i7n69nM2r9PrZR4xMdYHPatYz9NRBAfY0Fy/OUOtd+/vyMaZegzUlXFvpBcuatzfbfzxMdYa7X0a1Pb55gC5dXBuejbypq0C2Rtm0DUOLCI4ItR3QAA3Ru5H80vzk0SyJZMGn9HO0y+qz1qJsf7HyRRBDB7sx9b/0bFHGI3LFW8JfjLtNXIHj3HmGCIAkBELADGA+gDoDmAISJS6ZdPEUkG8EcAy4IbIVH4qJoUiw8f6IQbW2Xi2bmb8MSs9SguKTU6LDIx10N2ARCRbwHUcvDRY0qpWR6W010ptVdEagJYICKblFIOm7RpSaXhAFCvXj0PV++YR50Ye6HirXdKwuWv74YWjr6i8jaN7V2WHAGArYfPuF0mJtr5NjjavKS4aLSvn46VO4+7XTcA1EotnySwX6Wrry+nZhVs3H/KozK84e7xpkZyHA6fdv8rqj8J8/65dbxbwEVZ3h6Ctn6ulmw76vDzO7vUR83keGx45gYkxrr97+txHKkJMWjmwTFMFM58vWI4qslXWqrw/tKdKCo2ZnjbNXtOAgBeml+Aa5sEbpCBUBSO/TqN/6EQz/RvWfZ++q97DIyGKCA6AihUSm0DABH5BEB/ABsqzDcWwL8B/C244RGFl/gYC14f0hZZ6QmY9NM27DtxHq8PbevV8wVFDrc1jZRSvZRSLR38eZowglJqr/bvIQAzYb0wOJt3slIqTymVV6OG+5oUDtfh5PXVjX1bX6BuOO0TRnpK0MrxJHHyf32bufy84jpqJMcBAHo0tX6XTWu5r3HkaEQfe958v18+3B3v3ef08DFUII6Tj4d3dlmLy1YTyNMTurtjIBwfpojcqfiDgq//Dfq98Qumr3T8sD5v3QE8OXs9dh877+Pa/WOrCbXlkPsfJ0JdJJ7H/rdkp9EhEAVaHQC77d7v0aaVEZF2AOoqpVitjigAoqIEY/o2w9j+LfBDwSEMnrwUh04XGR0WmZDuzdNEJEmrSgoRSQJwPawdaOvHNmqM3aScmlVwU+vLTZ8qPjTo2ZZzwh3tcLOXza68GS690rJeLOoqkeXoK1n0jx4o+FdvDGibhXVP34AmtQLb/M1d6LVS431O/gVbh+x0tKtn1xm1h/tFjwcgd6tkAxYi763ZcxJ/+Wy1w8/OXjR387VIctfU5Viy1XHtTT0UXSrBrRMXY61W28teaRD6jWDXFBSORCQKwCsA/uLBvMNFJF9E8g8fPqx/cEQh7q4u2Zhydx62HDyDAeMXo/DQaaNDIpPxK2kkIgNEZA+ALgDmiMh8bXptEZmrzZYB4BcRWQ1gOYA5Sqmv/SnXHVvNFvuH76Q436va+XsD1qdVJl4f0ta7Mh08xidr2/D+/daaNs4SAf7E62z0tO451THnke6IjY4q6+enioffqTcJsHC61/3soa5IiLVLynm4cYG84XdbyytwRRGFvP0nfft1zVGi9++fr/EzGt9Ny9/tfqYw4sl57Pl5G3WPw2bd3pNYseM4nvpyfaXPHngvP2hxEIWYvQDshxXO0qbZJANoCWChiOwA0BnAbEedYQei1QJRpOnZLAPTHuyCiyWlGPjm4qD+2ELm5+/oaTOVUllKqTilVIZS6gZt+j6lVF/t9TalVBvtr4VSalwgAnfF1oQnOT6m7AHc05oW1avEOp2n4jqC0ebTPuGS/89e2DS2N5p72Am1vzVW7JdvUzcVLWr7NpJYxcTFhmdu8Hod65/2bhkjmiuY+dddd98HR00gAjYdCI9f1uwTVuGaGK54xnpu3saQ6Aj6/CX9+7ji2ZxC1AoAjUSkgYjEAhgMYLbtQ6XUSaVUdaVUtlIqG8BSAP2UUszEEgVIq6xUzBzZFRkp8bj77WWY+Rv7yyOrsBxfe3Sfpnjp1ja4qtHlIdw9SSJMHZaH2Q939zgpk1s3zdcQfRIXbSnXnMzbjr5naaNuGa1iss2Tmkje1hQLag7E091gwkRWoDuLJwoFFY/6cP5vEM7bZiMCTPpxm9FhEJEflFLFAB4GMB/ARgDTlFLrReQZEelnbHREkSMrPRGfj+iKvPpV8edPV+P177bwx2VyP3paKIqPseCW9lkAgBa1U9AmKxVP3tzC7XI9m2UAAL4Y1Q3FpZeHHbTddAf6v8s1TWqiWWYK/tgzx+k87poXeaONB0muCHi+iCi2o8ddYo7XAiLfeZKY4f8xg0VC9kzDY41ClVJqLoC5FaY94WTea4IRE1EkSk2IwXv3dcTo6Wvw8oLN2HP8PP41oCViLGFZ34Q8EPZ7Pj7GglkPd0du3TSX94z2N1mx0VEeNz3LSInzObbUhBjM++OVyKnpXWfSwbwfDLebz0Y1q/i8bJ+WToakD4XvKHKel4goRK3ZcwKbD3rfRPDpLyuOyG2sULgkEBERuRIbHYWXb2uDR3rk4NP83bjv3RU4XXTJ6LDIIGFZ08gZPRIgP/7tWl1HQ3FVQ8RtP02hcufqQ0Ljnq7ZqJoUiweubIDmT8x3Ot/vci+PWrf+6RsQbRE0edy3fthvblMb89YdcPq5s6Rkt5xquKNTfb9GxPNVtSRrH12edlpOFInCuRKKEecdb328fBf2nyzCa99tAQDseP5Gt8vYb5WvHZjrpaRUG4zD4DiIiIj8ISJ49PomyEpPxP/NXItbJy7BO/d2QGZqgtGhUZDxSVLjKr/iqjNtV0PW68V9PzV+rNuHdX35cHdcKLZ27vnR7zth6JRlvgfgoaf6uW9uCAB3d80uG/benxH0emlNF33x4QOdAcDjUQg6NqiKtxdtR6s6vnU8bu/P1zVGdvUkp7WkwvlhmciZisf9ur2ncOiUuRIPgRIK/8fHzFjr9TLnLurfobSvnpxlHTWtuFTh1Png/iq7/cjZgDZrJyIiuq1DXWSmxWPEB79iwPjFePueDmhe27M+gCk8hH3ztFBlazPqqPlbdJT1KaBWarzDZb2tYfTvQa28W6CCVlmpyMuuCgDoekV1PNC9AR68uqHDeYd2qudyXX/okYOG1ZN8iqNhDd+Wc2f1k9djwp3tdFm3vTu076Z3y1pY+XgvdGpYze91xsdYMKRjPacdXv97UGu0rJPi9FgiihSbD57xY2lzZGYK/dqG0LH72Dk89MFKr5YJ5h4q0JrYlZQqrN5zUrdyXlmwudK0a19aiGkrdutWJhERRaYrG9XAZw91AQDcNmkJftx82OCIKJiYNNK46hVer46wXamdloD/3N4GE+9sX+mz9KRYvDo4F+/c28HlOhzlCRb+9Rq8dXdeuWm3d6iHtMSYsvdJcdbaU31aZvoQOfD4Tc0xpk+zStNvbZ+FZwe4TlA1zkjG93+9xqdybU2xAi01IUb3jt/SE2Mwzu67qVbF976yvNEtpzq++sOV7NiOyA97jp8zOgQAwHebDuHshWKjw9Dd9iNnjQ7BFGzN+SracdQcxyMREYWXZpkp+GJUN9Stmoj73l2BT1fsMjokChI+KZrYgLZZTpMH/XProGayZ7VDBrarg79c1xgAkF09Cb2aO29qJQASY6Px2z+vw5M3N/c6ZlecNZNwNj3Bw6Z/jTOsnVv3y63jS1iGW/f0DVg8uqfRYRBFNH+a9Py85UgAI3Fuxq97cMN/fnI5T783fin33hx1oCLH/pPnjQ6BiIhIN7VS4zHtwc7ollMd/5i+Fi/NL3BZ+YLCA5NGEeCV23Lxh56NvFomPSkW0ZYowzrTfvOOdljw6FUezVuvaiIAoFaKZ0m0WaO6+RyXHqrERSMhNvh9YxFFKkeJarPd76zceQwHKnTw/Oi01WVNn5zZerh8LRxnTVMD7fDpC0Epx1fBqp1UXHL5QAqF/qSIiIi8lRwfg6nD8jC4Q1288UMh/vTpqrL+bSk8RWTSyBLl252c/VLrn74hMMFEgJ7NMpCZGo8Hrrzcz9GjWs0nZ/q2ykRWeqJX5dhnuV09/7Wpm+bVem2cHTWePmvyAYKInDlfoWPnQROWoNcrP/q93mCcdr7fdBAdxn2LhQWHglCab06ev4S1OvYvREREFEliLFF4bmAr/O2GJpi1ah/unrocJ88Fd/AHCp6ITBolx/s3aFxSrMWvkbiCJRC/nAci0VG9ShyWjOmJxhnJZdMe6dkISVrtGv+LYDaGiPyjd0Ujd83fHv9iXaVpZ0Kkf6Lfdp0AAKwxeVLGLH1PERERhQMRwahrc/Dq4Fz8tusEBk5YhN3HeK0NRxGZNHLEkwSLrZp/jeTgdFJMnqq883xNI61+8nqvl2HKiij0BbM9/oXikkrlbT8SGSOfBYLJWhKWY7ZmjkRERHrrn1sH/7u/Iw6fvoABby7C6t0njA6JAiwik0a+3tQlxlrw8q1t8PHwzoENSCdmbw4V6L42ArG+1IQY9zP5yOS7gyhiiIP/jcF61j9zoRhNHv8a//nW8chXgWY7LR4+fQGfrtiF00WXcPzsRV3KClbCxNcEnx7hlZaqcjXCJv20tew1z/lERBQpOjeshhkjuyI+xoLBk5diwYaDRodEARSRSaOKUhNicGNrz4aXH9Q+C5mpCbrEMaRjPdzRqZ4u644EnvZp5Miap67H2qc8r2XUP7e2lyWY+9dxokh39Iw+iZSKbAmb6Sv3BKU8WzL9wffz8Y/pa9HqqW/QduyCwJYR0LW5Z5baPK98U4AWT85Hyyfn43SRtR+HD5fZDT9s9l9uiIiIAiinZjJmjuyGRhlV8OD7+Xhv8Q6jQ6IAicikUcX7uNVPXo/aafokgrzx3MBWGDegldFhhCDnN+bubtk7NqgKAEiJj0FyvGe1jHq3qIVXB7d1+rmetZWISB9//Wy10SHo6kiQkmLB8O+vN/m03IxfyyfqSkoVii75PtrLa98X4ry2/OkiB/1PmSW7RUREFCQ1kuPwyfDO6NE0A0/OXo9/fbUBpaW8HoY68/fmrKNrm9RAyzqpbuermhSL/SeLfB51jczrf/d19HoZdx3aLhrdA8UlpZWm8+ghooqWbz+GlnVSWBPRC5sOnPZpuW83lh/d7Z53luPnLUew4/kbAxEWERERAUiMjcaku9pj7Fcb8NYv27H3xHn85/ZcxMdYjA6NfBTRSaN37vUsYfD2PR3w3cZDyEiJ1zmiwOKPnO55c/Jy1tKg4tdcJQRG1iMiY4kAB04W4bZJS9C3VS1dywkWdwl1s/l5yxFd17/a5KPJERER6cUSJXjy5ubISk/AuLkbcXDKUky5Ow/VqnBAqVAUkc3TvJWREo+hYd7X0Nj+LZGREofk+NBLeIy45gokxVrQIbtqpc+C+Qjj7bNZi9opGNOnqS6xEBlFRHqLSIGIFIrIaAefx4nIp9rny0QkW5ueLSLnRWSV9jdRvyB1W7NXzl60NmnatP90uarbgazGbdtUXZNH7LsHR89cxJDJS40Og4iIyDREBA9c2RBvDm2H9ftOYeCExdh+5KzRYZEPQi9D4AdbrZIeTWoaHElweHMibqqhAAAgAElEQVQff3Ob2ri5jfedO5tB+/rpWP9Mb6+WeXVwLlrUTtEpIscqju4255Erg1o+kd5ExAJgPIDrAOwBsEJEZiulNtjNdj+A40qpHBEZDODfAG7XPtuqlMoNatBB5qwGqH2tlIKDvjW/osB7Z9F2xEVb3P5w9P7SHViy7WiQoiIiIgodfVplomZKPH7/v3wMfHMRptydhzwHP/aTeUVUTaOEWAsWj+6B5we1NjoUsqNnbSBnebP+uXWQUzPZq3WxuR+RWx0BFCqltimlLgL4BED/CvP0B/Ce9vpzAD2lYkY1DF0sLt/PmastDua55ut1+7E0QMmOcDxHPv3lBvzfzLVGh0FERBTS2tdPx4wRXZGWGIuhby3DnDX7jQ6JvBBRSSMAqJ2WgNjoiNtsnzWtZa2N0zzTfYfhenjr7jxMH9HVkLKdESepqDB8XiLyVh0Au+3e79GmOZxHKVUM4CSAatpnDUTkNxH5UUTCqipevzcW4buNB4Ne7vFz1qHgneWoHvrgVwy2a1ZVWqpw/Kx3I62FfcaPiIiI/JZdPQnTR3RF6zqpGPXRr5j801aocPzFKQwxe0Iu9WqegR/+eg1ubJ2pWxmuHjh6Nc9A+/rpupUdCHxgIgqI/QDqKaXaAngUwEciUqkNqYgMF5F8Eck/fPiwTwUZ9X/2/vfyHXYWbcTt0qYDpxwOXf/Kgs1oO3YBjpy54PG6PIl/5c7j2Hb4jBcRms/F4lIUXSpx+Nm0/D1BjoaIiCj0VE2KxQcPdMKNrTLx7NxNeGLWeoejTpO5MGlEbjWonqTr+plfJgobewHUtXufpU1zOI+IRANIBXBUKXVBKXUUAJRSKwFsBdC4YgFKqclKqTylVF6NGjV02ITgEKd1FoGSAHaE7Uzv//6MCQu3Vpo+f/0BAMAxL2sb2ZSUKoz/oRBnLxSXmz5owmL0ePlHn9ZpFj1eXoim//za6DCIiIhCWnyMBa8PaYsHr2qI95fuxIPvr8S5i8XuFyTDMGkUhmxDvkdHRe7u/VPPRrBECXJqVgn4uivWFPD08S78e20hwgoAjUSkgYjEAhgMYHaFeWYDGKa9vgXA90opJSI1tI60ISINATQCsC1IcZvKd5uC34zNH/antq/W7MOL8wvw4vwCw+JxZYcfo7bsOX4+gJEQERFFrqgowZi+zTD2dy3xQ8Eh3D5pKQ6dLjI6LHIicrMKYey5ga3w995N0LlhaPRKr0cu5dqmNbH12b5Ijo8J2DrdJX2YE6JIp/VR9DCA+QA2ApimlFovIs+ISD9ttqkAqolIIazN0EZr068CsEZEVsHaQfZDSqljwd0C/Tlqur/n+Lly70t1qGkUrL7Gbc237H8x9Kfa+dy1+5E9ek5ZzaXCQ/6NLDflZ9/ykBU7MiciIiL/3dW5Pt4aloeth89gwPjF2MIRZE3Jr6SRiLwoIptEZI2IzBSRNCfz9RaRAhEpFJHRjuahwElPisXIa3KC9pDgLzZPIwofSqm5SqnGSqkrlFLjtGlPKKVma6+LlFK3KqVylFIdlVLbtOnTlVItlFK5Sql2Sqkv9YrRDOdG+xAulZQ/C544fynI0QTO3LXW5m35O45j5IcrsfvYOTz0wa9ln/d8eaHbdazceTlX+J8FmwEAe09Ya/n8bvziAEbruXFzNhhSLhERUbjr0TQDnw7vgoslpRg4YTEWbz1idEhUgb81jRYAaKmUag1gM4AxFWfQmhuMB9AHQHMAQ0SkuZ/lUhgw/rEtuCJte4nIN/9bstPl58Ulpaa8oVIAftxs7Zx825GzmLv2AIa9vRzf2o0at/Ww++ZhgyYsqTTt/MUSKKVw1s8+D3z9kWLOWg4NTEREpJdWWamYObIraqXEY9jbyzHzNw4wYSZ+JY2UUt9ozREAYCmsnZ5W1BFAoVJqm1LqIoBPAPT3p1wKD5FWwyjStpeIKivWmp7tPHrOqxpP9h1Lv/Z9IYZOWYal2456vLyeSWtXm3H6guMkz+aDp/Had1s8LqP/+EV4/ftCh837vPHRsl248bWfvV7uyJnLHYP/uus4dh8752JuIiIi8lZWeiI+H9EVefWr4s+frsZr322B8vfCTwERHcB13QfgUwfT6wDYbfd+D4BOASyXiIgoJDw1e71Py+07cbkT5q3a0PWHT18ISEwAsPPoWdSv5n6kTKUUNu4/jea1UwAAd01dhp+3HLF96FFZjR+fV9ZH0PUtMhzOs2HfqbIybGatqjgQn2/W7zvl1/ID3zSmiRwREVG4S02IwXv3dcTo6WvwyoLN2HP8HMYNaIUYC7tiNpLbb19EvhWRdQ7++tvN8xiAYgAf+huQiAwXkXwRyT98+LC/qyMT0+OX7+uaZ+CqxuYchpvN04jMwcj/i78U+tas7K6pyytNU/Csk+lth8/gyBnXCaZzF0s8iuOdRTvQ97Wfy2o5lSWMYK0BVZGjcu07le79X8e1fi462C5PmrYRERFRaIuNjsLLt7XBIz1yMC1/D+57dwVOF4Vuf4/hwG1NI6VUL1efi8g9AG4C0FM5rj+2F0Bdu/dZ2jRn5U0GMBkA8vLyWB+NvDLl7jzdy2AtSSIKtgOnLg9Da0t67TtxHjmPzXO7bI+Xf/S4nC2Hzrj83FZLZ/exc+jcsJrb9fl6vtS7OvovW9wn71glnoiIyBgigkevb4Ks9ET838y1uHXiErxzbwdkpiYYHVpE8nf0tN4A/g6gn1LKWQP/FQAaiUgDEYkFMBjAbH/KJTIG6woRkfFsfSE9P29T0MtWWu9sv+0+oWtn3MfPWfsQ0mugu39MX1P2evHWI9jgoMnaFwFqDkdERES+ua1DXbxzbwfsOX4evxu/COv3nTQ6pIjkb+PANwAkA1ggIqtEZCIAiEhtEZkLAFpH2Q8DmA9gI4BpSinfOnUgCmEmGOWbiExkl4k6U/a4Uo0230fLdmHolGW6xXPfu/m6rbuioVOWoa+DzrED2WcUERER+ebKRjXw+YguiBLBbROXlI3USsHj7+hpOUqpukqpXO3vIW36PqVUX7v55iqlGiulrlBKjfM3aCIiIl+ZJYE77O3K/RR54svV+wIcyeUaRERERERm07RWCmaO7IZ61ZJw37sr8MnyXUaHFFHYDTkZppPWH0ZMFA9DIqJQEC6ppb12o9FVtHr3CWSPnoOCA677dyIiIqLgqZUaj88e6oLuOdUxesZavDh/E/sfDBK3HWET6eX1IW2x+/g5JMRajA6FiCiiKQWc92AEtUI3HWWHg/cW7wAA/Lj5kLGBEBERUTlV4qLx1rA8PDFrHcb/sBV7jp/HC7e0Rlw0nyf1xKQRGSYh1oLGGclGh0FEFPGOnLmAP326yu18a/cGrwPKL1fvgxgwAMGM32wdYJukHSMRERGVibFE4dkBrZCVnogX5xdg/8kiTL6rPdISY40OLWyxXRCRn1grkohC3eKtR01Xi+gPH/+GgoOnDSvfLH1fERERUXkiglHX5uDVwblYtesEBk1YjN0mGmAk3DBpROShWqnxAICmmSkOP3f/gMEnECIzMKL2CoUeHiVERETm1j+3Dt6/vyOOnLmIAW8uwurdJ4wOKSwxaUTkody6afhiVDf8sWcjo0MhIgooVx1JlpYqFJeUBjEac2AlUiIiIvPr1LAapo/oivgYC26fvATfrD9gdEhhh0kjIi/k1k2DJYq/PxNReHHVzHbIlKXIeWxe8IIxicOnLxgdAhEREXkgp2YVzBzZDU0ykvHgByvx7qLtRocUVpg0IgoQd30bsX8MIgoVF4tL8dy8jVi58ziWbT8GgEkUIiIiMq8ayXH4eHhn9GqWgae+3ICxX21AaSnrDQcCR08j8pOnySB2mE1kDkzgVrZ469Fy7//62WpsOnAak37cVjatw7hvgx0WERERkccSY6Mx8c72GPvVBkz9ZTv2Hj+P/w7ORXyMxejQQhprGhEREUW4DftPlXu/6YBxo5YZ5cDJIqNDICIiIj9ZogRP9WuBf97UHPM3HMCQKUtx9AxrS/uDSSMiP3lag4i1G4iIzKvzc98ZHQIREREFyP3dG2DCHe2wYd8pDJywGNsOnzE6pJDFpBFRgOiZFLq2SQ1UrxKrXwFERERERERhpHfLTHw8vDNOFxVj0ITFyN9xzOiQQhKTRkQh4J17OyL/8euMDoMoLLDSHxEREVFkaFcvHTNHdkVaYiyGvrUMc9bsNzqkkMOkEVGQ8EGViIiIiIgouOpXS8KMEV3Ruk4qRn30Kyb9uBWKoxR5jEkjIiIiIoAdZRIZSER6i0iBiBSKyGgHnz8qIhtEZI2IfCci9Y2Ik4hCU3pSLD54oBNubJ2J5+Ztwj9nrUNxSanRYYUEJo2IgqR+tSSjQyAiIhd2HTtndAhEEUlELADGA+gDoDmAISLSvMJsvwHIU0q1BvA5gBeCGyURhbr4GAteH9wWD17VEB8s3YXh76/E2QvFRodlekwaEQVJ1aRY7Hj+RqPDICK2FSUiMpuOAAqVUtuUUhcBfAKgv/0MSqkflFK2zO5SAFlBjpGIwkBUlGBM32YY+7uWWFhwCLdPXoJDp4qMDsvUmDQi8lNqQgwA4N5uDQyOhIiI/PHT5iNGh0AUqeoA2G33fo82zZn7AczTNSIiCmt3da6Pt4blYdvhsxjw5mJsPnja6JBMi0kjIj8lxFqw4/kb8dDVVxgdChER+eHQaf7SSGR2InIngDwALzr5fLiI5ItI/uHDh4MbHBGFlB5NM/Dp8C64WFKKQRMWY/FW/njkCJNGRERERABmr95ndAhEkWovgLp277O0aeWISC8AjwHop5Ry2HO9UmqyUipPKZVXo0YNXYIlovDRKisVM0d2Ra2UeAx7ezlm/LrH6JBMh0kjIiKKKMJOjciJ00XsDJPIICsANBKRBiISC2AwgNn2M4hIWwCTYE0YHTIgRiIKU1npifh8RFfk1a+KR6etxmvfbYFSyuiwTINJIyIiIiIiMoxSqhjAwwDmA9gIYJpSar2IPCMi/bTZXgRQBcBnIrJKRGY7WR0RkddSE2Lw3n0dMbBtHbyyYDP+/vkaXCopNTosU4g2OgAiIiIiIopsSqm5AOZWmPaE3eteQQ+KiCJKbHQUXr6tDbKqJuK177Zg/8kivHlnO6TExxgdmqFY04iIiIiIiIiIIp6I4NHrGuOFW1pj6bajuG3iEuw7cd7osAzlV9JIRF4UkU0iskZEZopImpP5dojIWq0qab4/ZRIRERERERER6eW2vLp4996O2Hv8PAa8uQjr9500OiTD+FvTaAGAlkqp1gA2AxjjYt5rlVK5Sqk8P8skIiLymbAfbCIiIiJyo3uj6vhsRBdEieC2iUuwsCAy++D3K2mklPpG67gOAJbCOjwmEREREREREVFIa1orBV+M6ob61ZJw/3v5+Hj5LqNDCrpA9ml0H4B5Tj5TAL4RkZUiMjyAZRIRERERERER6SIjJR7THuqC7jnVMWbGWrzw9SaUliqjwwoat6Onici3AGo5+OgxpdQsbZ7HABQD+NDJarorpfaKSE0AC0Rkk1LqJyflDQcwHADq1avnwSYQEREREREREemjSlw0pg7Lwz9nrcebC7diz/HzePHW1oiLthgdmu7cJo3cDW8pIvcAuAlAT6WUw3SbUmqv9u8hEZkJoCMAh0kjpdRkAJMBIC8vL3LSd0REFBTs0oiIiIiIvBVticKzA1qibtUEvPB1AQ6cKsLku9ojLTHW6NB05e/oab0B/B1AP6XUOSfzJIlIsu01gOsBrPOnXCIiIiIiIiKiYBIRjLwmB68OzsWqXScwcMJi7DrqMBUSNvzt0+gNAMmwNjlbJSITAUBEaovIXG2eDAC/iMhqAMsBzFFKfe1nuUREREREREREQdc/tw7ev78jjp65iIETFmHV7hNGh6Qbf0dPy1FK1VVK5Wp/D2nT9yml+mqvtyml2mh/LZRS4wIROBERERERERGRETo1rIbpI7oiIdaCwZOX4Jv1B4wOSReBHD2NiIjI9ETYqxERERER+S+nZhXMGNENTWql4MEPVuKdRduNDingmDQiIiIiIiIiIvJBjeQ4fPL7zujVLANPf7kBY7/agNLS8BnTi0kjIiIiIiIiIiIfJcRaMPHO9rinazam/rIdIz/8FUWXSowOKyCYNCIiIiIiIiIi8oMlSvBUvxb4503NMX/DAQyZshRHz1wwOiy/MWlEREQRxRLFPo2IiIiISB/3d2+ACXe0w4Z9pzBwwmJsO3zG6JD8wqQRERFFlGgmjYiIiIhIR71bZuLj4Z1xuqgYAycsRv6OY0aH5DMmjYiIKKKwphERERER6a1dvXTMHNkV6YmxGPrWMny1Zp/RIfmESSMiIoooTBoRERERUTDUr5aEGSO6onWdVDz80W+Y+ONWKBVaI6sxaURERBGFzdOIiIiIKFjSk2LxwQOdcGPrTDw/bxMe/2IdiktKjQ7LY0waERFRwIhIbxEpEJFCERnt4PM4EflU+3yZiGTbfTZGm14gIjfoFSNrGhERERFRMMXHWPD64LZ48OqG+HDZLgx/fyXOXig2OiyPRBsdAFGk+fLh7rhUGjqZZSJPiYgFwHgA1wHYA2CFiMxWSm2wm+1+AMeVUjkiMhjAvwHcLiLNAQwG0AJAbQDfikhjpVRJoOM8cf5SoFdJRKSLge3qGB0CEREFSFSUYEyfZqibnognZq3D7ZOX4O1hHVAzJd7o0Fxi0ogoyFplpRodApFeOgIoVEptAwAR+QRAfwD2SaP+AJ7SXn8O4A0REW36J0qpCwC2i0ihtr4lgQ7ydFFo/KpDREDt1Hh0bFAVpQqYvXof3ruvI4a9vVzXMscNaInHZq7TtQxXfvvndWg7dgEA4NkBrQyLg4iI9HFn5/qok5aAUR/9igFvLsboPk0RH2PxaV1NayWjbtXEAEdYHpNGRD5KS4zBiXOssUBkpw6A3Xbv9wDo5GwepVSxiJwEUE2bvrTCspV+YheR4QCGA0C9evV8CrJtvTSfliNzubJRdfy85Qju7ZaNdxbtcDjPp8M74/bJSx1+ZqSrG9fAL4VHkF0tEe/c0xFpSTH4efMR/PWz1chIicO+E0WY9XA39Hn153LLPXh1QyTGROOmNplITYjBlJ+3YdKP21yWZX+tmvZgFywsOIQhHeshOT4as1btw5Oz16NtvTS8MKg1fik8gqe/tOZ4W2el4qbWmQCA65rXwrUvLcTSMT1RvUospuXvwaYDp1BSqtCreQaOn72IFTuOo2mtZJw8fwnDumZj6JSlUAoY0qkenpq9HtWrxGLZ//UCAJSWKmw7chYFB07jhhYZeOuX7YiPjoLFEoV/fnE5WfOv37XEnZ3rAwAuFJfgri710SG7Kn5/ZQPUTkvAPV2zMWbGWsxatQ89m9VEr2YZSE2MwTNfbsD2I2dxZaPqiIuOwrcbD5WtUwRw1v+oJUqw+snrUSUuGnd0spa7bu9JNK2VDEuUYOfRc7jmpYUuv+/Hb2yG+7s3QKkCjpy5gMdmri1XvjPv3NsBnRpURWKs9dZ8x/M3ul2GiIhC17VNa2Lag11w77sr8IePf/N5PfbXSr2ImXvuzsvLU/n5+UaHQeRQ0aUSlCpVdoNHFEwislIplWd0HPZE5BYAvZVSD2jv7wLQSSn1sN0867R59mjvt8KaWHoKwFKl1Afa9KkA5imlPndWnj/XiNNFl1CqgNSEGK+XVUpBRFBaqhAVZf33xPlLOHuhGGcvFqN6lThUiYtGrCUKUVGCU0WXUFKikJ4UCwAoKVW4UFyCSyUKsZYoxEVHla0nyof+lkpKFSxRAqUUlAIulpQiLjoKF4pLYYkSREcJLhSXouhSCRJjoxFjEVgrdwEXi0txsaQUVeKice5iscPz2cXiUsRGR+HomQtIiLUgLtoCAXyK1ZVzF4sRF20p1+eUUgqlyrh+qEpLFRSAKEHZdwYAJ85dRHJ8jMu4bMcJkVHMeJ0INj5LEJHZnS66hJ1Hz/m8fGZqPKpVifNpWU+vE3zaJfKRr1UIicLYXgB17d5nadMczbNHRKIBpAI46uGyAZMc732yyMaWCLAlTaKiBFWTYlFVSwpVlFKhLEuUOEzO+JqEsSUuRAQiQHyU9dxkf46Kj7E4PGfFRkchNto6JoazBLjtc19vSDzlqHwRgcXAvIuzfZKW6Hhf22PCiIiIiNxJjo9Byzrm7r6Eo6cREVGgrADQSEQaiEgsrB1bz64wz2wAw7TXtwD4XlmrvM4GMFgbXa0BgEYA9O24hIiIiIiIXGJNIyIiCgitj6KHAcwHYAHwtlJqvYg8AyBfKTUbwFQA72sdXR+DNbEEbb5psHaaXQxglB4jpxERERERkeeYNCIiooBRSs0FMLfCtCfsXhcBuNXJsuMAjNM1QCIiIiIi8hibpxERERERERERUSVMGhERERERERERUSVMGhERERERERERUSVMGhERERERERERUSVMGhERERERERERUSVMGhERERERERERUSVMGhERERERERERUSWilDI6BqdE5DCAnT4sWh3AkQCHEyhmjc2scQHmjY1xec+ssZk1LsB5bPWVUjWCHYyZ+HGNAMy9z/XA7Q1fkbStALfXG7xO8DrhDW5v+IqkbQW4vd7w6Dph6qSRr0QkXymVZ3Qcjpg1NrPGBZg3NsblPbPGZta4AHPHFsoi7Xvl9oavSNpWgNtLwRNp3z23N3xF0rYC3F49sHkaERERERERERFVwqQRERERERERERFVEq5Jo8lGB+CCWWMza1yAeWNjXN4za2xmjQswd2yhLNK+V25v+IqkbQW4vRQ8kfbdc3vDVyRtK8DtDbiw7NOIiIiIiIiIiIj8E641jYiIiIiIiIiIyA9hlzQSkd4iUiAihSIyOgjlvS0ih0Rknd20qiKyQES2aP+ma9NFRF7TYlsjIu3slhmmzb9FRIYFIK66IvKDiGwQkfUi8kcTxRYvIstFZLUW29Pa9AYiskyL4VMRidWmx2nvC7XPs+3WNUabXiAiN/gbm7ZOi4j8JiJfmSyuHSKyVkRWiUi+Ns0M+zNNRD4XkU0islFEupgkribad2X7OyUifzJJbH/Wjv11IvKx9n/CFMdZJJAgXycCRYJwXheR9tp5plBbVoK/peWJjudksx0Lep9PzbR/9T4PGr1vRef7Q2f70lkZ5B2jjx9fSQReJ4TXiHDet7xOmOU6oZQKmz8AFgBbATQEEAtgNYDmOpd5FYB2ANbZTXsBwGjt9WgA/9Ze9wUwD4AA6AxgmTa9KoBt2r/p2ut0P+PKBNBOe50MYDOA5iaJTQBU0V7HAFimlTkNwGBt+kQAI7TXIwFM1F4PBvCp9rq5to/jADTQ9r0lAPv0UQAfAfhKe2+WuHYAqF5hmhn253sAHtBexwJIM0NcFWK0ADgAoL7RsQGoA2A7gAS74+sesxxn4f4HA64TAYxd9/M6gOXavKIt28cE263LOdmMxwJ0Pp+aZf/qfR40w76FzveHzvalszL459W+M/z48SP2iLtOgNeIsNy34HVCt/3prAyXsRp54OvwxXcBMN/u/RgAY4JQbnaFnV0AIFN7nQmgQHs9CcCQivMBGAJgkt30cvMFKMZZAK4zW2wAEgH8CqATgCMAoivuSwDzAXTRXkdr80nF/Ws/nx/xZAH4DkAPAF9p5Rgel7aeHaicNDJ0fwJIhfWELmaKy0Gc1wNYZIbYYL0I7ob15B6tHWc3mOU4C/c/GHSd0GlbAnpe1z7bZDe93HwGbaNu52SzHQt6n0/NtH/1Pg+aZd9Cp/tDV/vSWRn882q/meL4CdC2hPV1ArxGBOW8YtD28jphoutEuDVPsx1cNnu0acGWoZTar70+ACBDe+0sPl3j1qrntYW1Ro8pYtOqkq4CcAjAAlgzvSeUUsUOyimLQfv8JIBqOsX2XwB/B1Cqva9mkrgAQAH4RkRWishwbZrR+7MBgMMA3tGqBr8lIkkmiKuiwQA+1l4bGptSai+AlwDsArAf1uNmJcxznIW7sPjedDqv19FeV5xuJD3PyWY7FvQ+n5pm/wbhPGi2fWsTjH3prAzynFmPH69EyHWC14gw3be8TgAw0XUi3JJGpqOsKTxlVPkiUgXAdAB/Ukqdsv/MyNiUUiVKqVxYfyHoCKCpEXHYE5GbABxSSq00OhYnuiul2gHoA2CUiFxl/6FB+zMa1mqVE5RSbQGchbWao9FxldHaOvcD8FnFz4yITWs33B/Wi39tAEkAegczBgptZj2vB1oInJMDzfTn00DheTA4+zJcjhfyXiRcJ3iNCN9rBMDrBGCu60S4JY32Aqhr9z5LmxZsB0UkEwC0fw9p053Fp0vcIhID6wXjQ6XUDDPFZqOUOgHgB1irCKaJSLSDcspi0D5PBXBUh9i6AegnIjsAfAJrVddXTRAXgLKMO5RShwDMhDXZZvT+3ANgj1Jqmfb+c1gvaEbHZa8PgF+VUge190bH1gvAdqXUYaXUJQAzYD32THGcRYCQ/t50Pq/v1V5XnG4Uvc/JZjsW9D6fmmn/6n0eNNu+tQnGvnRWBnnOrMePRyLoOsFrRPheIwBeJ0x1nQi3pNEKAI20XtVjYW2SMtuAOGYDGKa9HgZre2Lb9Lu13s87AzipVQ2bD+B6EUnXsqrXa9N8pvWOPhXARqXUKyaLrYaIpGmvE2Bta70R1uTRLU5is8V8C4DvtazobACDxdpbfgMAjWDt8MsnSqkxSqkspVQ2rMfO90qpO4yOCwBEJElEkm2vYd0P62Dw/lRKHQCwW0SaaJN6AthgdFwVDMHlpmm2GIyMbReAziKSqP0/tX1nhh9nEcIs1wmv6X1e1z47JSKdtbLutltX0AXhnGyqY0Hv86nJ9q/e50FT7Vs7wdiXzsogz5n1+HErkq4TvEaE9TUC4HUCMNN1QhnUuZVef7D2LL4Z1j5yHgtCeR/D2s7yEqwZ4PthbT/5HYAtAL4FUFWbVwCM12JbCyDPbj33AWJUjSYAAAE9SURBVCjU/u4NQFzdYa1qtgbAKu2vr0liaw3gNy22dQCe0KY3hPU/cSGsTYnitOnx2vtC7fOGdut6TIu5AAHs4R/ANbg8CoPhcWkxrNb+1tuObZPsz1wA+dr+/ALWnvsNj0tbZxKsvzKk2k0zPDYATwPYpB3/78M6ooPhx1mk/CHI14kAxq37eR1AnnZcbgXwBip0uGngtl8DHc7JZjsW9D6fmmn/6n0eNHrfQuf7Q2f70lkZ/PN6/5nq3OBF3BF5nQCvEWG5b8HrhGmuE7YFiYiIiIiIiIiIyoRb8zQiIiIiIiIiIgoAJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKgSJo2IiIiIiIiIiKiS/wcwsj8BWGkrTgAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KsEk2mLaIU4S"
+ },
+ "source": [
+ "torch.save(agent.dqn.state_dict(),'dqn_base.pt')\n",
+ "torch.save(inv_agent.dqn.state_dict(),'dqn_inv.pt')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "gn22Irb-riUX",
+ "outputId": "fe8e46f5-59aa-4f59-a16e-df2d5e49de5d"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "display_pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol[i,j]=agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()\n",
+ " display_pol[i,j]=d[agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('Policy at each step')\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:11]=1\n",
+ "grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, display_pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "KULSMbvL4WDf",
+ "outputId": "5e7b71ef-b8df-452f-c048-7c42adc6e3c5"
+ },
+ "source": [
+ "pos=torch.tensor([2,3]).float().to(\"cuda\")\n",
+ "with torch.no_grad():\n",
+ " print(agent.dqn(pos))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([ 0.3187, 0.4321, -0.9380, 0.2571], device='cuda:0')\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "ajA_styEqTd5",
+ "outputId": "d09ccf18-9375-4920-c16a-76f42bb19576"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "inv_pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " inv_pol[i,j]=d[inv_agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('Inverted Policy at each step')\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[0,1:11]=1\n",
+ "grid[0,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, inv_pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "Zd1_rBG-W44s",
+ "outputId": "37b98392-23f1-48ad-957a-74c0a506ad1e"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol1.shape[0]):\n",
+ " for j in range(pol1.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol1[i,j]=arrow_list[agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Windy CW DQN Policy\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol1[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BhUvf59xosyo",
+ "outputId": "db1714be-8f32-4ee4-82ee-c2907fd82d44"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "reference=torch.ones([4,9],dtype=torch.int64)\n",
+ "reference[1:,0:6]=0\n",
+ "reference[:,6:]=2\n",
+ "onehot=torch.zeros([4,9,4])\n",
+ "onehot=onehot.scatter_(-1,reference.unsqueeze(-1),1)\n",
+ "display_reference=reference.numpy().astype(object)\n",
+ "display_reference[reference==0]='U'\n",
+ "display_reference[reference==1]='R'\n",
+ "display_reference[reference==2]='D'\n",
+ "display_reference"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['R', 'R', 'R', 'R', 'R', 'R', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 47
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ymt3cV392KHG",
+ "outputId": "7b5a2602-783f-478e-f927-62a771b415ca"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "inv_reference=torch.ones([4,9],dtype=torch.int64)\n",
+ "inv_reference[:,6:]=0\n",
+ "inv_reference[:3,:6]=2\n",
+ "inv_display_reference=inv_reference.numpy().astype(object)\n",
+ "inv_display_reference[inv_reference==0]='U'\n",
+ "inv_display_reference[inv_reference==1]='R'\n",
+ "inv_display_reference[inv_reference==2]='D'\n",
+ "inv_display_reference"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'R', 'R', 'U', 'U', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 91
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0iqkzdtH6tlY"
+ },
+ "source": [
+ "class QSC(nn.Module):\n",
+ " def __init__(\n",
+ " self, action_dim = 4, t = 0.2, alpha = 2):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(QSC, self).__init__()\n",
+ " self.action_dim=action_dim\n",
+ " \n",
+ " self.fwd=nn.Sequential(\n",
+ " nn.Linear(self.action_dim,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,1),\n",
+ " nn.Sigmoid()\n",
+ " )\n",
+ " self.ce_loss=nn.CrossEntropyLoss(reduction='none')\n",
+ " self.t=t\n",
+ " self.alpha=alpha\n",
+ " def cat_CE_loss(self,pred,true):\n",
+ " return self.ce_loss(pred,true)\n",
+ " #return (-torch.log(prob) * true).sum(dim=-1)\n",
+ " def normalise(self,x):\n",
+ " #expect x of N by 4\n",
+ " a=(x-x.min(dim=-1)[0].reshape(-1,1))\n",
+ " a=a/torch.std(x,dim=-1).reshape(-1,1)\n",
+ " return (a**self.alpha) \n",
+ "\n",
+ " def get_error(self,q,labels):\n",
+ "\n",
+ " \n",
+ " normalised=self.normalise(q)\n",
+ " \n",
+ " loss=self.cat_CE_loss(normalised,labels)\n",
+ " \n",
+ " return loss#(loss>self.t).float()\n",
+ " def compute_target(self,dist,labels):\n",
+ " error=self.get_error(dist,labels)\n",
+ " return (error>self.t).float()\n",
+ " def forward(self,x):\n",
+ "\n",
+ " out=self.fwd(x)\n",
+ "\n",
+ " return out\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "KcGoPXayYaRZ",
+ "outputId": "ccf463d3-f611-4174-befb-2a8ff99e34ab"
+ },
+ "source": [
+ "dev=(torch.tensor(pol.astype(int))!=reference).float()\n",
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC DQN Labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(dev[i,j].item()),\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gsypifr7A-Ni"
+ },
+ "source": [
+ "classifier=QSC().to('cuda')\n",
+ "x1=torch.arange(0,4).reshape(1,4).expand(9,4).T.flatten().unsqueeze(1)\n",
+ "x2=torch.arange(0,9).repeat(4).unsqueeze(1)\n",
+ "input=torch.cat([x1,x2],dim=1).to('cuda').float()\n",
+ "q=agent.dqn(input)\n",
+ "labels=reference[x1,x2].squeeze().to('cuda')\n",
+ "target=classifier.get_error(q,labels).reshape(4,9)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nJFz_TqdBVx5",
+ "outputId": "70d1e723-276d-4652-ebfe-dd9d4ab4c3ca"
+ },
+ "source": [
+ "binary=(target>0.2).float()\n",
+ "binary[-1,:]=0\n",
+ "binary"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "tensor([[0., 0., 0., 0., 0., 1., 1., 0., 0.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 0., 0.],\n",
+ " [1., 1., 1., 1., 1., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0.]], device='cuda:0')"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 60
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-LzDAtVrBaVS",
+ "outputId": "1a0f0586-c4ed-4550-b727-2bf4823eab9d"
+ },
+ "source": [
+ "np.round(target.cpu().detach().numpy(),1)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0.1, 0. , 0. , 0. , 0. , 0.4, 0.4, 0.2, 0.1],\n",
+ " [2. , 1. , 1.1, 1.6, 4.9, 5. , 0.2, 0.1, 0.1],\n",
+ " [0.6, 0.2, 0.3, 0.3, 0.6, 1.8, 0.1, 0.1, 0.1],\n",
+ " [1. , 0.1, 0.1, 0.1, 0.2, 0.3, 5.3, 1.1, 0.1]], dtype=float32)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 61
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EzNuqxQy5Pz7",
+ "outputId": "a2b6ec11-9943-4a6b-f3a6-960525ee1d8e"
+ },
+ "source": [
+ "reference.float"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "tensor([[1, 1, 1, 1, 1, 1, 2, 2, 2],\n",
+ " [0, 0, 0, 0, 0, 0, 2, 2, 2],\n",
+ " [0, 0, 0, 0, 0, 0, 2, 2, 2],\n",
+ " [0, 0, 0, 0, 0, 0, 2, 2, 2]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 59
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "FY7B8V7n9TeN",
+ "outputId": "fd730355-971a-4d7b-84ad-f5d165a801c6"
+ },
+ "source": [
+ "pol"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1, 1, 1, 1, 1, 1, 2, 2, 2],\n",
+ " [1, 1, 1, 1, 1, 2, 2, 2, 2],\n",
+ " [0, 0, 0, 0, 0, 1, 2, 2, 2],\n",
+ " [0, 0, 0, 0, 0, 0, 1, 1, 2]], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 92
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SyL2tQJb9kqL"
+ },
+ "source": [
+ "labels=(torch.tensor(pol.astype(float))!=reference).float()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KrSVCBsErWej",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 282
+ },
+ "outputId": "0321fde7-f698-4082-dd9d-172881a3c17e"
+ },
+ "source": [
+ "losses=[]\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=QSC().to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters())\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " label=labels[x,y].squeeze().to('cuda')\n",
+ " q=agent.dqn(pos)\n",
+ " pred=classifier(q)\n",
+ " loss=bce(pred.squeeze(),label)\n",
+ " loss.backward()\n",
+ " optimiser.step()\n",
+ " losses.append(loss.item())\n",
+ "plt.plot(losses)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7fe1b4072e90>]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 65
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7anYVNP0ZmEM",
+ "outputId": "42f322ad-3c9a-4523-a29f-9cd3f9838cee"
+ },
+ "source": [
+ "bce=nn.BCELoss()\n",
+ "classifier=QSC().to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=torch.tensor(dev)[x,y].squeeze().to('cuda').float()\n",
+ " q=agent.dqn(pos)\n",
+ " pred=classifier(q)\n",
+ " loss=bce(pred.squeeze(),labels)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:12: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
+ " if sys.path[0] == '':\n"
+ ],
+ "name": "stderr"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wdANYmXb_Er-",
+ "outputId": "13eb59cd-47db-4c8f-eba2-022d7dd65603"
+ },
+ "source": [
+ "d"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "{0: 'Up', 1: 'Right', 2: 'Down', 3: 'Left'}"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 115
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2oTeC4R0uNy7"
+ },
+ "source": [
+ "classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=agent.dqn(torch.tensor([i,j]).to('cuda').float())\n",
+ " classified[i,j]=np.round(classifier(dist).cpu().detach().numpy(),3)\n",
+ "classified[3,1:]=0"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 721
+ },
+ "id": "XnNxkL6E7XNa",
+ "outputId": "5ab711a0-da5b-41ea-d295-aa2e370ec71b"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "plt.figure(figsize=[6,3],dpi=300)\n",
+ "plt.imshow(classified,cmap='gray')\n",
+ "ax=plt.gca()\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f= 'normal'\n",
+ " if i==3 and j==0:\n",
+ " text='S' \n",
+ " f= 'bold' \n",
+ " elif i==3 and j>0:\n",
+ " text='T'\n",
+ " f= 'bold' \n",
+ " else:\n",
+ " text=int(dev[i,j].item())#+'/'+str(display_reference[i,j])\n",
+ " if classified[i,j]>0.5:\n",
+ " color='black'\n",
+ " else:\n",
+ " color='w'\n",
+ " text = plt.text(j, i, text,\n",
+ " ha=\"center\", va=\"center\", color=color,fontweight=f)\n",
+ "plt.title('SC DQN trained on Windy CW')\n",
+ "plt.colorbar()\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N4tuDVa005VP"
+ },
+ "source": [
+ "inv_classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=inv_agent.dqn(torch.tensor([i,j]).to('cuda').float())\n",
+ " inv_classified[i,j]=np.round(classifier(dist).cpu().detach().numpy(),3)\n",
+ "inv_classified[0]=0 "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 721
+ },
+ "id": "RyYEdKnf0_VM",
+ "outputId": "849883de-f715-41cb-ce5e-ab995efeed16"
+ },
+ "source": [
+ "inv_dev=torch.tensor(inv_pol!=inv_display_reference).float()\n",
+ "plt.figure(figsize=[6,3],dpi=300)\n",
+ "plt.imshow(inv_classified,cmap='gray')\n",
+ "ax=plt.gca()\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f= 'normal'\n",
+ " if i==0 and j==0:\n",
+ " text='S'\n",
+ " f='bold'\n",
+ " elif i==0 and j>0:\n",
+ " text='T'\n",
+ " f='bold'\n",
+ " else:\n",
+ " text=str(inv_dev[i,j].int().item())\n",
+ " if inv_classified[i,j]>0.5:\n",
+ " color='black'\n",
+ " else:\n",
+ " color='w'\n",
+ " text = plt.text(j, i, text,\n",
+ " ha=\"center\", va=\"center\", color=color,fontweight=f)\n",
+ "plt.title('SC DQN test: Inv Windy CW')\n",
+ "plt.colorbar()\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "nnJDmxjzoVsI",
+ "outputId": "85ed4643-fcc4-45f7-f113-b926e05eca70"
+ },
+ "source": [
+ "inv_display_reference"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'R', 'R', 'U', 'U', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 96
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Qvri9oZ0ooQD",
+ "outputId": "6fb75a01-dfe3-43ea-aafe-76f33e5e6740"
+ },
+ "source": [
+ "inv_pol"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['L', 'L', 'D', 'U', 'U', 'U', 'U', 'U', 'U'],\n",
+ " ['D', 'R', 'R', 'R', 'R', 'R', 'U', 'U', 'U'],\n",
+ " ['D', 'R', 'R', 'R', 'R', 'R', 'U', 'U', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'R', 'R', 'R', 'R', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 102
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "uZL_GVlhqEq-"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/IQN.ipynb b/code/IQN.ipynb
new file mode 100644
index 0000000..aa57992
--- /dev/null
+++ b/code/IQN.ipynb
@@ -0,0 +1,862 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "IQN monotone.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SrcKRF9wcaA1"
+ },
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import collections\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J8IGieyXw7kq"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " \n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a] \n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "9EXpvrmedsuq"
+ },
+ "source": [
+ "class IQNetwork(nn.Module):\n",
+ " def __init__(self,input_dim,embed_dim=64,action_space=4,n=8,device=None,positive_weights=False,penalty=False):\n",
+ " super(IQNetwork, self).__init__()\n",
+ " self.d=400\n",
+ " self.positive=positive_weights\n",
+ " self.penalty=penalty\n",
+ " self.psi=nn.Sequential(\n",
+ " nn.Linear(input_dim,128),\n",
+ " nn.ReLU(True),\n",
+ " nn.Linear(128,self.d),\n",
+ " nn.ReLU(True),\n",
+ " )\n",
+ " self.input_dim=input_dim\n",
+ " self.n=n\n",
+ " self.embed_dim=embed_dim\n",
+ "\n",
+ " \n",
+ " if positive_weights:\n",
+ " print('Positive weights selected')\n",
+ " self.d_linear= nn.Sequential(\n",
+ " nn.Linear(self.d,300),\n",
+ " nn.Tanh(),\n",
+ " nn.Linear(300,action_space)\n",
+ " )\n",
+ " self.phi_linear=nn.Sequential(\n",
+ " nn.Linear(1,300),\n",
+ " nn.Tanh(),\n",
+ " nn.Linear(300,self.d),\n",
+ " nn.Tanh(), \n",
+ " ) \n",
+ " #self.weight_init() \n",
+ " self.clamp_weights\n",
+ " else:\n",
+ " self.d_linear= nn.Sequential(\n",
+ " nn.Linear(self.d,128),\n",
+ " nn.ReLU(True),\n",
+ " nn.Linear(128,action_space)\n",
+ " )\n",
+ " self.phi_linear=nn.Sequential(\n",
+ " nn.Linear(embed_dim,self.d),\n",
+ " nn.ReLU(True)\n",
+ " ) \n",
+ " self.device=device\n",
+ " def clamp_weights(self):\n",
+ " self.clamp_layer(self.phi_linear)\n",
+ " self.clamp_layer(self.d_linear)\n",
+ " \n",
+ " def clamp_layer(self,s):\n",
+ " for i in range(len(s)):\n",
+ " if hasattr(s[i],'weight'): \n",
+ " with torch.no_grad():\n",
+ " #tau_grad=(torch.where(tau_grad>0,torch.zeros(tau_grad.shape,device=self.device),tau_grad)**2).mean()\n",
+ " s[i].weight.data=torch.where(s[i].weight.data<-0.01,s[i].weight.data*0.001,s[i].weight.data)\n",
+ " #s[i].weight.data=torch.clamp(s[i].weight.data,min=0) #set to positive\n",
+ "\n",
+ " def phi(self,batch_size,t=None):\n",
+ "\n",
+ " #default sampling\n",
+ " if t is None:\n",
+ " if self.penalty:\n",
+ " tau=torch.rand([1,self.n],device=self.device,requires_grad=True).reshape(1,self.n,1) #1,8,1\n",
+ " else:\n",
+ " tau=torch.rand([1,self.n],device=self.device,requires_grad=False).reshape(1,self.n,1) #1,8,1\n",
+ " size=self.n\n",
+ " else:\n",
+ " #given a specific tau\n",
+ " tau=t\n",
+ " size=tau.shape[1]\n",
+ "\n",
+ " if self.positive:\n",
+ " q=self.phi_linear(tau) #1,n,d\n",
+ " q=q.expand(batch_size,size,self.d) #batch_size,n,d\n",
+ " else:\n",
+ " q=torch.cos(3.14159*tau*(torch.arange(1,self.embed_dim+1,device=self.device).reshape(1,1,-1))) #1,n,embed_dim\n",
+ " q=self.phi_linear(q).expand(batch_size,size,self.d) #batch_size,n,d\n",
+ " return tau,q\n",
+ "\n",
+ " def forward(self,x,mean=True,t=None):\n",
+ " out=self.psi(x.reshape(-1,self.input_dim)).unsqueeze(1) #1,1,d\n",
+ " tau,q=self.phi(x.shape[0],t=t) # q: batch_size,n,d\n",
+ " out=out*q ##batch_size,n,d\n",
+ " out=self.d_linear(out) #batch_size,n,action_space\n",
+ " if mean:\n",
+ " return out.mean(1),tau #batch,action_space\n",
+ " else: \n",
+ " return out,tau #batch, n, action_space\n",
+ "\n",
+ "class IQNAgent():\n",
+ " def __init__(self,env,epsilon_decay,input_dim,embed_dim=64,action_space=4,penalty=0,positive_weights=False,epsilon=1):\n",
+ " self.n=8\n",
+ " self.env=env\n",
+ " self.positive=positive_weights\n",
+ " self.epsilon=epsilon\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " self.penalty=penalty \n",
+ " self.net=IQNetwork(input_dim,embed_dim,action_space,self.n,device=self.device,penalty=(penalty>0),positive_weights=positive_weights).to(self.device)\n",
+ " self.target=IQNetwork(input_dim,embed_dim,action_space,self.n,device=self.device,penalty=(penalty>0),positive_weights=positive_weights).to(self.device)\n",
+ " self.update_target() \n",
+ " self.replay_buffer=collections.deque(maxlen=5000)\n",
+ " self.state=self.env.reset()\n",
+ " self.batch_size=32\n",
+ " self.max_epsilon=1\n",
+ " self.min_epsilon=0.1\n",
+ " self.epsilon_decay=epsilon_decay\n",
+ " self.target_update=200\n",
+ " self.optimizer=torch.optim.Adagrad(self.net.parameters(),lr=0.001)\n",
+ " self.gamma=0.9\n",
+ "\n",
+ "\n",
+ " def update_target(self):\n",
+ " self.target.load_state_dict(self.net.state_dict()) \n",
+ "\n",
+ " def step(self,action):\n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ " transition = (self.state, action, reward, next_state, done)\n",
+ " self.replay_buffer.append(np.array(transition, dtype=object))\n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def select_action(self, state):\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " action = self.env.action_space.sample()\n",
+ " else:\n",
+ " action = self.greedy_action(state)\n",
+ " return action\n",
+ "\n",
+ " def greedy_action(self,state):\n",
+ " with torch.no_grad():\n",
+ " action=self.net(torch.tensor(state).float().to(self.device))[0].argmax()\n",
+ " return action.cpu().numpy()\n",
+ "\n",
+ " def huber(self,x):\n",
+ " return torch.where(x.abs() < 1, 0.5 * x.pow(2), (x.abs() - 0.5))\n",
+ " def quantile_loss(self,y,tau):\n",
+ " return y* (tau-(y<0).float())\n",
+ "\n",
+ " def compute_loss(self,batch):\n",
+ " state, action, reward, next_state, done = [torch.tensor(np.vstack(batch[:,i])).squeeze() for i in range(5)]\n",
+ " state=state.to(self.device).float()\n",
+ " action=action.to(self.device)\n",
+ " reward=reward.to(self.device).float()\n",
+ " next_state=next_state.to(self.device).float()\n",
+ " done=done.to(self.device)\n",
+ " q1,tau1=self.net(state,mean=False) #batch x n x a\n",
+ "\n",
+ " q1 = torch.gather(q1, dim=2, index=action.view(-1,1,1).expand(-1,self.n,1)).squeeze() #batch x n\n",
+ " q2,tau2=self.target(next_state,mean=False) #batch x n' x a\n",
+ " next_action = torch.argmax(q2.mean(1), axis=1) \n",
+ " q2 = torch.gather(q2, dim=-1, index=next_action.view(-1,1,1).expand(-1,self.n,1)).squeeze() #batch x n'\n",
+ " done=(1-done.float()).view(-1,1).expand(-1,self.n)\n",
+ " target=(reward.unsqueeze(1)+done*self.gamma*q2).unsqueeze(2)\n",
+ " deltas=target-q1.unsqueeze(1) #batch x n x n'\n",
+ " huber=self.huber(deltas) #batch x n x n'\n",
+ " loss=huber*(tau1-(deltas<0).float()).abs()\n",
+ " loss=loss.sum()/self.n\n",
+ " if self.penalty>0:\n",
+ " tau_grad=torch.autograd.grad(q1.sum(),tau1,create_graph=True,retain_graph=True)[0]\n",
+ " tau_grad=(torch.where(tau_grad>0,torch.zeros(tau_grad.shape,device=self.device),tau_grad)**2).mean()\n",
+ " loss=loss+self.penalty*tau_grad \n",
+ " return loss\n",
+ "\n",
+ " def sample_batch(self):\n",
+ " indices=np.random.choice(len(self.replay_buffer),self.batch_size,replace=False)\n",
+ " train_batch = np.vstack([self.replay_buffer[i] for i in indices])\n",
+ " return train_batch\n",
+ "\n",
+ " def update_model(self) :\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " self.optimizer.zero_grad()\n",
+ " batch = self.sample_batch()\n",
+ " loss = self.compute_loss(batch)\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ " if self.positive:\n",
+ " self.net.clamp_weights()\n",
+ " return loss.item()\n",
+ "\n",
+ " def train(self,steps,update_interval=200):\n",
+ " \n",
+ " self.state=self.env.reset()\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = [] \n",
+ " score=0\n",
+ " update_cnt=0\n",
+ " for i in range(steps):\n",
+ " action=self.select_action(self.state)\n",
+ " next_state,reward,done=self.step(action)\n",
+ " self.state=next_state\n",
+ " score+=reward\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0 \n",
+ " if len(self.replay_buffer)>self.batch_size:\n",
+ " self.net.train()\n",
+ " loss=self.update_model()\n",
+ " losses.append(loss)\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon) \n",
+ " update_cnt+=1\n",
+ " if update_cnt%self.target_update==0:\n",
+ " self.update_target()\n",
+ " if i % update_interval == 0:\n",
+ " self.plot(i, scores, losses, epsilons) \n",
+ " self.net.eval() \n",
+ " def plot(\n",
+ " self, \n",
+ " frame_idx, \n",
+ " scores, \n",
+ " losses, \n",
+ " epsilons,\n",
+ " ):\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ig08Vf5Gu5pk"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "yt5d8YmgxH9r",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "3a144e17-6eb2-4067-cad3-465643f665ff"
+ },
+ "source": [
+ "env=cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "# parameters\n",
+ "num_frames = 80000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 75000\n",
+ "\n",
+ "# train\n",
+ "agent = IQNAgent(env,epsilon_decay,2,penalty=0,positive_weights=True)\n",
+ "#agent.net.load_state_dict(torch.load('iqn.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Positive weights selected\n",
+ "Positive weights selected\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "YqYtkwZ-jr93"
+ },
+ "source": [
+ "torch.save(agent.net.state_dict(),'iqn.pt')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "JlfaZ0Jbj37y",
+ "outputId": "d8420ec6-9e7b-4f0c-b642-a2eab58aed7d"
+ },
+ "source": [
+ "agent.net.load_state_dict(torch.load('iqn.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 12
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "VqXkv3kfxhN9",
+ "outputId": "2f67038e-ef57-419f-ba53-b7cbd961bdbe"
+ },
+ "source": [
+ "agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "d8EZy4fBthRS",
+ "outputId": "0206966c-3e74-47a3-bf5e-0cde16d8eb74"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol[i,j]=d[agent.net(torch.tensor([i,j]).float().to(\"cuda\"))[0].argmax().cpu().item()]\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('Policy at each step')\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:11]=1\n",
+ "grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 363
+ },
+ "id": "ZN_Q20pxjlLV",
+ "outputId": "951d12e0-6969-414a-d838-d38dbbf88eaf"
+ },
+ "source": [
+ "device=torch.device('cuda')\n",
+ "bins=101\n",
+ "tau=torch.linspace(0,1,bins,device=device,requires_grad=True).reshape(1,bins,1)\n",
+ "out=agent.net(torch.tensor([1,3]).reshape(1,1,2).float().to(device),mean=False,t=tau)\n",
+ "atoms=out[0].detach().cpu().numpy().squeeze()\n",
+ "mean=atoms.mean(0)\n",
+ "var=(atoms**2).mean(0)-mean**2\n",
+ "plt.figure(figsize=[17,4],dpi=300)\n",
+ "#plt.suptitle('Quantile function' )\n",
+ "for i in range(4):\n",
+ " plt.subplot(1,4,i+1)\n",
+ " title=str(d[i])+' - Mean: '+str(np.round(mean[i],3))\n",
+ " plt.title(title)\n",
+ " plt.plot(tau.detach().cpu().numpy().squeeze(),atoms[:,i])\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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U5TOfIKm/pGskLXPOPVDeiAMIxcyV23znm2Ukq3t2ZshpAAAAAAAAGo6C4lJd9eyCwEaYfdo21g0/PCjkVAAAAAAA1J612/L15PTVvrWs1CT97NjuIScCald6SqIenTBUOdnBTUaembVGd733VYipAKBheGrGagW8fa+Jh3UJNQsAAKg71uTl64+vLQ6sX3JUjoZ0YVNMAAAA1L54af/lfYo536reYnpvBWtWm3NurKRREVOXmFlBrK9TSRM949fNLC+KdX4laXD554WSfhrFa18TkiRdKukw59yJZrYxlos751pJyq7iaTmxzID4M2ul//9CQ7s0V0KC860BAAAAAACg+m5/e4m+2rzHt5aRkqh7zh6otOTEkFMBAAAAAFB7/vrOEhWVlvnWLj06Ry0yU0NOBNS+Zo1S9PgFwzT6gWnatKvQ95i73/9K7Zqm6SdDO/nWAQBVU1Bcqn/NWuNby8lupMO7tww5EQAAqAtKy0xXP7dAe4tKfesHtW2sq47rGXIqAAAAwF+8NluIpoHBvgrWrBbnXAtJ90RMPWZmH8TyGlXIkiXpDM90bhTr9JB0U8TUn81sSfTJKrRA0uuSpklaLGmrpCJJzSX1lXSipIslNYk4p6+k15xzR5mZt6FGdVwu6cYYroc6rrCkVPPX7PCtDetKt0QAAAAAAICaMnXZVj06dWVg/Y+nHaKc7Jj31gUAAAAAIG4tWr9TryzY4Ftr3ThVFxzWNeREQPzo0CxDj18wTGMenK7dBSW+x/xmyiK1apymY3q1CjkdANQ/r366Qdvzi31rE0Z2kXNsZAUAAL7vnx8t15zV231rKYkJuusnA5SSlBByKgAAAMBfvPxkmuYZF0WxhrdVdXqUWYLcJembv75slnRNjNevijGSMiLGmyS9VZUF3P53Nx/Rt6/TYkm3xSTd982TNNjMBprZ783sLTNbbWZ7zazYzDaZ2Xtmdq2krpJe9pw/WNIfaygbIEn6bN1OFZb47whBswUAAAAAAICasTO/WNc8/2lg/ZS+bTV6UPsQEwEAAAAAUPv+9k7wXilXH99T6SmJIaYB4k/vNo318PghgQ/mlJaZrpg8T5+t2xlyMgCoX8xMuVNX+dYyU5N0xqAO4QYCAAB1wuINu3Tnu0sD69ed1Eu92mSFmAgAAAA4sHhptlDgGadEsUZqBWtGzTl3sqRzI6auMrNtsVo/ChM948lm5t+mO9ilko4s/9wk/dTMomlyUSEzW2hm8yp57HZJoyW95Cld7pzjrmrUmJkr/f+XbpSSqD5tG4ecBgAAAAAAoGH4v1cXaeNO/7dyW2Wl6uZRh7ArFgAAAACgQfls3U59sGSLb61n60yN5qFGQJJ0aLcW+sdZAxT01lF+UanOz52ttdvyww0GAPXInNXbtXjjLt/amYM7KDM1KeREAAAg3hWXluma5z9Vcan51g/t1lwXHNY15FQAAADAgcVLs4U9nnFaFGukV7BmVJxzWZIejJh628yejsXaUebpJulwz3RuFdfoIOkvEVMPmdnUakaLGTMrk3SRpB0R02mSxsTwMvdLOqSKH6fF8PqIM7MCmi0M7tJcSYnx8q0SAAAAAACg/nj10w16ZcGGwPodY/qrWaNo+vICAAAAAFB33fOfrwJr15/Um3sYgAgnHdJWN/3o4MD61j2FmvDYLO3Ir5E9iACg3sudtiqwNn5E5/CCAACAOuOhj1YENmvKSk3SX8f0V0ICGy4AAAAgvsRLS1FvY4QM55wzM/9WZv4aVbBmtG6T1Kn883xJl8Vo3WhNkBT5m8U8M/usims8KCmr/PONkq6PRbBYMrPtzrlHJV0dMX2CpLtitP5mSZurcg476NVfJaVlmrt6u29teNfmIacBAAAAAACo/77eWaDfTQl+W3P8iM46qmd2iIkAAAAAAKh9X2zcpXcWb/Kt9e/YVMf2bhVyIiD+TRjZRRt27NM/P1rhW1+xZa8uenyOnrpouNKSE0NOBwB119c7C/T2oq99a0f3yla37MyQEwEAgHi3bPNu3f1ecBPJm358sDo0ywgxEQAAAFA58dLqfKukyMYKyZKq+tfB9p5xlR6k9+Oc66rvNle40cxWVXfdaLn9T/uP90znVnGNoyWdEjH1czPbWb1kNeZ9z7hXraRAvffFxt3aU1jiWxtGswUAAAAAAICYKiszXfvCp9pV4P9+TLeWjXTDyQeFnAoAAAAAgNp373+WBdZ+fmx3NgoBAlx/Um/9qH+7wPqc1dt19XMLVFZWlb2fAKBhmzxztUoDvm9OGNkl3DAAACDulZaZrnthoYpKy3zrx/dprTMGeR/7AgAAAOJDXDRbMLN9ktZ4pjtVcRnv8V9Gn+h/mkiK/CvlHc45q+hD0o2edSZ4jtkRZZ6jJHWJGBdJerqKazT1jJ+v5Nc0wXPejZ5jFlQxR2Ws9YzZyg41YubKPN/5lKQE9evQJOQ0AAAAAAAA9dsT01fp46+2+tYSE5zu/MkApaewyyAAAAAAoGFZtnm33ly00bd2cLvGOrZ3VfetARqOhASnv47pp+EH2FTlzc++1i1vfhFiKgCouwpLSvXMLO9t3ft1bdlIR/Xgdl4AAPBdj09bpXlr/B+VapyWpFtGHUITSQAAAMStuGi2UM7bHKFPFc/3bnUWi2YL8WaiZ/yamfk/JV4/FHvGybWSAvXerJXbfOcHdmyq1CRu7AcAAAAAAIiVZZt369a3gt+6/fmxPdS/o7dfLAAAAAAA9d+9/1km8988Wj87tjsPJAAVSE1K1EPnDVGPVpmBx0z6ZKUmfbIyxFQAUDe9sXCjtu4p8q2dd2hnJSTwcwkAAPjWmrx83fHvJYH1353aR60ap4WYCAAAAKiaeGq2sMAzHlnZE51zbSV1iZgqlrQ4BpnihnMuU9KZnuncWogSpjae8ZZaSYF6razMNHuVf7OFA3W7BwAAAAAAQNWUlJbpqmc/VWFJmW+9f8emuuKYnJBTAQAAAABQ+1Zu3atXP93gW+vVOksn9PHeQgPAT5OMZOVeMEytslIDj7n5jcV6e9HXIaYCgLrFzJQ7bZVvLSMlUWcO6RBuIAAAENfMTDdMWah9xaW+9SN6tNSYwfz8AAAAgPiWVNsBIrwu6fqI8XHOOWcW1LP9O07wjD8wsz0xyLRM0vFRnDde0nkR43ck3RExLo5izdGSGkWMv5b0dhTrTFV0X9O1+u7r/KSkJyLGu6NYsyKHe8Zra+AaaOCWbdmj7fn+/0sO69oi5DQAAAAAaptzLkfSMEkdJKVI2i7pS0nTzKygNrPVV865VElDJfWW1EJSova/z7Ba0mIzW1aL8QDE0D8/WqHP1u/0raUnJ+rOsf2VlBhP/YEBAAAAAAjH/R8sU1nAHVJXHNud3aOBKmjfNF2PnT9UYx+crr1F33/Yx0z65bPz9a8mIzSgY9NaSAgA8W3Wym1auM7/vfzRgzqocVpyyIkAAEA8e3b2Wk1dludby0hJ1J9P7yvneF8DAAAA8S2emi1Mk7RVUsvycTdJR0v6oBLnXugZvxKLQOUNG96r6nnOOW+TgI1mVuV1PCZ6xpPNrKSqi5jZFkX3NZ3rmVoRg6/pQNdLkjTBM/1+TV0PDdfMldt855MSnAZ15g+qAAAAQEPhnBsl6feSBgUcssc5lyvpD2a2tYazfCjpqGoscb6Z5VZwjYmSHqvGNby6mtmqqpzgnOun/c0dz5CUcYDjtkp6V9I9Zja9OiEB1J6vNu3W3e99FVj/7SkHqVt2ZoiJAAAAAACID2u35WvK/PW+tW7ZjXRK37YhJwLqvoPbNdED5w7WBbmzVeLTyaSguEwXPT5bUy4/TB2bB749DQAN0sMfrwisTRjZOcQkAAAg3n29s0C3vPFFYP36k3rzOxcAAADqhLjZJszMyiTleqZvdBW0MHPO/UDSERFTuyU9F9t0tcs511nff8gitxaihOnXkrp75mLSRAOINCug2cIh7ZsoIyWe+tEAAAAAqAnOuVTn3FOSpii40YIkZUq6UtJi59yRoYSrW4oqe2D5a36npPmSztUBGi2UaylpnKTToo8HoDaVlJbpmhcWqqi0zLd+dK9snTO8U8ipAAAAAACIDw/8d7nvw+CSdOUx3ZWYwO6PQDSO7Jmt20b3C6xv3VOkiY/N0s784hBTAUB8W75lj977YrNv7aie2ereKivkRAAAIF6ZmX738mfaXei/h+zQLs103qE0agIAAEDdEDfNFsr9RdKeiPFRkq4POtg5117SI57puyvaYdI5Z56Po6MNHJIJkiL/cjrXzBbVVpiqcM5d5pw7tornXC3pj57pV81sbuySAft/wZ+1Ms+3Nrxr85DTAAAAAAibcy5B0rOSzvGUSiWtlLRA0k5PLVvSW865ETWfsM6Yb2YbKnOgc66xpHcl/VLff18qT9LnkmZJWiKpMJYhAdSeSZ+s1Kdrd/jWstKSdNsZ/VRBz10AAAAAAOqlDTv26fk5a31rnVtk6Mf924WcCKhfzhzcQb/4QY/A+vIte3XJU3NUVOLfJBQAGppHPl4ZWPvpkd1CTAIAAOLdq59uCGzSlJKUoNtG91MCDSQBAABQR8TVtu1mttU592dJf46YvtU510nSzd/cuF/+MMSPJd0tKXLLsw2S/hZW3jC4/XcZj/dM59ZClGgNl3S/c+5TSc9JelvS52b2nQcmnHMZkn4g6RpJ3h1C8yRdHUJWNDBrtuVr0y7/Z3eG0WwBAAAAaAiulXSaZ+5BSX/yvAdxmqS79O17EBmSnnPOHWJm3mYMNeH4Kh7/eSWO+XcU60r7GyS8JKlRxFxuZU50ziVLmiLpiIjpfEl3SnrGzD73HJ8k6WBJJ0k6O4qsAOLA8i179Ld3lwbWf39qH7VpkhZiIgAAAAAA4sc//7tcxaXmW7v86BwlJcbbPjpA3fPL43po7bZ8vTR/vW99xopt+vWLC/W3sf1pCAqgQdu6p1AvzlvnW+vTtrFG5rQIOREAAIhXeXsK9YfXFgfWrzqup3KyM0NMBAAAAFRPXDVbKPcXSSMlnRoxd5mknzrnVmv/jpJdJTX1nLdP0lgz898ire46XFJOxLhI0tO1lKU6+pd/3CKpxDm3Vvv/XRZJaiapi6Rkn/N2S/qRmS0PKScakJkrt/nOOycN6UyzBQAAAKA+c861kPRbz/QNZnZb5ISZlUma4pybJekT7f/9VZI6aH9jwBtrOKrM7L0aWHOjpI1VPc85d4K+22ihWJV/n+J6ScdGjD+XdIqZrQ7IWCLp0/KPvzjnmlU1L4DaVVpmuu6FhYE7Ax7ZM1tjBncIORUAAAAAAPFh864CPTN7rW+tfdN0nT6Q35mBWHDO6bbR/bRh5z7NWOF/r9BL89erU4sM/fK4niGnA4D48eT01YHv5198ZFca0gAAgP/585tfatveIt9a3/ZNdPERXUNOBAAAAFRP3LU/L3+IYYykf3lKiZK6SRqo7zdayJP0QzObWvMJQzfRM37NzPz/6lN3JGl/w4wBkoZJ6iH/RgszJQ00s+khZkMDMiug2ULvNo3VJMPvP0kAAAAA9ch1krIixh9pfwNIX2a2XtJFnumryps2NCQTPePXzWxrRSc553pJ+l3E1CpJxwQ1WvBjZtsreyyA+JA7bZXmrvb/XzczNUm3ndGXmzMBAAAAAA3WQx+tCHyg8dKjc5SSFHe3dQF1VkpSgv557hDlZDcKPOau977SC3P9d3QHgPquoLhUT87w/7Ndm8ZpOrVfu5ATAQCAeDVzRZ5enOf/u1NSgtNfRvdTUiLvaQAAAKBuicufYM2swMzGSTpT0oIDHLpX0v2S+pjZh2FkC5NzLkP7G09Eyq2FKNXxgKS7JM3T/t0uK1Ig6W1JP5I0wsyW12A2NHBBzRaGd20echIAAAAAYXLOJUg63zN9k5nZgc4zs/clfRwxlSVpbIzjxS3nXBNJozzTuZU8/TZJqRHjK8xsSyxyAYhPq7bu1R3//jKw/ttTDlK7pukhJgIAAAAAIH7k7SnU5JlrfGutG6dqzOAOIScC6r8mGcnKPX+YWmamBB5zw0sLNW15hf2FAaDeeXHeusDdqS84vIuSeWASAABIKiop0+9eXhRYv/zoHPVp1zjERAAAAEBsJNV2gAMxsxclveic6y5puKT2klIk7ZD0haSpZlYQxbo1ul2amd0k6aYYrJMvKS5+0zCzifr+7pWVOW+mpJmS5JxLkXSQpK6S2mn/QynJknZJ2i5pqaT5Zub/ji0QQ1/vLNCabfm+taFdaLYAAAAA1HMjJWVHjFdI+rCS506SdETEeJT2NxpsCMZKinwyerOkNys6yTnXVtKpEVNzzKzC8wDUXWVlputeXKiCYv/dOQ/v3lJnDe0YcioAAAAAAOLH49NWaV9xqW/tkiNzlJacGHIioGHo2DxDD48forMemqHCku+/d1Vcarr0ybmacsVhysnOrIWEABC+sjLTIx+v9K1lpibprGGdQk4EAADi1aNTV+qrzXt8a91aNtIVx3YPOREAAAAQG3HdbOEbZrZM0rLazoHqKW+i8Gn5B1CrZq3aFlgb2rVZiEkAAAAA1IJTPON3zcwqee67nvHRzrlGZrY3Brni3UTPeLKZlVTivAn67ntQT8UsEYC49NTM1Zq10v+9l4yURN16Rl85V6P9cAEAAAAAiFsFxaV6auYa31rLzFSN44FGoEYN7NRMd581QJdNnie/vwzsKijRBbmzNeXyw9S8UUr4AQEgZO99sUkrt/r/qfOsoR3VOC055EQAACAerduer7vf+yqwfvOoQ5SaRPNIAAAA1E0JtR0AAGrDrJV5vvPdWjZSq6y0kNMAAAAACNkAz3haZU80sw2SVkVMpUjqE4NMcc0510PSSM90biVP9za3+KDagQDErbXb8nXbW18G1m84ubc6Ns8IMREAAAAAAPFlyvz12ra3yLd28RFdlZ7CgwlATTvpkLb67Q8PCqyvzsvXJU/OUWFJaYipAKB2PPLxSt/5xASn8w/vGnIaAAAQr/7w2mLtK/b/Hem0Ae00snvLkBMBAAAAsUOzBQANUtDuisO6Ng85CQAAAIBa4L2DcnEVz/ceH3xHZow455o45/o55450zg1yznV2zoV51/UEz3i+mS2s6KTyjIMipookfVFeS3HOjXHOveqcW+ac2+ec2+ac+8I597hz7ifOuaTYfQkAapqZ6YaXPlN+kf8NFod2a65zhncOORUAAAAAAPHDzPToJ/4PNGamJuns4Z1CTgQ0XBce3lXjRwS/VzV71XZd/8JCmVmIqQAgXPPXbNesVf73Up7St63aN00POREAAIhH7y3epHcXb/KtZaUm6ben1PitUwAAAECN4oZ1AA3OjvwiLd20x7dGswUAAACgfnPOpUvy3rG8torLeI/vFX2iijnn5kvqp+83zdzjnJsq6UVJT5hZYQ1dP0HSeZ7pxyp5eh9JkVvYrzOzYufcIEmTJfX2HJ8mqVn5/HhJS51zvzCzt6ueHEDYXl6wXp8s2+pbS09O1O2j+yshwYWcCgAAAACA+PHRV1v11Wb/+xXGDumorLTkkBMBDZdzTv93ah+t2ZavD5ds8T3m5QUb1LVlpn5xXI+Q0wFAOB752L8JlCRdfES3EJMAAIB4ta+oVDe++nlg/ZoTe6lVVlqIiQAAAIDY896kDwD13tzV2wNrQ7vQbAEAAACo51pKinzSt1jS5iqusd4zblWtRBUbIP/3cDIlnSjpIUmrnHNjauj6x+i7DSqKJD1dyXO7e8a7nHNHS5qu7zda8NNT0hvOuSsqeT0AtWT73iL96fUvAuvXndRLnVpkBNYBAAAAAGgIJn3i/0BjgpPOP6xLuGEAKCkxQfeMG6jebbICj7nzvaV6ZYH3zwIAUPet3ZavtxZt9K0d2q25+nZoEnIiAAAQj+75z1dav2Ofb61v+yY699DOIScCAAAAYi+ptgMAQNjmBDRbaNM4TR2apYecBgAAAEDIMj3jfDOzKq6xt4I1a0MbSc855/5qZtfGeO2JnvHrZpZXyXOb+oxflJRSPs7T/sYN0yXt1P6v4wRJo/Xt+1YJku51zq01s1erFj2Yc66VpOwqnpYTq+sD9c2tb32hbXuLfGtDOjfThBFdwg0EAAAAAECcWbpptz5ausW3dkKfNurYnCaFQG3ISkvWpIlDNeq+qdqyu9D3mGufX6j2TdM1hE1cANQjkz5ZqbKAv5L+9Mhu4YYBAABxadnm3Xr44xW+NeekW04/RIkJzrcOAAAA1CU0WwDQ4MwNaLYwuHMzOccv+wAAAEA9522MUBDFGt527TXRbKFA0ruS3pK0QNIySTskpUpqJWmEpHGSfigp8heZa5xzeWZ2WyxCOOeyJJ3hmc6twhLeZgtdIj5/S9J5Po0bHnXO9Zf0mqSOnvkeZub/S13VXS7pxhitBTRoM1bk6bk563xryYlOt57RVwncYAEAAAAAaOAe/WRlYO3CI7qGmASAV/um6Xpk/BD95KHpKigu+169qLRMP31yrqZcPlKdWzSqhYQAEFs784v13Jy1vrWc7EY6umerkBMBAIB4Y2b63cuLVFzq353p3OGd1a+D97YgAAAAoG5KqO0AABCmopIyfbp2h29tcOdmIacBAAAAUAvSPGP/bdgPzLu1VXqUWYL8XVIHM/uxmT1gZtPNbIuZFZvZHjNbYWaTzexUSUdKWu85/8/lzQpiYYykyC31Nml/k4TKCmpEMU/S6T6NFiRJZvappOMk5UdMt5B0WRWuDSAEhSWl+s2UzwLrlx6Vox6ts0JMBAAAAABA/MnbU6iX5nvfxtuvX4cmGsL9CkCt69+xqe76yYDA+ra9Rbogd7Z25heHmAoAasZTM1crv6jUt3bxEd1ooAwAADRl/nrNWLHNt9YyM1XXnNgr5EQAAABAzaHZAoAG5fMNO1VY8v0O9JI0pAs3LwAAAAANQIFnnBLFGqkVrFktZvZqUBMCn2M/kXS0pK0R007SzTGKM9EznmxmJVU4P+i1ucLMvE0rvsPMlkq63TP90ypcG0AIHvhwuVZs2etb69IiQ1cc0z3kRAAAAAAAxJ/JM9eoKOBehQsP7yrneKARiAcnHdJWvz65d2B9+Za9uvzpuSou9f//GQDqgn1FpXps6krfWsvMFI0a2D7kRAAAIN7s3FesP7/5RWD9d6ccpCbpySEmAgAAAGoWzRYANChzV2/3nU9PTtRBbRuHnAYAAABALdjjGadFsUZ6BWuGysyWSbrWM/1D51zz6qzrnOsm6XDPdG4Vl/F7bRaZ2YxKnv+wJIsYd3bOda5kxdneAAAgAElEQVRihiD3Szqkih+nxejaQL2wbPMe3f/B8sD6Laf3VVpyYoiJAAAAAACIP4UlpXpi+mrfWtsmafph37YhJwJwIJcc2U1nDe0YWJ+6LE//98rnMrPAYwAgnv3r/9m77zgrqvOP49+zFXbpsHQWpDeRrqJYsXdjSWLDqElMbDHNFBOT+DMxMbaoiSYqJkYTEzXGLopEBRWBRVR6XTosZVlYYMt9fn/sGm/GM7B19u69n/frta94znNm5rtYsjt35jkfFKpoV5m3dunhfbivDwAAdO8bS0N/Xji8b0edNbJ7xIkAAACAxpXR1AEAIEqzV/mbLRzSq60y0+k/AwAAAKSA4Mv/Oc45Z7V7KjL3AOdsCn+W9GtJedXjNEmTJD1Vj3NeJil+S725ZvZRLc/h+7P5T00PNrP1zrllkgbETY+V5H86vRbMbLOkzbU5hh0Ggc+YmX707EcqC9nF79zRPXRE/04RpwIAAAAAIPE8/+EGFe3a561dengfnlUAEoxzTr84e7gKt5Vq5vKt3jVPzipUv7xcXTmxb8TpAKB+yipieuitFd5ay8x0XXxYQ/U8BwAAzdWyzSV6bOYqby0zver3JZ6fAQAAQLLh0zoAKcPMNKfQ32xhbO96bfgKAAAAoPkokhTfWCFTUudanqNHYFyrF/Ybg5nFJE0PTA+q6/lc1aeilwamp9ThVJs8c0tqeY7FgXFt/34BaAT/mLNW76/c5q21z8nUj04dEnEiAAAAAAASj5npT2+Hv9D45fH5EScCUBOZ6Wn6/UVj1C8v2Hv5M//30kJNXeC7BQ4AieuZuWu1oXivt3bxYfnqkJsVcSIAAJBIzEw/f2GhKmL+PWu+elRf9e/cKuJUAAAAQOOj2QKAlLFm2x5tKfHvFjGmT/uI0wAAAABoCma2R1JhYLq2TzQH1y+qe6IGtSYwzqvHuY6W1CduXCbpiTqcZ6FnbmctzxFczy9wQBPbumufbnvJ9693lR+eOkQdW2VHmAgAAAAAgMT07vKtWrSxxFs7b0xPtc3JjDgRgJpqm5OpRyaPU/uQf0/NpOv/VqBP1hdHnAwA6qaiMqYHpi/31rIy0nTVxL4RJwIAAIlm2qLNemvJFm+tW9sW+uax/SNOBAAAAESDZgsAUsbs1f7dFp2TRufzrg4AAACQQoLNEYbW8vjgdu2J0myhPDCuz5PakwPj581sa21PYmZrJAWfJq/tG9gtAuPS2uYA0LBufXGhdpQG/5NT5bC+HXTemJ4RJwIAAAAAIDE9/M7K0NrlR/SJLgiAOundMVcPXTpWWen+xyxLyyp1xZTZ2rTTv0s8ACSSF+ZvUOE2/8dsF4ztqc5tgh/JAQCAVFJWEdMvXlgQWr/plMHKycqIMBEAAAAQHZotAEgZs1dv984P7NxabVuyWwQAAACQQuYFxhNqeqBzrpukPnFT5ZLCP2mMVtfA2N9q/gCcc60knReYnlKXc1UrCIy71PL4zoFxrZs+AGg47ywt0rMF67y1rPQ0/d85B8s5F3EqAAAAAAASz4otu/TGos3e2vGDO6tvXquIEwGoi3F9Ouj28w4OrW/cuVdXPjZbpWUVEaYCgNqJxUz3v7nMW0tPc/raUf0iTgQAABLNozNWatVWf2Omsb3b68xDukecCAAAAIgOzRYApIy5Ic0WRvduH3ESAAAAAE3shcB4kqv5m8EnBsZvmtmuBsjUEI4MjNfU8TxfkJQbN94o6ZU6nkuS/h0Yj6npgc65dEkjA9OL65EFQD2UVcT0k39/HFr/5rH91Y8XRQAAAAAAkCQ9OmNVaO2KiQdFFwRAvZ0zqqeuPa5/aP2jdcW68e8fKhazCFMBQM29tmCjlm72f6R5zqge6tUhJ+JEAAAgkWwu2avfTfM3ZnJO+ukZw9h0AQAAAEmNZgsAUkLxnnIt3lTirY2l2QIAAACQamZKKoob95V0TA2PvSIwfq4hAtWXc+5oScEtZ96o4+kmB8Z/NbP6bMn1bGA8yTnXpobHniwp/s3tEklz6pEFQD08MmOlVmzZ7a31y8vV14/pG3EiAAAAAAAS047SMv1zzlpvbUi3Njq8b8eIEwGor29NGqjTRnQLrb/yyUb95jV6BQNIPGam+94Mf3ny6mOCHzECAIBUc8eri7Vrn//RoAvG9NLBPdtGnAgAAACIFs0WAKSEgsLtspDm8WP70GwBAAAASCVmFpM0JTD9U3eAFuzOueMlTYybKpH0VMOmqz3nXK6kewPTH5nZijqcq7ekowPTU+oYTZJUneP1uKkcSd+uQZY0ST8OTD9jZpX1yQOgbjYW79W9bywNrd92zsHKzkiPMBEAAAAAAInrn3PWak+5/zbWFUcexG6QQDOUlub02/MP0SG92oWu+f305Xpq9poIUwHAgU1fskUfr9vprZ16cDf1y2vlrQEAgNQwf+0O/SOkYWTr7Ax956RBEScCAAAAokezBQApYc7q7d75Tq2yld8hJ+I0AAAAABLA7ZJ2xY2PlvT9sMXOuR6S/hSYvsfMivZ3EeecBb6OOcD6e5xz3fcf/X/Wd5L0b0kjAqWf1vQcAZdJin/Se46ZfVzHc8X7QXDsnDv5AMf8StJhcWNT1d83AE3gtpcWqrTM/5LIuaN76FB25AQAAAAAQFLV7tFPzCr01jq1ytYZh3SLOBGAhtIiM11/vHSMerRrGbrmR89+pPdWbI0wFQCEMzPdP21ZaP2aY/tHmAYAACQaM9Mt//4kdFPL644foLzW2dGGAgAAAJoAzRYApISwZgtje7dnxwgAAAAgBVU3SbgtMP1L59wD8c0OnHNpzrmzJc2U1Cdu7XpJv22EaNdJWuGce9Y5d5Fzro9vkXOul3Puu5I+knRcoPwvM3u2thd2Vb8cXRqYnlLb8/iY2WxJj8VNZUp6zjl3i3MuL5BjsHPuH5K+GzjNr8xsYUPkAVA77y7fqn9/uN5ba52doZtOGRxxIgAAAAAAEtf7K7dpxZbd3tpFh+YrOyM94kQAGlLn1i30p8vGKjfL/+9yeaXp64/P0coi/38HACBK76/cptkhz05OGtJZQ7q1iTgRAABIJM/NW6+5hTu8tb6dcnXZhD7RBgIAAACaCM0WACS9isqY5q3x3wQY07t9xGkAAAAAJJDbJb0QmLtaUqFzbrlzbq6krZKelZQft2aPpAvMzP+LRv1lSzpb0uOSVjrnip1zi5xz7zvn5jrn1kkqlPRrSV0Dx74t6ct1vO6RkvrFjcskPVHHc/l8U9LsuHGWpJ9K2uCcW+ycm+WcK5S0UNJ5gWNflnRzA2YBUEPllTHd8u9PQus3nDBQnVu3iDARAAAAAACJ7Yn3C73zaU764vheEacB0BiGdGuj+748Wmkh+7vsKC3XV6Z8oB2lZdEGA4CA+6YtC61989j+ESYBAACJZve+Cv3y5fA9T24+faiyMnjlDAAAAKmBn3wBJL2FG0pUWlbprY3pQ7MFAAAAIFWZWUzS+ZL+FiilS+oraZSkdoHaVkmnmtmMxk/4X20kDZI0vjpTd8+amKqaLxxvZnvqeJ3JgfHzZratjuf6HDPbLekESa8ESumSBkoaJ8n3tPlDks40M/8vdgAa1V/eXa3Fm0q8tYFdWunSw3tHnAgAAAAAgMS1bXeZXvl4o7d23OAu6ta2ZcSJADSWYwd31s2nDw2tryzara8/PkdlFbEIUwHAZ+at2aF3lhV5a0f276RR+Tw7CQBAKvvDf5Zr08593toxg/J07ODOEScCAAAAmg7NFgAkvdmr/e8GZWekaXj3thGnAQAAAJBIzGyvmX1J0nmS5u1n6W5JD0gaambTGzHSV1XV/GFNDddvlHSPpEFm9n0zK6/LRZ1zOapqPBFvSl3OtT9mtkPSqZIukjR/P0srJL0u6Ugz+5qZVTR0FgAHtrlkr+6auiS0/rMzhysznVvMAAAAAAB86uk5a1VW6X+x+qJD8yNOA6CxTZ7QR5ccFt6M9L0V2/Tjf30kM4swFQBUuW/astDaN4/tH2ESAACQaNbt2KMH31rhrWWkOf34tPDGcgAAAEAyymjqAADQ2Oas3u6dP6RnO2Vl8EIAAAAAAMnMnpb0tHOuv6RDJfWQlCVph6SFkmaY2d46nNfVcv0fJf1RkpxzHSUNkdRbUp6kXEmVkrZLKpJUYGb+Tz5rn7NUUpuGOFcNrmWSnpD0hHOun6Txknqq6s97q6S1kt42s+Io8gAId/vLi1Wyz9/r5IxDuuvwfh0jTgQAAAAAQOIyMz05q9Bb69GupY4amBdxIgCNzTmnn54xVKu3leqtJVu8a56avVZ981rp60f3izgdgFS2cMNOvb5wk7c2pnd7Hda3Q8SJAABAIvnta4tVVuFvFjl5Qh/179wq4kQAAABA06LZAoCkF9ZsYUyf9hEnAQAAAJDozGyZpPBtXiJkZlslvVP9lZTMbLmk5U2dA8DnzVm9TU/PXeut5WSl60enDok4EQAAAAAAie3dFVu1omi3t3bhuF5KT6tVX1YAzURGepru+/IofeGBmVq6eZd3ze2vLFKfjrk6eXjXiNMBSFV3v74ktHbNsf3lHD+XAACQqhas36lnC9Z5ax1zs3Tt8QMiTgQAAAA0PbZ0B5DU1u3Yow3F/s1nx+TTbAEAAAAAACCoMmb6yXOfhNavO36AurZtEWEiAAAAAAAS3xPvF3rn09OcLhzXK+I0AKLUpkWmHpk8Th1zs7x1M+mGvxfoo7XFEScDkIo+WlusVz/Z5K0N695GxwzKizgRAABIJL96ZZHM/LVvnTBQbVtmRhsIAAAASAA0WwCQ1Gav2hZaG9ObZgsAAAAAAABBT8wq1Cfrd3prffNy9ZUjDoo4EQAAAAAAia1o1z69+slGb+34wZ3VpQ1NC4Fk16tDjh66dKyyMvyPZO4tj+mKxz7QhuI9EScDkGrunLo4tHbtcf3lnIswDQAASCRvL92it5Zs8db65uXqizSLBAAAQIqi2QKApDZ39XbvfL+8XLUP6SYPAAAAAACQqrbtLtMdr4Y/iPmzM4eFPjAOAAAAAECqenrOWpVX+reF/PKh+RGnAdBUxvRur9+cNyK0vrlkn66YMlu791VEmApAKpmzerveXOx/gXJotzY6cWjXiBMBAIBEEYuZfvnSotD6TScPVkY6zwIAAAAgNfGTMICkNjuk2cLY3h0iTgIAAAAAAJD47py6WMV7yr21U4Z31cQBeREnAgAAAAAgscVipidnFXprPdu31FH8Lg2klLNG9tANkwaE1hds2Knr/zZPlTF/gxYAqI87p4Y3U/72iQOVluYiTAMAABLJcx+u04INO721cX3a64ShXSJOBAAAACQOmi0ASFq79lVoYcgNgTF92kecBgAAAAAAILEt3liiJ973vxzSIjNNPz59aMSJAAAAAABIfO+u2KpVW0u9tS+Nz+elRiAFXX/8AJ01snto/fWFm/SrlxdGmAhAKpi5vEgzlm311kb2aqfjBneOOBEAAEgUe8srdcerS0LrN50yRM5x/wIAAACpi2YLAJLWvMIdCmsCP6Y3zRYAAAAAAAA+ZWa69cUFofdSrjm2v3q0axltKAAAAAAAmoGwxoUZaU7nj+kZcRoAicA5p9u/MGK/zyf98e2VenKW/78fAFBbZqY7Xwt/gfLbJw7kBUoAAFLYX95drXU79nhrpwzvyrsVAAAASHkZTR0AABrLnNXbvfMdcrPUt1NuxGkAAAAAAAAS1/TFW/T20iJvrWf7lrpyYt+IEwEAAABNwznXT9J4ST0lZUnaLmmRpJlmtrcJ8rSRNFhSb0ndJH36QecOSRslzTWz1VHnagjOuVxJYyUNkNROVX/exZI2SJptZryBioS3pWSfXv1ko7c2aUgXdW7TIuJEABJFi8x0PXjJGJ19/wyt3e5/qenmf32s/A45OqJ/p4jTAUg2by0t0uyQ5yXHH9RBR/LfGQAAUtaO0jL9btpSby0jzem7Jw2KOBEAAACQeGi2ACBpzV69zTs/Or89XZoBAAAAAACqlVfG9IsXF4TWf3DKELXITI8wEQAAABA959zZkm6WNDpkyS7n3BRJPzMzf6eyhsmRI2mypKMkHSqpTw2OWSXpUUn3mZn/Q1L/cX0krax9ys+YWa0/eHXOHSvpBkmnSQr9ZcM5t1TSQ5LuNzP/W6pAE/vnnLWqiJm39uVD8yNOAyDRdGqVrUcnj9O5D8xUyb6Kz9UrYqavPz5Hz35jgvp3bt0ECQEkAzPTb19bHFr/9gkDeV4SAIAU9sD05dq59/O/j0hV9y765rWKOBEAAACQeNKaOgAANIbKmKmgcIe3NrZP+4jTAAAAAAAAJK6/vrdaK7bs9tbG9WmvUw/uGnEiAAAAIDrOuWzn3OOSnlV4owVJaiXpGkkLnHNHNWKk7pLul3ShatBooVofST+TtNA5d27jxKo/51wL59yjkqZJOlP7abRQbYCk30ia75wb19j5gNqKxUxPzir01vI75LCDNABJ0oAurXX/RaOVnuZ/0blkb4W+MmW2tu0uizgZgGQxdcEmzV9b7K1NHNBJh/btGHEiAACQKNZuL9WUmau8tdysdF13/IBoAwEAAAAJimYLAJLS4o0l2uXpCC9JY3rTbAEAAAAAAECSdpSW6a7Xl4bWbz59KDteAQAAIGk559Ik/V3SRYFSpaSVkuZJCr61lCfpZefc4Y2f8H/skLRQ0vuSPpS02bOms6R/OOcmR5irRpxzGZKekTTZU96hqj/rDySt8dT7S5rqnBvTaAGBOpixvEiF20q9tS+O76W0kBerAaSeowbm6ZYzh4XWC7eV6mt/ma19FZURpgKQDGIx051Tl4TWv33ioAjTAACARHPna0tUVhHz1r52dD91apUdcSIAAAAgMWU0dQAAaAxzCrd757PS03Rwj7YRpwEAAAAAAEhM97yxVMV7yr21L4zuqRE920WcCAAAAIjUdyWdFZj7g6RfmNl66b8NGc6SdLek/Oo1OZKecs4NNzP/FrL197GklyW9LWmWmW0KLnDOHSTpcknfkdSyejpN0oPOuffMbFEtr/mapN/UPfJ+3SDplMDcDEk/lPS2mdmnk865fEk3SrpWn20i0lbSn51zI83M/0sMELEn3i/0zmekOZ0/plfEaQAkuksO660VW3bp0RmrvPUPVm3XTU9/pDsvOITmpwBq7KWPN2jRxhJvbdKQzhrZi3v8AACkqo/XFevZeeu8tbzW2bpy4kERJwIAAAASF80WACSlOau2eeeH92ijFpnpEacBAAAAAABIPMu37NJf3l3trbXMTNf3TmbHKwAAACQv51xHST8KTP/AzH4VP2FmMUnPOudmSXpHUp/qUk9VNQT4aQNH2yBpgJktO9BCM1sp6SfOuWckTZPUvrqUJelnki6s7bXN7PVaHnNAzrlMVTVViPeCpLPN7HNbeJtZoaQbnHNzJP05rjRU0nmSnmzojEBtbSnZp6kLPtcDRZJ04rAuymvNzpAAPu/Hpw3V6q2lmrZos7f+bME69emYq+snDYg4GYDmqDJmumvqktD6t04YGGEaAACQaG5/ZZE+a2/6v248YaBysnidDAAAAPhU2oGXAEDzM7dwh3d+bJ8OEScBAAAAAABITLe9uFAVMf/TFVcf009d2rSIOBEAAAAQqe9Jah03fkvS7WGLzWydpCsD09+qbtrQYMxsd00aLQSOmSfpB4Hp05xzifJD/UR91ghCksolfc3XaCGemf1F0quB6TMaOBtQJ//+cH3o79RfHt874jQAmov0NKd7vzRKg7u2Dl1z1+tL9K8C/+6zABDvuXnrtHzLbm/ttIO7aVj3thEnAgAAieK9FVv19tIib61fXq7OH9Mz4kQAAABAYqPZAoCkU7Rrnwq3lXpro/Pbe+cBAAAAAABSydtLt+iNkB30urVtoasm9o04EQAAABAd51yapMsD07eYhe31VsXM3pD0dtxUa0kXNHC8unpSUixunCspv4myBA0KjN83s/U1PPbpwLh/A+QB6u2ZuWu98/kdcjShX4P2YAGQZFplZ+iRyeOU1zo7dM33/jlfs1ZuizAVgOamvDKmu19f6q05J90waUDEiQAAQKIwM/32tcWh9ZtOGaKMdF4lAwAAAOLxEzKApFNQuCO0Njq/XYRJAAAAAAAAEk9FZUy3vrAwtH7TKYPVMis9wkQAAABA5CZIyosbr5A0vYbHPhwYn90QgerLzHZK2hKY7tQUWTw6BMZranFsYWDMB75ocos27tQn63d6a+eN6am0NBdxIgDNTfd2LfWnS8eqRab/8c2yypi+9pfZWlnk37EeAJ6cVRi6IdXZI3toQJfWEScCAACJ4q2lRfpg1XZvbWzv9po0pHPEiQAAAIDER7MFAEmnoNB/c6BHu5bq3KZFxGkAAAAAAAASy98+WKPFm0q8tVH57XTmId0jTgQAAABE7rTAeKqZWQ2PnRoYH+Ocy22ATA0h+GFoeJf6aBUHxi1rcWxwbVE9swD19szcdaG1c0b1iDAJgObskF7tdPeFI+VC+rNsLy3XV6Z8oO27y6INBiDh7dxbrrtfX+qtpac5XX/8gIgTAQCARGFm+u1ri0Pr3z1pkFzYLyEAAABACqPZAoCkU1Dof2ZoVD6bnAAAAAAAgNS2c2+57pq6JLR+8+lDebgCAAAAqWBkYDyzpgea2XpJq+KmsiQNbYBM9eKcGySpbdzULknhP/xHa15gPNbV/BeP8YHxrAbIA9RZRWVMzxb4my2MP6iDenXIiTgRgObs5OHd9INTBofWVxbt1tf+Mkf7KiojTAUg0f1h+nJtC2nEct7onurTKVF6wQEAgKhNXbBJ89cG+55WmTigkw7t2zHiRAAAAEDzQLMFAEmlMmb6cG1Ys4X2EacBAAAAAABILA/+Z7m2hjyEedbI7hrN/RMAAACkhiGB8YJaHh9cHzxfU/hxYPxXM6uoy4mcc72cc+OccxOdc8Occ3n1zPaupMK4cU9JX6xBjg6SroqbMkmP1jMLUC8zlm/VlpJ93tp5o3tGnAZAMrhqYl99aXx+aH3Wqm266emPZGYRpgKQqNbv2KOH31nprbXITNMNJwyIOBEAAEgUsZjpzv1svPDtEwdFmAYAAABoXmi2ACCpLNlUotIyfzf3UfntIk4DAAAAAACQODbt3Bv6EGZ2Rpq+d3L4LnoAAABAsnDOtZQUfKNxTS1PE1zfZE8qO+daOOfulnRx3PQWST+pw+lOdM6tV1VjhFmS3pL0saTNzrmVzrlHnXOH1/akZlYp6QeB6YeccyeHHeOc6yHpJUmd4qbvN7MPa3t9oCE9M3etdz47I02nHNw14jQAkoFzTj8/a5gmDugUuubZgnW6542lEaYCkKjueG2x9lXEvLUrj+yrbm1bRpwIAAAkihc+2qBFG0u8tUlDumhkL96lAAAAAMJkNHUAAGhIBYU7vPNZ6Wka1r1NxGkAAAAAAAASx92vL9Xecv9DmF89qq96tOMhTAAAAKSETpJc3Lhc0uZanmNdYNy5XokOwDl3qKTWcVMtqq85VtIXAtffKOlUM6vt9yRJ3fZT6yNpsqTJzrlpki43s8KantjMnnDOjZD0/eqpVpJerj7XC5KWq+rvRVdJR0q6UFJu3Cn+IumGml4PaAwle8v16icbvbWThnVV6xaZEScCkCwy09N0/0Wjdd7vZ2rJpl3eNXe/vlS9O+bonFE9I04HIFF8vK5YzxYEfxWp0qlVlr5+TL+IEwEAgERRURnT3VOXhNZvPGFghGkAAACA5odmCwCSSkHhdu/80O5tlJ2RHnEaAAAAAACAxLBs8y49Ndu/WW/H3Cx97WgewgQAAEDKaBUYl5qZ1fIcuw9wzob2oKRDDrBmr6Qpkn5iZlsaOc9xkgqcc+eY2Vs1PcjMbnLOzZX0a0m948513H4Omy/pVjP7R53ThnDOdZaUV8vD+OUphb380cbQJobnju4RcRoAyaZNi0w9Mnmczr5/pop27fOu+d4/56tb25Y6rG/HiNMBaGpmpl++vFBhv7lcP2mgWmXzSDgAAKnqmYJ1WlEUvGVZ5bQR3TSUTSsBAACA/Upr6gAA0JAK1uzwzo/KbxdxEgAAAAAAgMRxx6uLVRnzP4V53fEDeAgTAAAAqSTYGGFvHc6x5wDnjFqZpHsl3VHHRgtrJf1e0vmShkhqJylTUidJ4yR9T9KKwDEdJD3nnBtcmwuZ2VOSRqmqMcSBfCzpDknP1eYatfCN6mvU5quxsqAZeHruWu98XutsHdm/U8RpACSjnu1z9KfLxqpFpv+xzvJK01f/PFvLNpdEnAxAU5u+ZItmLNvqrfXLy9UXx/WKOBEAAEgUZRUx3fP6Um8tzUnfmjQw4kQAAABA80OzBQBJo3hPuZZt3uWtjcpvH3EaAAAAAACAxDBn9Xa98slGby2/Q46+ND4/4kQAAABAk2oRGJfV4RzB7aZb1jFLQ8lSVUOExc65PznnWtfwuGJJZ0rqbWbfMLN/mtkiMys2swoz22pms83sN5IGSvqZpFjc8e0kPe6cczW5mHMuwzn3I1U1bphcg0OGS/qzpBXOudNr+D0BjWLNtlK9v3Kbt3bOqB7KSOcRLAANY2Svdrr7wpEK+3/XnXsrNPnRD7SlJPjjCIBkVVEZ020vLgyt33TKEGXyswgAACnr77PXaN2OYG/YKueM6qn+nZu6TywAAACQ+Li7BiBpfLhmR2htVK92ESYBAAAAAABIDGam219eFFr/zkmDlJXBbWIAAACklL2BcVYdzpF9gHM2KDMbaWbu0y9JbVTV/OBiSS/HLU2XdIWkd5xzHWtw3u1m9ryZxWqwttLMbpF0Y6A0RtK5BzreOddG0nRJt6qqSYMkrZX0XUmHqOp7ypaUL+lCSdPiDu8h6Xnn3DUHug7QWP5VsC60du7oHhEmAZAKTh7eTT84ZXBofe32PbrysQ+0p6wywlQAmso/5qzV0pBNqA49qIMmDekccSIAAGH7BE0AACAASURBVJAo9pZX6r5pS721jDSn648fEHEiAAAAoHniKVoASaOg0N9soVOrbPVs39SbyQAAAAAAAERv2qLNmrXKv/Pm8B5tdPrB3SJOBAAAADS54FtKLepwjuCHj/43nxqJmZWY2VIz+6uZnSrpREnb45aMkPRYI137Hkn/CUxfsr9jnHNO0tOSjoibflHSUDO7w8zmV39PZWa2xsyeMrPjJV0tyeKOudc5d1wDfBufekDS8Fp+ndWA10czYWZ6JqTZwtBubTS4a5uIEwFIBVdN7KuLD8sPrX+4tljX/a1AlTELXQOg+du9r0J3Tl0SWv/RaUNU9eM2AABIRY+/t1qbdu7z1i4Y10v5HXMiTgQAAAA0TzRbAJA0CtZs986Pym/HBwoAAAAAACDlVMZMt7+yKLR+08lDlJbGPRMAAACknGBjhBxX+w8Tcw9wzkiZ2VRJp0mKxU2f5pw7oZEu+dvA+DjnXMZ+1l8qaVLceKGk882sZH8XMbM/SLo1bspJ+l1tgh7g/JvN7JPafEla3lDXR/Mxt3CHVhbt9tbOHd0j4jQAUoVzTrecMUzHDsoLXTN1wSbd+uKCCFMBiNpDb63QlhL/C5RnjeyuET3bRZwIAAAkit37KvTAdP+tqqyMNF17XP+IEwEAAADNF80WACQFM1NB4Q5vbVQ+HygAAAAAAIDU8/TctVqyyf/O18QBnXTkgE4RJwIAAAASQpGk+C2gMyV1ruU5gm9Xb65XogZgZu9KejwwPbmRLjdN//tn2FpSt/2svz4w/oWZ7anhtW6XFP9B8FDn3BE1PBZoEM/MXeudT09zOmskzRYANJ6M9DTd9+XRGta9TeiaR2es0iPvrIwwFYCobN65Vw+9tcJby0pP03dOHBRxIgAAkEimzFylbbvLvLWLD+2tbm1bRpwIAAAAaL5otgAgKaws2q3iPeXe2qhe7SNOAwAAAAAA0LT2llfqrqlLQuvfP3lwhGkAAACAxFH9kn9hYDq/lqcJrl9U90QN6pnAeEJjXMTMdkvaHpj2brvtnOsgaWTcVEzSS7W81puB6aNqejxQX/sqKvX8h+u9taMH5imvdXbEiQCkmtzsDD0yeZy6t20RuuYXLy7Qq59sjDAVgCjcOXWJ9pRXemuXH9FHvTrkRJwIAAAkil37KvTHt/1NmVpmpuvqY/pFnAgAAABo3mi2ACApFBTu8M6nOWlEz7YRpwEAAAAAAGhaj81cpQ3Fe721s0Z21/Ae3C8BAABASgs2Rxhay+OHHOB8TWV5YNy1Ea8V7ISfGbKujyQXN95mZsW1vFZwu+4etTweqLNpCzdr594Kb+3c0fyjCCAaXdq00COXj1Pr7Axv3Uy6/m8FKigM9kIC0Fx9vK5Yf5+9xltrl5OpbxzbP+JEAAAgkTz+3mrtKPVvVHn5EX1oDgkAAADUEs0WACSFgjX+DwsHdW2j3JAPGgEAAAAAAJJRcWm57n9zmbeWme707RMGRZwIAAAASDjzAuMJNT3QOddNVQ0EPlUuaUEDZGoM/ieu68k5lyGpY2B6S8jy4JPd/rfW9y/4faTX4RxAnTw9d613vnWLDE0a0iXiNABS2eCubfT7i8coI81563vLY7risdlaVbQ74mQAGlosZvrJcx/LzF+/7rgBatsyrNcZAABIdnvKKvXHt1Z4a62yM/TVo/pGnAgAAABo/mi2ACApFBTu8M6Pym8XcRIAAAAAAICm9cB/loXuunnRob2V3zEn4kQAAABAwnkhMJ7knPO/ufh5JwbGb5rZrgbI1BB6B8abGuk6h0mK73hfIWljyNqtgXFH51xWLa/XIzAOa+wANKiiXfs0fbH/H7fTR3RXi0z6fgCI1pEDOum2cw8OrW/bXabLHp2lol37IkwFoKE9PXet5oY8D9m7Y44uPiz4Yz8AAEglf31/tbbuLvPWLpvQW+1yanvrDQAAAADNFgA0e6VlFVq0scRbG9WLZgsAAAAAACB1bCzeqykzVnlrrbIzdO1x/aMNBAAAACSmmZKK4sZ9JR1Tw2OvCIyfa4hADeSMwHh+I10n+GfwrpmVhqxdp6pmDJ/KlDSxphdyzqXp839vltf0eKA+nv9wvSpi/i2lvzA62AMEAKJxwdheum4/9/hWby3VFVM+UGmZvxkrgMRWvKdcv3p5UWj9h6cOUVYGj34DAJCq9pZX6qG3VnhrOVnpuuLIvhEnAgAAAJIDd9wANHsfrS1WZcgDDqPy20ecBgAAAAAAoOnc9+ZS7auIeWtfPaqvOrbKjjgRAAAAkHjMLCZpSmD6p845t7/jnHPH638bBZRIeqph09WNc26IpMsD0w3eCMI5d4ykSwLT/wpbb2a7Jb0XmP5OLS55iaSegbk3anE8UGfPzF3nnc/vkKMxvXkWAUDT+dYJA3XuqPCmLx+uLda1TxSootJ/nxBA4rpr6pLQnaqPHpinE4d2iTgRAABIJE/NXqPNJfu8tYsP660OuVkRJwIAAACSA80WADR7BWt2eOfbtMhQ3065EacBAAAAAABoGmu2lervH6zx1jq1ytYVRx4UcSIAAAAgod0uaVfc+GhJ3w9b7JzrIelPgel7zKxofxdxzlng65j9rG3tnHvAORdsLrBfzrnhkl6VFP809WpJ/9jPMSc45y53zmXU4jrHSXpGUnrc9AZJfzjAoY8Hxic7526uwfXGS/pdYHqama090LFAfa0s2q2P1hV7a+eO7qED9GYBgEblnNOvvjBCR/TvGLrmjUWbdfNzn8jMv4ENgMSzcMNO/fndVd5aVnqabjlzGD+DAACQwsoqYvrD9OXeWnZGmq6cyPMAAAAAQF3RbAFAs1dQuN07PzK/vdLS+HABAAAAAACkht9NW6rySv/D09ce11+52TV+hwoAAABIetVNEm4LTP+yutlB908nnHNpzrmzJc2U1Cdu7XpJv23gWOmSrpa0wjn3vHPuUudcP+d5o8o5l+Wcm+Cc+72kuZJ6xZVN0rVmtmc/1+oh6RFJq5xzdznnjnXOtfVcJ905d6hz7jFJUyW1jyvHJH3TzEoP8H09ImlJYO7nzrl/V5/7f74/51wP59zPJb0lqXXgej88wLWABvHi/PWhtXP2s5s8AEQlKyNNv794jAZ3bR265slZhbr/zWURpgJQV2amnzz3sWIh/VGuOuogHcTGUwAApLSn567V+uK93tqXxuerc+sWEScCAAAAkgdP1wJo1sxMcwt3eGujerWLOA0AAAAAAEDTWLFll56eu85b69Gupb44vpe3BgAAAKS42yVNkHR63NzVkr7qnFstqVjSQZKCHzzukXSBmfk/qKy/zOpMn+Yqcc5tlLRDkpPUVlWNHzI9x5qkq8zs+Rpeq4ekG6q/5JxbJ2mbpN2S2kjKl9Qq5Do3mNmzB7qAmZVXN6x4R1KHuNIZ1V/bnXOFksokdam+ps/1ZvZ+Tb4poL5emL/BOz+yVzv17siLjgASQ5sWmZpy+Xid+8CM0Jeu7nhtibq2banzxvSMOB2A2vjXvHX6YJV/06nubVvom8f2jzgRAABIJOWVMT0w3d9ILSs9TV8/ul/EiQAAAIDkktbUAQCgPtYX79WWkn3e2qh8mi0AAAAAAIDUcM8bS1UZsuXVdcf3V3ZGesSJAAAAgMRnZjFJ50v6W6CULqmvpFH6fKOFrZJONbMZjZ/wv1pLGiBpnKSx1X/ta7SwVNLxZvZwPa7VQ9LBkg6TNFT+RgsbJJ1kZr+r6UnNbKGkoyTN95TbSzpEVd+fr9FCiaTLzey+ml4PqI9lm0u0aGOJt3b6iG4RpwGA/evatoWmfGW82rQI33frpqfn660lWyJMBaA2SvaW67aXFoXWf3z6UOVksbceAACp7Ll567Vm2x5v7fyxPdW1bYuIEwEAAADJhWYLAJq1gkJ/N2epakcJAAAAAACAZLd4Y4n+/eF6b61PxxydO5pd6wAAAIAwZrbXzL4k6TxJ8/azdLekByQNNbPpjRSnWNLRkm6XNEtSWQ2OKZf0hqSLJR1sZm/W8FrTJP1U0nRVNTM4kJikuZK+Lqm/mU2t4XX+y8w+UVVDhSslzanBIZtU9Wcx1Mym1PZ6QF29MH9DaO3Ug2m2ACDxDOzSWg9dOlZZ6f7HQStipqsfn6OP1xVHnAxATdz9+tLQDaeO7N9JpwzvGnEiAACQSCpjpvvfXOatZaQ5XX1Mv4gTAQAAAMmHVqcAmrWCwh3e+b55uWqXkxVxGgAAAAAAgOjdNXWJzPy1GyYNVGbIQ9YAAAAAPmNmT0t62jnXX9KhknpIypK0Q9JCSTPMbG8dzutqsdYkvVX9JedctqShkvpJ6iapdfXS4upciyR9ZGY1acoQvFahpJ9L+rlzzlVfo7+kXpLaSWqhqgYT2yWtkTTLzHbW9jqe65ZJeljSw865DqpqvpBffc1MVX1vWyQVmNnS+l4PqIsXQ5otjO3dXt3btYw4DQDUzGF9O+rOCw/RNU8UeOu7yyo1+dFZ+ufXJ6hPp9yI0wEIs2RTiabMXOWtZaY73XLmMFX9uA4AAFLVC/PXa2XRbm/tC6N7qmf7nIgTAQAAAMmHZgsAmrWCwu3e+VG92kecBAAAAAAAIHofrS3WK59s9NYGdG6lMw7pHnEiAAAAoHkzs2WS/FvFRczM9kkqqP5qzOuYqr7nSL9vM9sm6dUorwkcyOKNJVq6eZe3dtqIbhGnAYDaOX1Ed20s3qtbX1zorRftKtMlj7yvp78+QZ3btIg4HYAgM9NPnvtYlTF/N+WvHHmQ+nduFXEqAACQSGIx033T/Lfs0tOcvnFsv4gTAQAAAMmJLc0ANFv7Kir18Xr/5imj8ttFnAYAAAAAACB6d05dHFq78YSBSk9jxysAAAAAAGrqhfnrvfPOSaceTLMFAInvyol99ZUjDgqtr9m2R5c9+oF27i2PMBUAn+fnb9B7K7Z5a13aZOu64wZEnAgAACSaVz7ZGNoU8qxDuqt3x9yIEwEAAADJiWYLAJqthRtKVFYR89ZotgAAAAAAAJLdnNXb9ebiLd7asO5tdNKwrhEnAgAAAACg+TIzvTh/g7c2vk8HdWEXeADNxI9PG6LTRoQ3iFm4Yaeuemy29pZXRpgKQLzi0nL94oUFofUfnTZUudkZESYCAACJxsz0u2nLvDXnpG8c2z/iRAAAAEDyotkCgGaroHC7d75lZroGdWkdcRoAAAAAAIBo3Tl1cWjtxhMGKi3NRZgGAAAAAIDmbcGGnVpRtNtbO30/Ly0DQKJJS3O684JDNKFfx9A176/cpuv/VqDKmEWYDMCn/u+lBdpSss9bO7xvR53Bzx4AAKS81xdu1sINO7210w7upv6dW0WcCAAAAEheNFsA0GwVFO7wzo/o2VYZ6fznDQAAAAAAJK+Zy4s0Y9lWb21kr3Y6bnDniBMBAAAAANC8vTh/g3c+zUknD+eFRwDNS3ZGuh68ZIyG92gTuubVTzbpx//6WGY0XACi9M7SIj01e623lpHm9LOzhsk5mikDAJDKzEwPTF8WWr/muP4RpgEAAACSH28jA2i2CtZs986Pym8fcRIAAAAAAIDomJnufG1JaP07Jw7iQUwAAAAAAGrBzPRCSLOFw/p2VF7r7IgTAUD9tW6RqUcnj1fvjjmha56cVai7pobfawTQsErLKnTTM/ND61cceZAGdmkdYSIAAJCIPli1PXRjypOGddHgruFN1QAAAADUXkZTBwCAuthSsk9rtu3x1kblt4s4DQAAAAAAQHT+s2SLZq/2N6E89KAOOqJ/x4gTAQAAAADQvH28bqcKt5V6a6eP6B5xGgBoOHmts/WXrxyqc38/U0W79nnX3DttmTq2ytZlE/pEGw5IQXe8ukRrt/ufe+zTMUc3TBoYcSIAYZxz/SSNl9RTUpak7ZIWSZppZnubMltUnHMdJR0hqZ+kXEm7JS2XNMPMtjbC9dIljZE0VFJnSZmSdklaK2mhpEVmFmvo6wKJ6MH/LA+tXXvcgAiTAAAAAKmhWTRb4GZFcnLO9ZB0uKTeklpK2ilpiaR3zGxXU2ZD4pu3xt+pUZJG9aLZAgAAAAAASE5mpjv3s9Pct08cJOdchIkAAAAAAGj+Xpi/3jufnuZ08vCuEacBgIaV3zFHj31lnL744Hsq2VfhXXPL85+oXU6mzhrZI+J0QOqYW7hdj85cGVr/5bkj1DIrPcJEAHycc2dLulnS6JAlu5xzUyT9zMyKIgsWwjmXI2m+qhoixHvMzCbX8ZyHSPq5pNMlpXmWVDrnXpR0s5nNr8s1Atc7SNJ3JX1J0v4eAt/pnHtT0kNm9lJ9rwskqsUbS/TGos3e2sQBnTS8R9uIEwEAAADJz/fLb8Jwzp3tnJsjaZmkJyT9WtKtku6X9IakLc653znnOjVhzP9yzuU455Y55yzwNaWGx0/2HFufrz4N8D11dc5t85z7lnqc8+jqGx1rJf1D0h2SfiHpHkkvS9rqnPtzQ+RH8ioo9O/e2KNdS3Vu0yLiNAAAAAAAANGYvmSL5q8t9taOGpin8Qd1iDgRAAAAAADNm5nphfkbvLUJ/TqqQ25WxIkAoOEN695WD106VlkZ/kdGzaQbn/pQbyzcFHEyIDXsq6jU9/85X2b++pcPzdfh/TpGGwrA/3DOZTvnHpf0rMIbLUhSK0nXSFrgnDsqknD7d6s+32ihzpxz10uaLelMhb9rkl5dn+Ocu7Ye10pzzv1A0kJJV2v/jRYkqY2ksyRdWtdrAs3Bg28tD6197agG+9cdAAAAQJyEbLbAzYoGU9YA57hfUvsGOI9clV9Lmi7pmP0szZJ0iaSPnXNfaIhrI/l8uHaHd35U/oHuswEAAAAAADRPZqZ7Xl8aWv/2CQMjTAMAAAAAQHKYt2aH1u3Y462dMaJ7xGkAoPEc3q+j7v3iSKU5f70yZvrGX+fqvRVbow0GpIAH3lyupZt3eWtd2mTrplMGR5wIQDznXJqkv0u6KFCqlLRS0jxJwW7oeZJeds4d3vgJ/Zxz4yVd34Dnu1HS3ZIyAqUNkuZU/2+8DEn3Oueuq8O1MiX9TdJtkrID5WJJiyTNUlUjhtLanh9ortbv2KN/z1vvrQ3v0UZH9Kc5EwAAANAYEq7ZAjcrGkyBmfl/y6oh59x5ks5toDySdK+k7wbmTNIaSXMlFQVquZL+7pw7pwEzIAnEYqb5a/w7OI7sRbMFAAAAAACQnN5ZVqR5a/wNKI8f3FmHcF8EAAAAAIBae3F+8H2hKhlpTicO6xJxGgBoXCcP76Zbzz44tL6vIqYrH5ut+SEb4QCovUUbd+qB6ctC6/939sFq0yIzwkQAPL4r6azA3B8k5ZtZXzMbJamDqp6rL4xbkyPpKedc22hifsY5lyXpYX32Psjuep5vgqRfB6anSxpjZt3NbKyZdZc0TtJ/Aut+W/0uRW08LOn8uHGFqjaJHC+pvZkNMbNDzWyopNaShki6QdJMVb1/ACSlh99ZqYqY/x/xrx3VT86FdE4DAAAAUC8J12xBqX2z4lVJJ9Th6yTPNafUMYMkyTnXXtJ9cVP1vQFzgaRrAtNPSxpkZvlmNsbM8iRNkjQ/bk26pMecc33qc30kl5Vbd6tkX4W3xksFAAAAAAAgGZmZ7nl9aWj9uuMHRJgGAAAAAIDkEIuZXvzI32xh4oBOapeTFXEiAGh8Xz40X985cWBofde+Cl32yCwt3VQSYSogOVXGTN9/+iOVV/pfmjzjkO6aNJTmTkBTcs51lPSjwPQPzOzq+I0PzSxmZs9KmiBpVdzanpJubPSgn/dDScOr/3qdpAfreb7fqOq5/U89L+kkM5sbv8jMZks6UdKLcdMZ1cfXiHPuYkmXxE2tV1VTh2vM7AMz+5//aFb/2S8ys3vM7AhJ36jptYDmpLi0XE/OKvTW8jvk6JThXSNOBAAAAKSOhGq2kOo3K8xsg5m9Xtuv6sNz405VLumJun87kqS7JH16B7dA0rN1PVF1M4rbA9N/kHS+mf3PE+Jm9oakoyTNjptuLelndb0+kk9Y5/Q0Jw3r3ibiNAAAAAAAAI3v3RVbNXv1dm/tmEF5NKAEAAAAAKAOCtZs14bivd7aaSO6R5wGAKLzzWP766qJB4XWt5eW65KHZ2nNttIIUwHJ59EZK/XhGv/zju1yMvXTM4ZGnAiAx/dU9az6p97S5597/y8zWyfpysD0t6rfg4iEc26YpB/ETV0jqc5dkpxzp6jqvYxPbZV0hZmV+dZXz3+let2njnLOnVCDa3VS1XsKnyqWdLSZzQ85xHd9/4emQDP3l/dWqbSs0lu7auJBykhPqNe/AAAAgKSSaD9tp/zNijqaHBi/YGZFdT2Zc+5ESZdVDyslXVX9v3V1haQ+ceOlkr4V7Dr5KTMrrr5+/A2ai5xzg+uRAUnkwzXF3vmBXVorJysj4jQAAAAAAACN7943lobWrj1uQIRJAAAAAABIHs9/uME7n5WephPYZRpAEnPO6YenDtGFY3uFrtm4c68ufvh9bd7pb0oDYP8Kt5bqjtcWh9Z/esZQdWqVHWEiAEHOuTRJlwembwl7xv1T1ZsLvh031VrSBQ0cz6s688OSsqqnnjWzf9XztMH3Me43sy37O8DMNkt64ADn8fmRpE5x4x+a2bIaHAcktb3llXp0xipvrWNuls7fz8/tAAAAAOovYZotcLOizhnaSjo7MD2lHufLlfRQ3NS9ZjanruerFrxx8ksz2+8nMGa2QNLf46bS9fl/PpCiPlzr7/Q8omfbiJMAAAAAAAA0vvdXbNV7K7Z5axMHdNKY3u0jTgQAAAAAQPNXGTO99JG/2cJRAzupbcvMiBMBQLScc7rt3IN12sHdQtes3lqqSx+ZpR2l3o2tAYSoqIzpxqfmaW95zFs/ZlCezh7ZI+JUADwmSMqLG6+QNL2Gxz4cGAef528sN0g6tPqvd6pqo8g6c85lSzopMP1IDQ8PrjvFOZflXfnZtS6Nm9oo6cEaXgtIav+cs1Zbd/t/5p48oY9aZKZHnAgAAABILQnTbEHcrKirCyS1jBtvlvRSPc73S0m9q/96taSb63EuOed6ShodN7VL0lM1PDz49/Ws+mRBciivjGnB+p3e2iG92kWcBgAAAAAAoPH9blr4Zi7XHT8gwiQAAAAAACSP2au2aXPJPm/t9BHdI04DAE0jPc3prgtH6uiBeaFrFm0s0eRHP9CufRURJgOat99PX67Zq7d7a7lZ6fq/cw6Wcy7iVAA8TguMpx5oo8j4tYHxMdWbHjYa51xfSb+Im/qBma2v52mPkRSfe7GZra7JgWa2StLSuKnWko7ezyHnSOoQN/6bmVXWLCaQvCpjpj++vcJby8lK1yWH9/bWAAAAADScRGq2wM2KupkcGP/VzOr0qYZzboKkb8ZNfcPMdtc1WLXg39cZtTjnDEmlceNBzjmeHk9xizeWaF+Fv9vzIT1ptgAAAAAAAJLLnNXb9M6yIm/t8L4dNa5PB28NAAAAAADs3wvzN3jnszLSNGlol4jTAEDTycpI0x8uHqOxvduHrpm3Zocuf3SWSstouAAcSEHhdt39xtLQ+vdPGawe7VqG1gFEamRgPLOmB1a/N7AqbipL0tAGyLQ/f5SUU/3X70r6fQOcs85/BtVmHOB88YLvFbxZy2sBSemVjzdq9dZSb+2L4/LVLicr4kQAAABA6kmkZgvcrKil6sYDEwLTU+p4rmxJf9Jn/0z83cxeqnu6/6rP39cKSbMOcD6kmPlri73zWRlpGtS1dcRpAAAAAAAAGte9bywLrV13PH1JAQAAAACoi1jM9OonG721YwflqdX/s3fn4VVV9/7HPysnIwlJgCQMCSHM8yA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\n",
+ "text/plain": [
+ "<Figure size 5100x1200 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Q5ck7ZP0tyvS",
+ "outputId": "3185d6f5-61d6-4230-9654-7996b4052b42"
+ },
+ "source": [
+ "device=torch.device('cuda')\n",
+ "bins=101\n",
+ "tau=torch.linspace(0,0.3,bins,device=device,requires_grad=True).reshape(1,bins,1)\n",
+ "out=agent.net(torch.tensor([2,3]).reshape(1,1,2).float().to(device),mean=False,t=tau)\n",
+ "atoms=out[0].detach().cpu().numpy().squeeze()\n",
+ "mean=atoms.mean(0)\n",
+ "print(mean)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[ 0.25098237 0.34306845 -1.0511526 0.11468245]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "G1vk7GELxtTH",
+ "outputId": "e004fcda-1695-476f-f796-74a08f906010"
+ },
+ "source": [
+ "device=torch.device('cuda')\n",
+ "bins=101\n",
+ "tau=torch.linspace(0,1,bins,device=device,requires_grad=True).reshape(1,bins,1)\n",
+ "out=agent.net(torch.tensor([2,3]).reshape(1,1,2).float().to(device),mean=False,t=tau)\n",
+ "atoms=out[0].detach().cpu().numpy().squeeze()\n",
+ "mean=atoms.mean(0)\n",
+ "var=(atoms**2).mean(0)-mean**2\n",
+ "plt.figure(figsize=[22,5])\n",
+ "for i in range(4):\n",
+ " plt.subplot(1,4,i+1)\n",
+ " title= 'Action: '+str(d[i])+' - Mean: '+str(mean[i])\n",
+ " plt.title(title)\n",
+ " plt.plot(tau.detach().cpu().numpy().squeeze(),atoms[:,i])\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1584x360 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "yMpeDbJV4I_A",
+ "outputId": "59d95808-40c5-4e7b-87ed-b05fab6b5acb"
+ },
+ "source": [
+ "with torch.no_grad():\n",
+ " print(d[agent.net(torch.tensor([3,3]).reshape(-1,2).float().to(\"cuda\"),t=tau_cvar,mean=True)[0].argmax().cpu().item()])"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "D\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "A0h28xVJ4cUb",
+ "outputId": "2cc00f80-f0d4-4367-ef84-3d100afc5ada"
+ },
+ "source": [
+ "tau_cvar"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "tensor([[[0.0149],\n",
+ " [0.1605],\n",
+ " [0.1801],\n",
+ " [0.0834],\n",
+ " [0.0750],\n",
+ " [0.0306],\n",
+ " [0.1878],\n",
+ " [0.0263]]], device='cuda:0', grad_fn=<MulBackward0>)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 92
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 550
+ },
+ "id": "DNF9ik-kySUG",
+ "outputId": "4fea1bc4-1fc5-44ed-9374-ca7969286164"
+ },
+ "source": [
+ "a=0.4\n",
+ "tau_cvar=torch.rand([1,8],device=device,requires_grad=True).reshape(1,8,1)*a #1,8,1\n",
+ "cvar_pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " cvar_pol[i,j]=d[agent.net(torch.tensor([i,j]).reshape(-1,2).float().to(\"cuda\"),t=tau_cvar,mean=True)[0].argmax().cpu().item()]\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:11]=1\n",
+ "grid[3,env.reward_region+1:]=-1\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('CVaR Policy at each step')\n",
+ "plt.imshow(grid)\n",
+ "\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, cvar_pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f) \n",
+ "print(tau_cvar.flatten())\n",
+ "print(np.all(cvar_pol==pol))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([0.1387, 0.2073, 0.3066, 0.2987, 0.1864, 0.0493, 0.0736, 0.1084],\n",
+ " device='cuda:0', grad_fn=<ViewBackward>)\n",
+ "True\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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kb5h8afS5JM+YcT0sYRKuPTbJL8+6Fnasuz9aVW9Ocn6SjUk+keT02Va167gNIAAAAAzAKQAAAAAwAAEAAAAADEAAAAAAAAMQAAAAAMAABAAAAAAwAAEAAAAADEAAAAAAAAMQAAAAAMAA/n/62jSr/uOhiwAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "eQm9vbb5_E6R",
+ "outputId": "cdaa4e35-fbcc-4863-e931-507a07a745d6"
+ },
+ "source": [
+ "plt.figure(figsize=[22,5])\n",
+ "for i in range(4):\n",
+ " plt.subplot(1,4,i+1)\n",
+ " title= 'Action: '+str(d[i])+' - Mean: '+str(mean[i])\n",
+ " plt.title(title)\n",
+ " plt.plot(atoms[:,i],tau.detach().cpu().numpy().squeeze())\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1584x360 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "jNSEmR8r3J6A",
+ "outputId": "cb958090-e9eb-4e50-e2f9-cf1abaf0863a"
+ },
+ "source": [
+ "plt.figure(figsize=[22,5])\n",
+ "for i in range(4):\n",
+ " plt.subplot(1,4,i+1)\n",
+ " title= 'Action: '+str(d[i])+' - Mean: '+str(mean[i])\n",
+ " plt.title(title)\n",
+ " p=torch.autograd.grad(out[0].squeeze()[:,i].mean(),tau,retain_graph=True)[0].squeeze()\n",
+ " plt.plot(out[0].detach().squeeze()[:,i].cpu(),1/p.cpu())\n",
+ " #plt.ylim((0,0.5))\n",
+ "plt.show()\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1584x360 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aluzl3M65peN"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/QRDQN b/code/QRDQN
new file mode 100644
index 0000000..724a184
--- /dev/null
+++ b/code/QRDQN
@@ -0,0 +1,603 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.6"
+ },
+ "colab": {
+ "name": "Copy of qr-dqn-solution-cool.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "INP8kR50Vn0n"
+ },
+ "source": [
+ "# Distributional Reinforcement Learning with Quantile Regression"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gbXVaUDgVn0q",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c32b92b1-1809-4d47-e11b-3907bb08b9c3"
+ },
+ "source": [
+ "!wget https://raw.githubusercontent.com/senya-ashukha/quantile-regression-dqn-pytorch/master/logger.py\n",
+ "!wget https://raw.githubusercontent.com/senya-ashukha/quantile-regression-dqn-pytorch/master/rl_utils.py "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "--2021-07-28 11:29:49-- https://raw.githubusercontent.com/senya-ashukha/quantile-regression-dqn-pytorch/master/logger.py\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 2770 (2.7K) [text/plain]\n",
+ "Saving to: ‘logger.py’\n",
+ "\n",
+ "\rlogger.py 0%[ ] 0 --.-KB/s \rlogger.py 100%[===================>] 2.71K --.-KB/s in 0s \n",
+ "\n",
+ "2021-07-28 11:29:49 (36.2 MB/s) - ‘logger.py’ saved [2770/2770]\n",
+ "\n",
+ "--2021-07-28 11:29:49-- https://raw.githubusercontent.com/senya-ashukha/quantile-regression-dqn-pytorch/master/rl_utils.py\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 1162 (1.1K) [text/plain]\n",
+ "Saving to: ‘rl_utils.py’\n",
+ "\n",
+ "rl_utils.py 100%[===================>] 1.13K --.-KB/s in 0s \n",
+ "\n",
+ "2021-07-28 11:29:49 (24.5 MB/s) - ‘rl_utils.py’ saved [1162/1162]\n",
+ "\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": false,
+ "id": "aAxRIFcsVn0r"
+ },
+ "source": [
+ "import gym\n",
+ "import torch\n",
+ "import pickle\n",
+ "import random\n",
+ "import numpy as np\n",
+ "import torch.nn as nn\n",
+ "import torch.optim as optim\n",
+ "\n",
+ "from logger import Logger\n",
+ "from rl_utils import ReplayMemory, huber"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DSqaDyPyVn0r"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(self, len_state, num_quant, num_actions):\n",
+ " nn.Module.__init__(self)\n",
+ " \n",
+ " self.num_quant = num_quant\n",
+ " self.num_actions = num_actions\n",
+ " \n",
+ " self.layer1 = nn.Linear(len_state, 300)\n",
+ " self.layer2 = nn.Linear(300, num_actions*num_quant) \n",
+ "\n",
+ " def forward(self, x):\n",
+ " x = self.layer1(x)\n",
+ " x = torch.tanh(x)\n",
+ " x = self.layer2(x)\n",
+ " return x.view(-1, self.num_actions, self.num_quant)\n",
+ " \n",
+ " def select_action(self, state, eps):\n",
+ " if not isinstance(state, torch.Tensor): \n",
+ " state = torch.Tensor([state]) \n",
+ " action = torch.randint(0, 2, (1,))\n",
+ " if random.random() > eps:\n",
+ " action = self.forward(state).mean(2).max(1)[1]\n",
+ " return int(action)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ps9bEIMSVn0s"
+ },
+ "source": [
+ "eps_start, eps_end, eps_dec = 0.9, 0.1, 500 \n",
+ "eps = lambda steps: eps_end + (eps_start - eps_end) * np.exp(-1. * steps / eps_dec)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BNl79ERyVn0s"
+ },
+ "source": [
+ "#env_name = 'MountainCar-v0'\n",
+ "#env = gym.make(env_name)\n",
+ "env = cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "memory = ReplayMemory(10000)\n",
+ "logger = Logger('q-net', fmt={'loss': '.5f'})"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GAkztIrTVn0t"
+ },
+ "source": [
+ "Z = Network(len_state=len(env.reset()), num_quant=32, num_actions=env.action_space.n)\n",
+ "Ztgt = Network(len_state=len(env.reset()), num_quant=32, num_actions=env.action_space.n)\n",
+ "optimizer = optim.Adam(Z.parameters(), 1e-3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "nMQoe3z1Vn0u"
+ },
+ "source": [
+ "steps_done = 0\n",
+ "running_reward = None\n",
+ "gamma, batch_size = 0.99, 32 \n",
+ "tau = torch.Tensor((2 * np.arange(Z.num_quant) + 1) / (2.0 * Z.num_quant)).view(1, -1)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "DSjSJMbkVn0u",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ba4e1a53-ad10-459e-df04-e8620446e070"
+ },
+ "source": [
+ "for episode in range(2001): \n",
+ " sum_reward = 0\n",
+ " state = env.reset()\n",
+ " while True:\n",
+ " steps_done += 1\n",
+ " \n",
+ " action = Z.select_action(torch.Tensor([state]), eps(steps_done))\n",
+ " next_state, reward, done, _ = env.step(action)\n",
+ "\n",
+ " memory.push(state, action, next_state, reward, float(done))\n",
+ " sum_reward += reward\n",
+ " \n",
+ " if len(memory) < batch_size: break \n",
+ " states, actions, rewards, next_states, dones = memory.sample(batch_size)\n",
+ " \n",
+ " theta = Z(states)[np.arange(batch_size), actions]\n",
+ " \n",
+ " Znext = Ztgt(next_states).detach()\n",
+ " Znext_max = Znext[np.arange(batch_size), Znext.mean(2).max(1)[1]]\n",
+ " Ttheta = rewards + gamma * (1 - dones) * Znext_max\n",
+ " \n",
+ " diff = Ttheta.t().unsqueeze(-1) - theta \n",
+ " loss = huber(diff) * (tau - (diff.detach() < 0).float()).abs()\n",
+ " loss = loss.mean()\n",
+ "\n",
+ " optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " optimizer.step()\n",
+ " state = next_state\n",
+ " \n",
+ " if steps_done % 100 == 0:\n",
+ " Ztgt.load_state_dict(Z.state_dict())\n",
+ " \n",
+ " if done and episode % 50 == 0:\n",
+ " logger.add(episode, steps=steps_done, running_reward=running_reward, loss=loss.data.numpy())\n",
+ " logger.iter_info()\n",
+ " \n",
+ " if done: \n",
+ " running_reward = sum_reward if not running_reward else 0.2 * sum_reward + running_reward*0.8\n",
+ " break"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ " step steps running_reward loss\n",
+ "------ ------- ---------------- -------\n",
+ " 50 1749 -1.3 0.00693\n",
+ " 100 2697 0.8 0.00094\n",
+ " 150 4779 0.1 0.00347\n",
+ " 200 11084 -0.1 0.00044\n",
+ " 250 12282 -0.3 0.00069\n",
+ " 300 16479 -1.0 0.00039\n",
+ " 350 17677 0.7 0.00050\n",
+ " 400 20676 -1.6 0.00076\n",
+ " 450 22967 -0.0 0.00199\n",
+ " 500 25143 0.0 0.00217\n",
+ " 550 27006 -0.7 0.00104\n",
+ " 600 28588 -1.0 0.00045\n",
+ " 650 29658 -1.0 0.00077\n",
+ " 700 31441 0.2 0.00037\n",
+ " 750 33751 0.2 0.00095\n",
+ " 800 34845 -0.6 0.00128\n",
+ " 850 36200 0.7 0.00332\n",
+ " 900 37566 0.2 0.00368\n",
+ " 950 38898 0.3 0.00577\n",
+ " 1000 40658 0.2 0.00088\n",
+ " 1050 41615 0.5 0.00050\n",
+ " 1100 44883 -1.0 0.00084\n",
+ " 1150 46616 0.6 0.00031\n",
+ " 1200 48106 0.5 0.00062\n",
+ " 1250 48958 0.8 0.00022\n",
+ " 1300 50016 0.7 0.00022\n",
+ " 1350 51093 0.3 0.00280\n",
+ " 1400 52375 0.7 0.00024\n",
+ " 1450 53602 0.8 0.00034\n",
+ " 1500 54705 0.3 0.00035\n",
+ " 1550 56252 0.3 0.00071\n",
+ " 1600 57368 0.7 0.00038\n",
+ " 1650 58487 0.6 0.00060\n",
+ " 1700 59669 0.6 0.00086\n",
+ " 1750 61045 0.6 0.00026\n",
+ " 1800 62138 -0.1 0.00029\n",
+ " 1850 63245 0.7 0.00212\n",
+ " 1900 64232 -0.5 0.00100\n",
+ " 1950 65464 0.6 0.00195\n",
+ " 2000 67474 0.6 0.00046\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e2L1HU3DVn0u"
+ },
+ "source": [
+ "# Vizualization"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "R72nzEFwVn0v"
+ },
+ "source": [
+ "import time\n",
+ "import matplotlib.pyplot as plt\n",
+ "from IPython import display\n",
+ "%matplotlib inline\n",
+ "\n",
+ "from matplotlib import rcParams\n",
+ "rcParams['figure.figsize'] = 8, 3\n",
+ "rcParams['figure.dpi'] = 300"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Qt21GzMxVn0v"
+ },
+ "source": [
+ "actions={\n",
+ " 'CartPole-v0': ['Left', 'Right'],\n",
+ " 'MountainCar-v0': ['Left', 'Non', 'Right'],\n",
+ "}\n",
+ "\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MYMHXOAfbXrW"
+ },
+ "source": [
+ "sup=torch.linspace(0,1,32)\n",
+ "for i in range(4):\n",
+ " plt.plot(Zval[0][i],sup)\n",
+ "print(Zval[0][1].mean())"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dUH7LyO9XJAV"
+ },
+ "source": [
+ "**Graph used in the report down is in next cell**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "VeItim0ZaKGh",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 588
+ },
+ "outputId": "d51e4e96-545f-4e24-e7f9-043ac477b465"
+ },
+ "source": [
+ "Zval = Z(torch.Tensor([2,3])).detach().numpy()\n",
+ "plt.title('Quantile function at state $[2,3]$')\n",
+ "for i in range(env.action_space.n):\n",
+ " \n",
+ " x, y = get_plot(Zval[0][i])\n",
+ " plt.plot(x, y, label='%s Q=%.1f' % (d[i], Zval[0][i].mean()))\n",
+ " plt.legend( ncol=4, prop={'size': 6},loc='lower right')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
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uO5P8fK31opEHDAAwwSTfAQAYpg3pvlbqN4fRWa11Js0opE7HDaO/Qam13tnjfnvSJAFvmVN8cJKf7iOcV9Zar1jktudn72P5jD76nhSvyv3/v9S3kjxzKcex1nptkpd2FJ85DusCT7BRnbuv7lL2B7XWCxa5//9Ta91Rax2LpRTmMzsN8X/pUvXCWut1i2mj1npu9n7IYDp7P8SwWBNznxqz+/tIlVIOSbK2o/jPaq11qW3NJoGHsVzNvvpdDsfPd1b//iB7J6ZvT/K6FmLp1aPSPER13+tRSR6U5JB5tr8yyRNrre8aTXgAAMuH5DsAAMN05DzlPf2YvUjd2u788X/ZmB31+YGO4h/ssbmvJHnzEvreleSvOoqPK6Ws67H/sVdKeUT2XjP3t2qtd3Tbfh/el2at7Lme21NgjOTcLaX8UJIndRRfn+R3Ftv3BHpemjW457q41vrhJbbzX9JMAT/XL84m95div7lPDfj+3oZuSb1uD8gtS+Nw/Hxn9a+U8rwkv9Kl6hWLncFhwlyX5Cm11h+utX627WAAACaR5DsAAMN04Dzlvfzou1jd2p4vjuWi88fwH+ixnXfOjtZbin/tUnZyj/1PgrM63t+dvRN7izI7+vOSjuLTe2mLkZ27P9al7A8nZdrhHp3RpexPl9pIrfXLSS7rKD4qyeOW2NT+dp8a1P29Dd9NsyTDXJP08MAgtH38fGf1oZRySpI/71L1N7XWzmUFlotHJbmqlPL+/WhmAwCAgZpqOwAAABiwbtPZdpv6fmyVUo5K8kNJHpnkoUmOSDOC8OB0/yydMwwc32PXH+lhny93KTusx/4nwWkd7z8z35rTi/TVjveP7aOt/dmozt3TO97vSvKXPfQ9SU7teH9Pel+n/v3ZexTuqUn+ZQltTPR9qsX7+8jVWneUUq5LM8X1fV5RSvm3WusH24qrHxN4/Hxn9aiU8sAkf5/koI6qLyT5udFH1J9a65q570spU0kOT7NE1OOTPD/Nw1YlycrZ9z9eSnlNrfW/jzhcAICJJvkOAMAwbZ+n/LAk3x5Sn4d3Kbt7SH0NVCnlh9NMbfqMJEudinmuqVLKmlrr1iXu1zlCbzG6TfO/LJPvpZSV2XvU4qNKKf1My9qZmDmslHLA7FTZLN7Qz91SynT2TjR9tta6rYe+J0Ip5eDsvc7xv9VaO6ePX6xrupQtdeT7RN6nxuD+3pZ3JXnDnPerk/xNKeWTSd6d5O9rrd9oJbIlmMTj5zurd6WUDUn+T5JjOqpuSfIjtdaJ+HflQmZnbNky+7o2yTtKKY9JcmH+/wMzU0l+v5RyVK31V1sJFABgAkm+AwAwTN+dp/zwjDb5fteQ+hqIUsqhSd6e5CcH2OxhSZb64/58x2sh3X5w7ycxMc6OSpM4muuI2dcgHZlk04DbXO5Gce6uTTMacK5/76HfSXJU9h7N+/k+2vtCkj25/xJ4a5fYxkTdp8bo/t6WtyZ5QZIndJT/wOzrLaWUm5J8NMmnZv973ewU562b8OPnO6sHpZR1aRLvJ3ZU3ZbkabXWr40+qtGotX62lPKDaT7/E+dUvbyU8ola6wdaCg0AYKJY8x0AgGH6VrpPA3/sMDorpaxK82Nzp9uG0d8gzP6wf1kG+8N+0kNiabmNXBuCbufWMBw4on6WjRGdu50jPpPk9hH026ZuSbo7em1sdq32zoehuv1dF2pjYu5T43R/b0utdSbJs9Ik8+ZzUpIXJnlTks8m2VJK+YtSyjNLKa39brUMjp/vrCWaXVbgiiQP6ajakibx/sXRRzVas7My/FT2nr3q/Nmp6gEA2AfJdwAAhmb2R/ebu1R9/5C6fFS6/6h9/ZD6G4Q/zN7TwibN1Mp/lOZH/yekeWDhsCTTtdYy95UJXHt0Qg16tCCT5dAuZZMy+rhXh3Qpu6fPNjv379bHcuH+nqTWuiXJ09Mk9D69iF2OTPIfk/xDkhtKKc8bYngLmfTj5ztrCUopRyS5PMkjOqq+mybxPs7/lhyo2dH9f9FRfEKaZRcAANgHTywCADBsn8neU3c+fkh9dWv3jnFdT7aU8sgk/7mjeGuSlyR53xKm3V02o87GXOcosCT5q1rrfxh5JLSh2/IVa0YexWh1W9f44D7b7Nx/4tdO7sb9/f5mP+/7k7y/lPLQJM9MclqSJ2fhEdonJ/nrUspbk7x0VNPRL5Pj5ztrkUoph6VJvD+2o+qOJE+vtV47+qhad0mSX+woOz3NQzEAACzAyHcAAIbtqi5lT52dIn7Quo3I+ach9NNNL9PI/lT2Xk/57Frre5eYYFjStM30bEuXMn/7/kzM9NlJvtOlbLmPLO02rf7hvTY2O4V45wwCvazhPgnc3+dRa/18rfWCWuuP1VrXJvm+JD+fZqRtt/ts0iS9f2tUMWZ5HD/fWYswu7zAPyY5paPqriTPqLUuZraG5ejmLmUPHHUQAACTSPIdAIBh+/vsve772iTPGWQnpZRj0qwr2+lD+9i1c/3gXpOBvaytekbH++trrR/soR0/ho7G5ux9Lp/QRiBjoM3rpi1bkuzuKHtUG4GM0Jbsfc4/tI/2Ts7ev0PMl2yddO7vi1Rr/VKt9Z211p9NckySZye5psumv1VKWTuisJbD8fOdtQ+llEOSXJZm+YC57k6TeP/X0Uc1NnZ0KVvOy4QAAAyM5DsAAEM1u27k1V2qfmnAXb0oey+rNJPk7/axX+dU0t3WdV6Mk3rY5/iO9//cY99P6nE/lqDWuiNJ59Sz31dKWd9GPC1r87ppRa11Z5plNOZ6TCml32nYx1atdVuSGzuKH1NKWdljk92WBlmuo0rd33tQa7231vr3aaajv6Sj+qAkPzKiUCb++PnOWlgpZU2SS5P8QEfV1iRn1lo/Ofqoxsq6LmXL9WEpAICBknwHAGAU3tilbGMp5ScH0Xgp5YQkv9Gl6sJaa7epoue6o+P9A0opnVPNLsZpPezTOYJvydMvz65L289I1LZ0jiBOkl4TeqN0eZeynxh5FO1r87pp09Ud76eSDHP95HG4Tj7e8X5Nek+APn8R7S8Xbd/fx+Hc6VmtdXeS3+xStdBsE4P8zG0ev0F+Dt9ZXcw+NHVxmoc85ronyTNrrR8bfVRjp/OhhCS5beRRAABMIMl3AACGrtZ6UZJuU3f+cb9TyM6uIfy2JJ2jT3cned0imugc1bkmycOWGMPG9DaC956O9738LX61h33Gwd1dytaMPIqlu6hL2StKKZ2zLix3bV43bfrbLmUvH+LxH4fr5LIuZS9ZaiOllBOzd9J+S/aeTWC5aPv+Pg7nTr++0KXssAW2H+RnbvP4DfJz+M7qUEo5KM2SSD/UUbUtybNqrb3OcrBszD5M1+3Bsk+MOhYAgEkk+Q4AwKi8OHuP5lqf5PJSypG9NDj74+D/SvKMLtWvrbV+dRHNdJvy+AVLiOGAJK9d7PYdbu14/7TZhwkW2/fTkpzdY99tu71L2divbTw7Gu7qjuIHJrlg9NG0qs3rpjWz0xD/U0fxw5P87pC6HIfr5G+z973qR0spZy6xnT/J3kuDvK3WuqvnyMZb2/f3cTh3+vU9Xco2L7D9ID9zm8dvYJ/Dd9b9lVJWp3kg4fSOqu1JfrTW+pGRBzWeXpTksR1lM9l7KQgAALqQfAcAYCRqrZ9N96nhH5PkylLKyUtpr5RyaJJ3J/n5LtWfTXLeIpv6eJI7O8p+eXYq+33FsCLJm5I8YZF9deocXfXALHJEaSnlcUn+d5JepvoeB5/rUvbMkUfRm1clqR1lv1xK+d0ep15PKeURpZQ/L6Uc0X94I9HmddO2bveW3yylLHmUayllupRyzAKbtH6dzCbH/6RL1Z+XUhY120Ep5bwkz+oo3pHkT/sMb5y1fX9v/dwppTy5lPKyUsohPTbxsi5lnWuYzzXIz9zm8Rv0sfOdlaSUsirJB5M8raNqe5Jn11qvGkEMDyil1M7XANt/bCnllbOj+3tt4/lJ3tyl6p211q29RwcAsP+QfAcAYGRqrRckeXuXqkcnua6U8nullPULtVFKWV1K+Zkkn0/yn7psckuS5y52NGWtdXuS93UUr0nyfxZKLJVSTkry4SS/OFu0YzH9dXh/l7I3llJeOt8P4qWUlaWUc5Jclf8/De5dPfTdqlrrd7P3lMI/V0p5eSlloWmFWzc7krDbSOdXp3mQpHMq265KKUeVUl5USrk8yXVpzueJWJO55eumVbXWK5L8YUdxSXJBKeWvSynft682SiknllLOTXJzFlg/fYyukwvSPNQ019okV5dSfmq+nUoph5dS3pHkt7tU/7da67cGGOO4afX+Pibnzrokb0jyjVLKW0spT5+d9WJBpZQ1pZTXJfmVjqo700wX3tWAP3Nrx2/Qx853VjI7zf77k3TO2LEjzb8Zrxh9VENxWJLXJ/lqKeX8Usr3L3bH2Qcq3pfm79R5nW5K8luDCxMAYHnbb9d4AgCgNfdNP/9LHeWr0ozO+q1SykeTfDTNtK/fTnJomulnH5nmh9P51j69NcnTaq1fW2JMr03yH5PMHZ13UpJrSykfTjNl621JVifZkGTj7Ou+f09vSfLGJL+3lE5rrVeUUv4pyVPmFE+lGXH0slLK3ya5Ic2orKOTPCLJc5IcO2f7TWkSY69fSt9j4p25f9wr0yQ1LyilfCNNouXejn3eWmt964jiW8h5SR6SvddEPT3JP5VSvpjmvLk+yXfTTNd6eJIj0qyNfkqSh2ZCEhfzaOW6GRP/Lc2UvBs7yp+X5MdLKZ9KckWSr6U5/quTHJnmGn58mgeOFqv166TWurOU8oIk/5r733+PTvKXpZTXJPlQkpvSrFe9PskTk/xY7n9+3Ocf0n00/bIxJvf31s+dWYem+e5/cZK7SymfSfJvSb6a5I4066uvTnJckselWUqmW5L5FbMP/ixkIJ95DI7foI/d/v6d9VNpjk+nHUleX0rp599Qn6q1vqiP/YdhXZJXJHlFKWVTks+keYDq1jTX3LY09/Ij0py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+ "text/plain": [
+ "<Figure size 2400x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kmC_hSI3hqnZ",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 546
+ },
+ "outputId": "625789a3-4b84-4612-cc1e-4af290a1a937"
+ },
+ "source": [
+ "x, y = get_plot(Zval[0][3])\n",
+ "sup=np.linspace(min(x),max(x),51)\n",
+ "out=[]\n",
+ "d=np.diff(y)\n",
+ "for i in sup:\n",
+ " for j in range(len(x)):\n",
+ " if i <= x[j]:\n",
+ " out.append(y[j])\n",
+ " break\n",
+ "plt.plot(sup,out)\n",
+ "plt.plot(x,y) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2400x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "QulTY9BVkTmV",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 556
+ },
+ "outputId": "c082d4d5-10ca-45bc-8b50-6ea3a6f4a326"
+ },
+ "source": [
+ "plt.plot(sup[1:],np.diff(out))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "[<matplotlib.lines.Line2D at 0x7f0074143550>]"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 35
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2400x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jwWVTIYPVn0v"
+ },
+ "source": [
+ "def get_plot(q):\n",
+ " eps, p = 1e-8, 0\n",
+ " x, y = [q[0]-np.abs(q[0]*0.2)], [0]\n",
+ " for i in range(0, len(q)):\n",
+ " x += [q[i]-eps, q[i]]\n",
+ " y += [p, p+1/len(q)]\n",
+ " p += 1/len(q)\n",
+ " x+=[q[i]+eps, q[i]+np.abs(q[i]*0.2)]\n",
+ " y+=[1.0, 1.0]\n",
+ " return x, y"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "iT15jXtqVn0x"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "TJU-7tbSZlen"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/Risky_MDP.ipynb b/code/Risky_MDP.ipynb
new file mode 100644
index 0000000..80d0496
--- /dev/null
+++ b/code/Risky_MDP.ipynb
@@ -0,0 +1,926 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "Risky_MDP.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.10"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "from gym import spaces\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NmvdzO0Rmab1"
+ },
+ "source": [
+ "## Environments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sVrs7hkH_ioo"
+ },
+ "source": [
+ "class mdp():\n",
+ " \n",
+ " def __init__(self):\n",
+ " self.pos=0\n",
+ " self.action_space = spaces.Discrete(2)\n",
+ " self.observation_space = spaces.Discrete(2)\n",
+ " self.d={-1:-1,\n",
+ " -2:1,\n",
+ " 0:0,\n",
+ " 1:-0.1,\n",
+ " 2:-0.1,\n",
+ " 3:1,\n",
+ " }\n",
+ " def reset(self):\n",
+ " self.pos=0\n",
+ " return self.pos\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " if self.pos==0:\n",
+ " if a==0:\n",
+ " isdone=True\n",
+ " if np.random.random()<0.1:\n",
+ " self.pos=-1\n",
+ " else:\n",
+ " self.pos=-2\n",
+ " elif a==1:\n",
+ " isdone=False\n",
+ " self.pos=1\n",
+ " elif self.pos==1:\n",
+ " if a==0:\n",
+ " isdone=True\n",
+ " self.pos=2\n",
+ " elif a==1:\n",
+ " isdone=True\n",
+ " if np.random.random()<0.2:\n",
+ " self.pos=2\n",
+ " else:\n",
+ " self.pos=3\n",
+ " reward=self.d[self.pos]\n",
+ " return self.pos,reward, isdone, None\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " np.random.seed(seed)\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SBqyrMIfnV2h"
+ },
+ "source": [
+ "e=mdp()\n",
+ "t=[]\n",
+ "for i in range(100000):\n",
+ " e.reset()\n",
+ " n,r,d,_=e.step(1)\n",
+ " n,r,d,_=e.step(1)\n",
+ " t.append(r)\n",
+ "print(np.mean(t))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IPE7fSIinV2i",
+ "outputId": "51ef4262-4d09-47b9-c793-c1a27a146109"
+ },
+ "source": [
+ "np.bincount(t)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([20197, 79803])"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n",
+ "\n",
+ "Please see *01.dqn.ipynb* for detailed description."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 150), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(150, 150), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(150, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.99,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 2,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim =1# env.observation_space.shape[0]\n",
+ " action_dim = 2\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ " \n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ " \n",
+ " def risk_action(self,state,alpha=1):\n",
+ " selected_action = self.dqn.risk_action(\n",
+ " torch.FloatTensor(state).to(self.device),alpha=alpha,\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ "\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 500):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " \n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ " #if reward>=0:\n",
+ " #self.epsilon=max(self.min_epsilon,self.epsilon*0.993)\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ "\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JB9OrdL-vXOu"
+ },
+ "source": [
+ "Creating reference policy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ag7Mail7nV2p",
+ "outputId": "5f488443-0adc-4f96-b568-08640242256a"
+ },
+ "source": [
+ "#x=[]\n",
+ "#for _ in range(100):\n",
+ "env.reset()\n",
+ "env.step(1)\n",
+ "env.step(0)\n",
+ " #x.append(r)\n",
+ "#print(np.mean(x))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(2, 0, True, None)"
+ ]
+ },
+ "execution_count": 122,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zcwOjstJqeEs",
+ "outputId": "672a106b-bd4b-4381-c6a5-e14bd66212a3"
+ },
+ "source": [
+ "seed = 77\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "env=mdp()\n",
+ "env.seed(seed)\n",
+ "# parameters\n",
+ "num_frames = 50000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 45000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay, max_epsilon=0.9)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "mC5KKHXLHvmo",
+ "outputId": "60ddf96c-56a4-484d-cc19-aced9726dbd8"
+ },
+ "source": [
+ "agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IaA6t5-JnV2s"
+ },
+ "source": [
+ "torch.save(agent.dqn.state_dict(),'mdp.pt')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "scrolled": true,
+ "id": "wVXUrmevnV2s",
+ "outputId": "3b79502a-524f-424c-da78-3313dd43d9a3"
+ },
+ "source": [
+ "print(agent.greedy_action([1]).item(),agent.greedy_action([0]).item())\n",
+ "print(agent.risk_action([1],alpha=5).item(),agent.risk_action([0],alpha=5).item())\n"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1 0\n",
+ "0 1\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 387
+ },
+ "id": "jEJ-blyFtVIJ",
+ "scrolled": true,
+ "outputId": "33db3c24-d3f6-45c8-aa7b-e5f9c40d36cb"
+ },
+ "source": [
+ "plt.figure(figsize=[20,5])\n",
+ "pos=torch.tensor([0])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(2):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(i))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "print(m-1*np.sqrt(variance))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [0.79517436 0.5891807 ]\n",
+ "Var Q: [0.46573147 0.28583977]\n",
+ "Entropy: [1.3481475 1.7872149]\n",
+ "[0.11272913 0.05454087]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MqtIzQwsnV2u",
+ "outputId": "aa745c85-f60b-40a5-bb98-fd5d6aaefdf1"
+ },
+ "source": [
+ "pos=torch.tensor([1])\n",
+ "agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([-0.09971979, 0.83900476], dtype=float32)"
+ ]
+ },
+ "execution_count": 176,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Un3hSKASnV2u",
+ "outputId": "4713f1c2-e6f8-423b-e4fa-802feaf6e6d7"
+ },
+ "source": [
+ "plt.figure(figsize=[20,5])\n",
+ "pos=torch.tensor([1])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(2):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(i))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "print(m-5*np.sqrt(variance))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [-0.09971979 0.83900476]\n",
+ "Var Q: [0.07242161 0.25626782]\n",
+ "Entropy: [0.90028864 1.4133334 ]\n",
+ "[-1.4452829 -1.6921403]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/blackjack.ipynb b/code/blackjack.ipynb
new file mode 100644
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+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "c51_blackjack.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ax_tMYQiHvmT"
+ },
+ "source": [
+ "## Configurations for Colab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBE-ev78Hvmi"
+ },
+ "source": [
+ "## Network\n",
+ "\n",
+ "The parametrized distribution can be represented by a neural network, as in DQN, but with atom_size x out_dim outputs. A softmax is applied independently for each action dimension of the output to ensure that the distribution for each action is appropriately normalized.\n",
+ "\n",
+ "To estimate q-values, we use inner product of each action's softmax distribution and support which is the set of atoms $\\{z_i = V_{min} + i\\Delta z: 0 \\le i < N\\}, \\Delta z = \\frac{V_{max} - V_{min}}{N-1}$.\n",
+ "\n",
+ "$$\n",
+ "Q(s_t, a_t) = \\sum_i z_i p_i(s_t, a_t), \\\\\n",
+ "\\text{where } p_i \\text{ is the probability of } z_i \\text{ (the output of softmax)}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 300), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300, 300), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(300, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ "\n",
+ " def u_mean(self, x: torch.Tensor,f) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * f(self.support), dim=2)\n",
+ " return q \n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def skew_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " skew=self.skew(dist,q)\n",
+ " return q-alpha*skew \n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ "\n",
+ " def skew(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**3\n",
+ " skew=torch.sum(dist * cm.T,dim=-1)\n",
+ " return skew**(1/3)\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.99,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 2,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim = 3#env.observation_space.shape[0]\n",
+ " action_dim = env.action_space.n\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ "\n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action\n",
+ "\n",
+ " def u_action(self,state,f):\n",
+ " selected_action = self.dqn.u_mean(\n",
+ " torch.FloatTensor(state).to(self.device),f=f\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ "\n",
+ " def skew_action(self,state,alpha=1):\n",
+ " selected_action = self.dqn.skew_action(\n",
+ " torch.FloatTensor(state).to(self.device),alpha=alpha\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ "\n",
+ " def risk_action(self,state,alpha=1):\n",
+ " selected_action = self.dqn.risk_action(\n",
+ " torch.FloatTensor(state).to(self.device),alpha=alpha\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ "\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 200):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " \n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ " #if reward>=0:\n",
+ " #self.epsilon=max(self.min_epsilon,self.epsilon*0.993)\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " def test_(self,printout=False):\n",
+ " self.is_test=True\n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ "\n",
+ " while not done:\n",
+ "\n",
+ " action = self.greedy_action(state)\n",
+ " if printout:\n",
+ " print(state,action)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " self.is_test=False\n",
+ " return score \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ "\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EMEkNhyJHvmk"
+ },
+ "source": [
+ "## Environment\n",
+ "\n",
+ "You can see the [code](https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py) and [configurations](https://github.com/openai/gym/blob/master/gym/envs/__init__.py#L53) of CartPole-v0 from OpenAI's repository."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eTGBnLnsO_Bs",
+ "outputId": "02cf491a-f07f-44e9-886a-9e8e98b7b411"
+ },
+ "source": [
+ "!pip install gym_gridworlds\n",
+ "\n",
+ "import gym_gridworlds\n",
+ "env = gym.make('Blackjack-v0')"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Collecting gym_gridworlds\n",
+ " Downloading gym_gridworlds-0.0.2-py3-none-any.whl (5.9 kB)\n",
+ "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from gym_gridworlds) (1.19.5)\n",
+ "Requirement already satisfied: gym in /usr/local/lib/python3.7/dist-packages (from gym_gridworlds) (0.17.3)\n",
+ "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.7/dist-packages (from gym->gym_gridworlds) (1.5.0)\n",
+ "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from gym->gym_gridworlds) (1.4.1)\n",
+ "Requirement already satisfied: cloudpickle<1.7.0,>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from gym->gym_gridworlds) (1.3.0)\n",
+ "Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym->gym_gridworlds) (0.16.0)\n",
+ "Installing collected packages: gym-gridworlds\n",
+ "Successfully installed gym-gridworlds-0.0.2\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jsi7CspeHvml"
+ },
+ "source": [
+ "## Set random seed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aChSfk4uHvmn",
+ "outputId": "b59c2544-0146-49d4-b188-6cfab55167ad"
+ },
+ "source": [
+ "# parameters\n",
+ "num_frames = 150000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 300\n",
+ "epsilon_decay = 1 / 140000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "YhdGKDorvCyh",
+ "outputId": "5b8deaf8-66a9-4cea-c222-2b03f73339b7"
+ },
+ "source": [
+ "agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bQsZQfIfeIL1"
+ },
+ "source": [
+ "def fn(x):\n",
+ " return - torch.exp(-0.5*x)+1\n",
+ "policy=np.ndarray(shape=[17,10])\n",
+ "index=np.ndarray(shape=[17,10,3])\n",
+ "player_cards=list(reversed(range(5,22)))\n",
+ "dealer_cards=list(range(2,12))\n",
+ "policy_ra=np.ndarray(shape=[17,10])\n",
+ "for i,player_hand in enumerate(range(5,22)):\n",
+ " for j,dealer_hand in enumerate(range(2,12)):\n",
+ " policy[i,j]=agent.greedy_action(torch.tensor([player_hand,dealer_hand,False]).float())\n",
+ " policy_ra[i,j]=agent.skew_action(torch.tensor([player_hand,dealer_hand,False]).float(),alpha=5)\n",
+ " index[i,j]=np.array([player_hand,dealer_hand,False])\n",
+ "policy=np.flipud(policy)\n",
+ "policy_ra=np.flipud(policy_ra)\n",
+ "index=np.flipud(index)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wmib6JlZBxbj",
+ "outputId": "bfeff2c6-d58d-42b7-9828-4978687ddd32"
+ },
+ "source": [
+ "agent.greedy_action(torch.tensor([12,5,False]).float())"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array(1)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 13
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "Q6-DsIwdhHhT",
+ "outputId": "f6c0779d-4227-46a2-85a8-5fc3eb1b659b"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[3,6],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "ax.imshow(policy,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.xaxis.tick_top()\n",
+ "ax.set_xticks(list(range(10)))\n",
+ "ax.set_xticklabels(dealer_cards)\n",
+ "ax.set_yticks(list(range(17)))\n",
+ "ax.set_yticklabels(player_cards) \n",
+ "ax.set_title('Agent Strategy')\n",
+ "f='bold'#'extra bold'\n",
+ "for i in range(policy.shape[0]):\n",
+ " for j in range(policy.shape[1]):\n",
+ " plt.text(j, i,'H' if policy[i,j]==1 else 'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\" if policy[i,j]==1 else 'black',fontweight=f)\n",
+ " highlight_cell(j,i, color=\"gray\", linewidth=0.3)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x1800 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 634
+ },
+ "id": "jEJ-blyFtVIJ",
+ "outputId": "ffe03080-58ed-4519-c5fa-07b8270f3c9e"
+ },
+ "source": [
+ "plt.figure(figsize=[10,4],dpi=150)\n",
+ "pos=torch.tensor([12,4,False])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**3\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(2):\n",
+ " plt.subplot(1,2,i+1)\n",
+ " plt.title(str(i))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "#print('Argmax:',policy[tuple(pos)],'for pos:',str(pos.numpy()))\n",
+ "print(m-3*np.sqrt(variance))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [-0.20313776 -0.08032256]\n",
+ "Var Q: [0.43113688 0.1684665 ]\n",
+ "Entropy: [1.6901021 1.8592284]\n",
+ "[-2.1729681 -1.3116627]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 1500x600 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "DjOEtFl1Hvmq"
+ },
+ "source": [
+ "Expert policy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "-kE2Vqv-uH_Y"
+ },
+ "source": [
+ "expert=np.ones(shape=[17,10])\n",
+ "expert[0:4]=0\n",
+ "expert[4,:-1]=0\n",
+ "expert[4:9,:5]=0\n",
+ "expert[9,2:5]=0\n",
+ "#expert=np.fliplr(expert)\n",
+ "#expert=np.flipud(expert)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "57JKiF1x60qO",
+ "outputId": "e533151b-8988-4a07-819d-c7cc8fff5bc3"
+ },
+ "source": [
+ "expert"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "rvGNLXR6sLgI",
+ "outputId": "7ab834df-d562-4c53-aa2c-4216e95bf98b"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[3,6],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "ax.imshow(expert,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.xaxis.tick_top()\n",
+ "ax.set_xticks(list(range(10)))\n",
+ "ax.set_xticklabels(dealer_cards)\n",
+ "ax.set_yticks(list(range(17)))\n",
+ "ax.set_yticklabels(player_cards) \n",
+ "ax.set_title('Expert Strategy')\n",
+ "f='bold'#'extra bold'\n",
+ "for i in range(policy.shape[0]):\n",
+ " for j in range(policy.shape[1]):\n",
+ " plt.text(j, i,'H' if expert[i,j]==1 else 'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\" if expert[i,j]==1 else 'black',fontweight=f)\n",
+ " highlight_cell(j,i, color=\"gray\", linewidth=0.3)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 900x1800 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Qhpmbrnt4Egp",
+ "outputId": "49ec778a-e29d-4396-9f51-1d327c0d929e"
+ },
+ "source": [
+ "policy"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 1., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 22
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3jWZWQOAvblc",
+ "outputId": "7c93a98a-f4a0-4508-de44-fc7086db8894"
+ },
+ "source": [
+ "ra_expert=np.ones(shape=[17,10])\n",
+ "ra_expert[0:6]=0\n",
+ "ra_expert[6,:-2]=0\n",
+ "ra_expert[6:9,:7]=0\n",
+ "ra_expert[9:11,:5]=0\n",
+ "ra_expert[11,:3]=0\n",
+ "#ra_expert=np.fliplr(ra_expert)\n",
+ "ra_expert"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 21
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 792
+ },
+ "id": "YTIcFk8cwAba",
+ "outputId": "81164b42-0c62-4614-c777-43deaeb4c5a9"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[3,6],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "ax.imshow(ra_expert,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.xaxis.tick_top()\n",
+ "ax.set_xticks(list(range(10)))\n",
+ "ax.set_xticklabels(dealer_cards)\n",
+ "ax.set_yticks(list(range(17)))\n",
+ "ax.set_yticklabels(player_cards) \n",
+ "ax.set_title('Modifi Strategy')\n",
+ "f='bold'#'extra bold'\n",
+ "for i in range(policy.shape[0]):\n",
+ " for j in range(policy.shape[1]):\n",
+ " plt.text(j, i,'H' if ra_expert[i,j]==1 else 'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\" if ra_expert[i,j]==1 else 'black',fontweight=f)\n",
+ " highlight_cell(j,i, color=\"gray\", linewidth=0.3)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2250x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IrfV7Mg6tPkh"
+ },
+ "source": [
+ "class SafetyClassifier(nn.Module):\n",
+ " def __init__(\n",
+ " self, support, action_dim = 4, size = 51, t = 0.2, alpha = 2, state_dim = 0):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(SafetyClassifier, self).__init__()\n",
+ " self.action_dim=action_dim\n",
+ " self.size=size\n",
+ " self.support=support.reshape(1,1,size)\n",
+ "\n",
+ " self.fwd=nn.Sequential(\n",
+ " nn.Linear(state_dim+self.action_dim*self.size,200),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200,200),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200,1),\n",
+ " nn.Sigmoid()\n",
+ " )\n",
+ " self.ce_loss=nn.CrossEntropyLoss(reduction='none')\n",
+ " self.t=t\n",
+ " self.state_dim=state_dim\n",
+ " self.alpha=alpha\n",
+ " def adapted_ce_loss(self,pred,labels):\n",
+ " pred=pred.softmax(dim=-1)\n",
+ " pred=pred*torch.sum(labels,dim=-1,keepdim=True)**1\n",
+ " pred=torch.minimum(pred,torch.tensor([1]).to(pred.device))\n",
+ " out=-torch.log(pred)\n",
+ " out[labels==0]=float(\"inf\")\n",
+ " return out.min(dim=-1)[0] \n",
+ " \n",
+ "\n",
+ " def cat_CE_loss(self,pred,true,adapted=False):\n",
+ " if adapted:\n",
+ " return self.adapted_ce_loss(pred,true)\n",
+ " else:\n",
+ " return self.ce_loss(pred,true)\n",
+ " def normalise(self,x):\n",
+ " #expect x of N by 4\n",
+ " a=(x-x.min(dim=-1)[0].reshape(-1,1))\n",
+ " a=a/torch.std(x,dim=-1).reshape(-1,1)\n",
+ " return (a**self.alpha) \n",
+ "\n",
+ " def get_error(self,dist,labels,adapted=False):\n",
+ "\n",
+ " mean = torch.sum(dist * self.support, dim=2)\n",
+ " \n",
+ " normalised=self.normalise(mean)\n",
+ " \n",
+ " loss=self.cat_CE_loss(normalised,labels,adapted=adapted)\n",
+ " \n",
+ " return loss#(loss>self.t).float()\n",
+ " def compute_target(self,dist,labels,adapted=False):\n",
+ " error=self.get_error(dist,labels,adapted=adapted)\n",
+ " return (error>self.t).float()\n",
+ " def forward(self,dist,state=None): #x is N x action_dim x size\n",
+ " if dist is not None:\n",
+ "\n",
+ " dist=dist.reshape(-1,self.action_dim*self.size)\n",
+ " if state is not None:\n",
+ " dist=torch.cat([dist,state],axis=1)\n",
+ " out=self.fwd(dist)\n",
+ " else:\n",
+ " out=self.fwd(state)\n",
+ "\n",
+ " return out\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zFaWNqfLimNv",
+ "outputId": "92120e53-fb55-4a9e-9153-6f771a8bfe4e"
+ },
+ "source": [
+ "labels=(policy==ra_expert).astype(float)\n",
+ "labels"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 0., 0., 0.],\n",
+ " [1., 1., 1., 1., 1., 0., 0., 0., 1., 1.],\n",
+ " [1., 1., 1., 1., 0., 0., 0., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 0., 0., 0., 1., 1., 1.],\n",
+ " [1., 1., 1., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 0., 0., 1., 1., 1., 1., 1.],\n",
+ " [0., 0., 0., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
+ " [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 25
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 792
+ },
+ "id": "HNgXXRmt0AGx",
+ "outputId": "e540bd6a-2efe-4ec7-f722-a5c5d706fdf5"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[15,6],dpi=150)\n",
+ "ax = plt.gca()\n",
+ "ax.imshow(labels,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.xaxis.tick_top()\n",
+ "ax.set_xticks(list(range(10)))\n",
+ "ax.set_xticklabels(dealer_cards)\n",
+ "ax.set_yticks(list(range(17)))\n",
+ "ax.set_yticklabels(player_cards) \n",
+ "ax.set_title('Risk Averse Strategy Deviations')\n",
+ "f='bold'#'extra bold'\n",
+ "for i in range(policy.shape[0]):\n",
+ " for j in range(policy.shape[1]):\n",
+ " plt.text(j, i,'0' if labels[i,j]==0 else '1' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\" if labels[i,j]==1 else 'black',fontweight=f)\n",
+ " highlight_cell(j,i, color=\"gray\", linewidth=0.3)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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BnV3FHyEUicHAlWY2uR9/RtrJ25+k9vQeDlAkBT+9azh5vNDd078OIRzDTvRpl2p/uPsGd7/C3U909wMIPyY+T9giS7ZoC8lWjbuvIewqhrA+tNSZPKvjY//b7v6v7n5/lcMM+2R8jeQ97c/7+UTs71d3qu03uHEt4fKnZrr/5mSX8LztbBnlqSP2NxH2WiWyvEcAuPuDdB8mmQ5dbYUnr9nXXcIT6T7jfpK7Xxd3wadlXTfqSX5gvqTWtaxRsnGw3cMffeXum939Knf/uLu/mvBZ/ATh7OyX071Xr99UXHv6ImGFG0sodpm4+3p3X0B34XopUPUXUfzi+yjwXcJujB+a2fv687px1+174tP3E754anXJRfhvNLMDKxaVfGgPNLPXx/GvrxiX/htW070bL4+t7z5x90fc/WuEMwUhXAKRuJ2w+wwKyJbybOpx+ou/q/Bt59hxb7089u+qNjIeBji8zvxJnnpZki/8/jT7d2fst1q4acE2Ysbx9RYSd/N9Kz49NR57TY6/fqv6XPmy0JBF0trZ9z001JJI3qN9zKzWscPeSD5vH4g/0D9AOD/jScIPjL5I1o0n6pxf8rYaw6F360Y9v4/9QYSrI2pJMtzez9fpNXd/Ku5d+nwcNM7MRmVZpoprirs/QPexi9lVjttV1YsTl9It8tQ8SSkW2FMIXwq7EZpTq9xd2xvT4/zPANe4+7N1utsJx15g263X+whn7EHY/Zpstd7j7lW/tOk+MeNkMxtXL2QvjkPXmq/er13ofr+73mt330BolADg81alRZws2czszds7VhOLxbHx6SpPNY9IuPYuMaIvr11DcqLZYTXGzyb8uKolyVMvS1K8DjWz0+qFMbPhFZ+TG1Kv8f9qzPYZenfc+zuEY2xvAP4zDrsprr8DKu4+/BGh0GwAvlQxyY2Ea+AB/nN73xV11rsfEn6MjST8OEw+i9/3vt/FK1k3XmpmL60cGVsnmlFn/t6sGzV5aNUqOWFqVrW9hGb2Lrp//C3sz+tU04fvDqjzXd0rPoCnIpepoxeNSMTpDqW79Y5PVIw7qtoy4vBlhC+Dg+lu9MAI178l19s9ROq09jrLM0IjCE740vhgH//W5XHey3o5/RfpvkSp8rrT0+m+lGA1FZcaVFnW7qm/dy3hBLFRqfEjCLdjuxxYXmX+5PT5o+q8xrmEa0ink7oWlvBL/gN0X4O7oGK+fem+FvWROH9zanwL4TjMj4Hr+/ie/4iwFfHfhF/ce6TG7RFzJe+LA6dXzD+M8OXpwGep0coTvby8gvAl74SGC04FBsfh+xAKkMe8tS5zmR/H3UKV1pVS0303Trc1Lnd0xf9jAuGcgyepuG6ZUFST9+NrxFaICEX/84S9SFUbkajx/nuq69NnpsryVtd63ZjvKMLJUsnlXZuAY2os62i6Gye5NT7fLTV+NGGX5O3ArDqZkpawfp/6O99QZ/qq6wrh3ITk2uOb6G6cZVfCuRr3p9YNZ9vL+P4hNe4DfX39OC7diMSP6W5EYjfCobTkUr16jUisrvPabdXyE/ZI3kJoCzy9riZ/e9J6W9Xrivu0DmVdwI7S0cviGqe9mu5i2JQaflS1ZaSH030N1ZOpD5THleXNtearkeO/6C6w03r5d6av75vUy3lenZrnvRXjRtH9pe+EL7z9trO8/QhN3iXzbCWcpVrZxNjKKvP2prieV7Gcv7Nt034rgH2qzHsw4TZY6b/nKbq/bJLuF31cvxZWzO+EX/iVTTK+APx7jWV8OzXdBsIJZatJNUNH74vrCLpbykpe9+nUe3QJNZoWjPMfmZr2ecJZvaup+EIjnCMwp+JvXE/PZgyTbv+KeQfRs+nEpJgmzW9eTjj25cAl2/l7j04tp88tMlVZ3uq4rI10XxO/hm2vi08KwKHbWd5kejazmXxHPFexrHPqLOPdFdNu0wBMjc/JNusKoZhX/s+SHwpPVLxWW5X5f5kavy5ZN4BP93ZdZdvmD5+m53fNMqp815CtuJ5Iz7/7ufh/SK+rjwAHZVl/3FVca037+tQbPSM1/KhqyyCcmXgc4Uyz3xO+iDbHFfYuQpNn1VaSqsurmOYbdH/xnNiL7MkX3Vri1kov35+kxZTFVcb9OPV+3NDL5e1KOMHkJ3Fl3RQ/vKuAxcAZVG83tDfFdT/CSSsLCDe5T37IPEU4y/kMYEid+ZsIW3PXE74wtxC+NFcSdr99jDpbazWWOQh4C6Fhg18Sfpg9F5fdSbg29xvUb8CiidCM4jJ6fonPq7IeL+lFppGErclVcX18gtBi1/Fx/LzK5VfMfwzhWsMeXz41pp1IuIb3fsKPnc2E65lvBL4AvLrGfEY4HHEb4QfOOsLW3clxfNJyUdUfJBXLeSpOu02buH3t6Nkuc9Il/8sHCG0JfxE4rA/L3Dv+/26j+0fEM4QzducQCnDNz2xcxx5P5fl/23m9uusK4TjxjXQX1vsJP+j3o0ZxSs07Iq7Pf6a7KDs927be7rpKOD5+BeGkz02E762lhPsJV/2BRLbiuhdhj9V34/uefP7Xxv/LLGBE1vXH3cOuJxGRsokndv2VcNboh939ijrTjqf7RJkDvQHHW0Xq0QlNIlJW0wmF9XnC3oB6/jn2f63CKmWg4ioihTGzhWb2fkvdWzY2y3c23WeeX+7uf6uzjHfR3dDJBQOXVqT3tFtYRApjZmsJZ69COFa7JfUc4GbCiXnrKuZ7GfB/hLOsk8Y4furuRV7DLNJlUNEBRORFbQbhxKlxhBN+diecePUHwo3Ur/Dq13EOIrQy5IQ2aH9EuHZXpBS05SoiIpIzHXMVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxbUOMxtmZpPN7Dtm9mcze87MNpjZ3WZ2brw/Z9EZ/8XMrjKzlWb2jJltMrO/mNnl8V6TpWJmo8zscTNzM7t/+3MMeJ4lMUut7p1FZwQwsxYzuyCuhxvNrNPM7jSz/ygw01Hbee+S7tyiMsacrzezH5rZo2a2xczWmtnNZnZSTjemz5rvYDP7npn9LX5+V5vZN9OtVg3w6483s7Pj98jDyf+tF/OdaGa/M7Nn4/p4rZm9seh8ZvZyM/ukmc0zsz+Z2dY4z1F5Z6tH17nWYWan0N0E25+Aewj35nwj4Z6O9wL/6O6PF5MQzOxJwl15lhHuPgNwCHAAobWbY939pwXF24aZzSPceN2AB9z9HwrOswT4R+BKwl1ZKn3d3f/Y0FAVYqP01xNu/7ec7vVwLOEeqYU0BmNmBwFn1xi9K91NEr7V3W9sTKqezOx9wA9injsJd35pAd5MaIhigbt/qIhsMd9bgWsILU3dS7g71aGEz+/DwER3f3iAM1wNvLdyuLvX/OFhZhcS7j61kXDj+yGE2/4Z8H53v7qofGb2acLdoCq9xd2X5JVru/K4tc7O2hFurHspcHDF8H0JH1Sn4obcBWR8E1VurwZ8Mubb5gboBWZN7rl5aezfX4JMS6hxW60ydIRC8AThFnTvqTK+5s2yC859THxf/0qNG783IMMgwi3FHDihYtzBdN+i7i0F5RsWP58OfCE13ID/iMOvb0COzxNun/duYB/iPWbrTP82uu+b+6rU8ImE28Y9TU63betnvvcQiusJwKsIP0zr3sZyQN7XIlaqnaGLK1Jys91e3ze1wRnvjxlr3kO0gVmGxjzL4wqv4tq7fBfHfJ8sOksfc38v5v5KgRkOjRnurTH+ojj+cwXlm5bkA3apGLcb4T68Th/uGZtTru0Vr2tjrk9XGZe8p2cWla/K9D8vorjqmGv/3R37TYTddWWUtMm6udAUwb8Co4FP0J1L6jCzoYQv4A2EG5HvEMxsON278Wreg7UBNvVyuqcGNEVt42P/N+6+NT3CQ3vKt8Sn2+wSLUpcJ98an/6oyiTJsBf9DRTUcH//jY79LUBnkUGqMbPpwIHAytgVmeU1wJnAXHe/2czaisxTw8lmNgrYCtwHXO3ufy040+sIx/b/z903mtkxwNsJx7fuA37o7o8WGbCGYwnnAdzl7isKzPEg8ABwoJmd4O4LkhFmdjDhh8vTwI8Lyjc89p+uMT4p+oc1IEtvHUjYoHjCqx8LvjP2X9O4SOWk4tp/Z8T+z929t7+QB4yZfZZwItNwwvGkQ4BHgQ+6+wsF5toF+DawFvhcUTl6YVbF8wvM7Evu/qVC0gRjY//xGid1fNnMTnb3hQ3OtT3JiUxFbrXi7i+Y2UeAnwLfM7MzCT809yac0LQCONHdi/px/ETst9YY/8rtjC/CK2K/6klW7r4h3kZwpJk1u/v6xkUrF+0W7od4c+aTCVutZbnN1T8RTsB6P6Gw/oVQWO8oNBX8M/B64LPuXtTut3p+A0wHxhBOMDkQOAd4HviimZ1RZ96BNjL23wO8EzidUBjaCDcFHwpcZmavLSRdFWa2L+HEtReAwou+u99COBv8QaAdmAq8hbCH4hdxeFF+E/sdlZfdmNn+hL0UEPZelEVy+eHf60yzIfbLlLvhVFz7KF5+MJ9wRt9n3f3u7czSEO7+Ng+npo8EjiT8Qr/JzM4pKpOZvQL4N+Amd59XVI563P1cd5/v7g+6+0Z3v8/dvwxMjpOcF48zFSH5fA4CznX3i939CXf/i7t/FlhEOPHlswXlq+aDhMtefuHujxUdxsw+CPwOeAg4nFAcDgDmEQ5V/NrMmgqKdwNhN+ruwHVm9gYz293MJgLX0b1ncWutBUh5qbj2Qfw1+XNCAfuGu19UcKRtuPtad78ZeBdwB/AlM3t9QXH+BxhMOIlph+LuNwC/B0YQvpSLkL7uttoJTcmwf2xAlt4qxS5hADN7FXAZ4ZKRSe7+O3ff4O4r3f3jhN3F7cBHi8jn4VTWYwln0L8OuA1YD/yWsIfivDhprWOyRUjWyWF1pkmOJb9odwmDjrn2mpntRfil2Ur4Ujur2ET1ufsWM/sB4YzEdwO3FxBjEuFY6yUVDeEMif39YyMOAMeXYUunwkrCl96+Bb3+X2L/7+7+RJXxq2N/78bEqS+eJDSO8AWcWyMCGRxP2LL/ubtXayDkh4R19EjgfxsZLOHuf4m79acQGqcZSii23yMUXuLzskhO8ntZtZHxTPERwNMv5uOtoOLaKxaaObyOcILJVcDH4q/Osnsy9lsKzDCC2ltWQ1LjhtSYpkjJMc8NdacaOHfF/lAza6py4txesV+tcBRheuxf5e71jsk1SlIAnqkxPhk+ssb4hnD35wm7+Belh6eaElzS6Ex1/JlwiVOLme3v7o9UjG+P/WWNjVU+2i28HfF4zE+ANxBa+ij07Ns+SgrXA0W8uLtbtY7usyAfSA1fXUTGWswsaSIPui8vaKh4KdDdhOP71X6gJMPuqjKuoWIbvSfEp4XvEo6SPSGvqzE+OVyyeuCj9I2Z7UM4OfEpwg/6UnD3jcCv49Pjqkzy/ti/pjGJykvFtQ4z25VwxuNbgZsJ7fSWoUEGAMzsTWb2zni5S3r4bmb2z4QtiY2EtlWlgpm90cKNGXatGN5GuPZxOLC4xvV8jfK12L8gnokLQNyVeGZ8eknDU23rzYRDJo/Q/eVbtJ/E/pFmdlp6hJlNAD4Tn1ZrDKEhzOxQMxtSMexlhOzNhJaONhYSrrZvxP6seFwbgHgi1scJh4K+U0SwMtFu4fo+RTgWAmEX68U1bqJxlrs/WW3EAHsV4fjvk2Z2B+FX7kuAVxOOEz5HuI7voQKy7QgOILx/j5nZnYQvhVbCceohhGNdHysuHrj7AjN7B+EyqxVm9lvCcbk3Ei7mn+Pui+oto0GSE5kWVLY2VBR3v9PMLiCcH3GxmZ1OuLZ1P0LzpbsA33L3XxYY8yxgSlz//kY4fn4E4X/7JXe/bKADmFkHPS8pHByH35oa9iV3/xmAu//SzC4iXOv/BzP7RZzn7YS9LCe5+9qi8sUfoemGQQ6K/YvNbF18/LMBv4a9kW0t7mgd4Ww970XXVlC+VwLnA/9HaDBiM+H42z3AfwH/UPR7WCN3GyVoW5jQ2MbFhLOqHydct7wWWAr8CzC06Pcq5jRCkf894fjvs4QzSj9SdLaYr4nQSlkp2rGukm8K4ZDOk3S3qPZrwiGeorNNJlyB8Lf4+V1DOBnsqDR4na8AACAASURBVAZmOLEX33En1pgvWSefJpyX8sai86W+X+p18wb6fdUt50RERHKmY64iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxVVERCRnKq4iIiI5U3EVERHJmYqriIhIzlRc+8DMlpvZ8qJz1KJ82ShfNsqXjfJlU7Z8Kq4iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjNz96IzDBgzewwYBjyU0yLHxP4DOS0vb8qXjfJlo3zZKF82eed7OfB3d9+nPzPv7MV13S677NI8fPjwoqPUNGjQIJ5//vmiY9SkfNkoXzbKl43y9d+GDRvYunXrenffo18LcPedtgOWNzc3O1DabsaMGYVnUD7lK2unfMpXcLe8v/VHx1xFRERypuIqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFzraG9vZ+bMmSxevJg1a9b0uEB47ty5RcdTPuVTPuVTvpLmG1R0gDKbPXs2kydPLjpGTcqXjfJlo3zZKF82Zc+Xy5armQ0zs8lm9h0z+7OZPWdmG8zsbjM718x2rzLPy83sk2Y2z8z+ZGZbzczN7Kg8MuWts7Oz6Ah1KV82ypeN8mWjfNmUMV9eu4VPAH4MfBR4AVgM3Ay8EvgCcLuZ7V0xz/uA/wE+AhwEWE5ZcrNo0SKmTp1KW1sb7e3tRcfZhvJlo3zZKF82ypdN2fPltVt4C/At4EJ3/1My0Mz2BX4GjAMuJBThxINx2O2x+ybwjpzy5GLBggVdj1tbWwtMUp3yZaN82ShfNsqXTdnz5VJc3f0y4LIqw/9mZqcDvwWONbPB7r45jltM2MIFwMw8jywiIiJFa8TZwnfHfhMwqgGvJyIiUqhGFNfRsb8FKN9RZxERkZw14lKcM2L/5+6+aSBewMyW1xg1ZiBeT0REpJ4B3XI1s3cBJxO2WmcP5GuJiIiUxYBtuZrZQcB8wiU2n3X3u7czS7+5+yE1MiwHxg7U64qIiFQzIFuuZrY/8HNgJPANd79oIF5HRESkjHIvrma2F3AD0ArMBc7K+zVERETKLNfdwrGZw+sIu2KvAj7m7jvs9auzZs2io6MDgKamph7jOjo6WLp0adfziRMnNjQbKF9WypeN8mWjfNmUPV9uxdXMmoCfAG8Argc+6O4v5LX8IowZM4YJEyZUHdfS0kJLS0uDE/WkfNkoXzbKl43yZVP2fHk13L8rsBB4K6FN4WOTlphERERebPI65vopYEp8/CRwcbzbTWX3kmQGM9vXzG5NOiD5CXJxanihl++cdNJJmFmvOuVTPuVTPuVTvkReu4VHph5PqTkVnEcovhCaQzy8yjQHpx7fmy2WiIhI4+XVcP95hMLZl3lWU8LbzImIiGTViLaFRUREXlRUXEVERHKm4ioiIpIzFVcREZGcqbiKiIjkTMVVREQkZyquIiIiOVNxFRERyZmKq4iISM5UXEVERHJmO/DtVrfLzJaPHDly7PTp04uOUlNzczPr168vOkZNypeN8mWjfNkoX//Nnz+fzs7OFe5+SL8W4O47bQcsb25udqC03YwZMwrPoHzKV9ZO+ZSv4G55f+uPdguLiIjkTMVVREQkZyquIiIiOVNxFRERyZmKq4iISM5UXEVERHKm4ioiIpIzFVcREZGcqbiKiIjkTMW1jvb2dmbOnMnixYtZs2ZNj9Y35s6dW3Q85VM+5VM+5StpvkFFByiz2bNnM3ny5KJj1KR82ShfNsqXjfJlU/Z8uWy5mtkwM5tsZt8xsz+b2XNmtsHM7jazc81s9zrznmhmvzOzZ82s08yuNbM35pErT52dnUVHqEv5slG+bJQvG+XLpoz58totfALwY+CjwAvAYuBm4JXAF4DbzWzvypnM7EJgLnAo8Evgd8Dbgd+YWeE/SRYtWsTUqVNpa2ujvb296DjbUL5slC8b5ctG+bIpe768dgtvAb4FXOjuf0oGmtm+wM+AccCFhCKcjHsbcAbwFDDR3VfG4ROBJcBcM1vi7mtzythnCxYs6Hrc2tpaVIyalC8b5ctG+bJRvmzKni+XLVd3v8zdP54urHH434DT49NjzWxwavS/xP6/JYU1zrMUuAQYAZycRz4REZFGasTZwnfHfhMwCsDMhgJvjcN/VGWeZNi7BzaaiIhI/hpRXEfH/hYgOep8IKHYPuHuD1eZ587Yf80AZxMREcldIy7FOSP2f+7um+LjV8R+tcKKu28ws7XASDNrdvf19V7AzJbXGDWmz2lFREQyGtAtVzN7F+G46RZgdmpUcmnO3+vMviH2mwcgmoiIyIAZsC1XMzsImA8Y8Fl3v3s7s/Sbux9SI8NyYOxAva6IiEg1A7Llamb7Az8HRgLfcPeLKiZ5NvaH1VnM8Nivu0tYRESkbHIvrma2F3AD0EpoIOKsKpP9NfZfVmMZwwmX4jy9veOtIiIiZZPrbuHYzOF1hF2xVwEfc3evMumfgU1Ai5nt7+6PVIxPmttYlme+vpo1axYdHR0ANDU19RjX0dHB0qVLu55PnDixodlA+bJSvmyULxvly6bs+XIrrmbWBPwEeANwPfBBd3+h2rTuvtHMfg0cAxxHaL0p7f2xf01e+fpjzJgxTJgwoeq4lpYWWlpaGpyoJ+XLRvmyUb5slC+bsufLq+H+XYGFhIYhbgaOdffN25ntG7E/y8xelVrWRODjwFrgO3nkExERaaS8jrl+CpgSHz8JXGxm86p0L0lmcPdfAhcRWm36g5ldbWbXAr8hbFGfVGS7wgAnnXQSZtarTvmUT/mUT/mUL5HXbuGRqcdTak4F5xGKLwDu/mkz+wOhOL8d2Ey4O86X3P23OWUTERFpqFyKq7ufRyic/Zl3HjAvjxwiIiJl0Ii2hUVERF5UVFxFRERypuIqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFxFRERyZtVvt7pzMLPlI0eOHDt9+vSio9TU3NzM+vXlvR+88mWjfNkoXzbK13/z58+ns7Nzhbsf0q8FuPtO2wHLm5ubHShtN2PGjMIzKJ/ylbVTPuUruFve3/qj3cIiIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxVVERCRnKq4iIiI5U3Gto729nZkzZ7J48WLWrFnTo/WNuXPnFh1P+ZRP+ZRP+Uqab1DRAcps9uzZTJ48uegYNSlfNsqXjfJlo3zZlD1fLluuZjbezM42s6vM7GEzczPz7cyzl5n9h5ndb2abzOxxM/uRmb02j0x56+zsLDpCXcqXjfJlo3zZKF82ZcyX127h2cBXgCnA/tub2Mz2BW4HzgKGA9cBDwLHAreZ2TtyypXJokWLmDp1Km1tbbS3txcdZxvKl43yZaN82ShfNmXPl9du4aXAMkLBvB1YDTTVmf5bwGhCUT3O3TcAmNlk4Erge2Y22t0LvRfRggULuh63trYWmKQ65ctG+bJRvmyUL5uy58uluLr7V9PPzazmtGb2cmAS8DxwWlJY43KuNrPvAycAHwUuyiOfiIhIIxVxtnCy/b7K3f9SZfyNsf/eBuURERHJVRHFdXjsP11j/FOxf1gDsoiIiOSuiEtxnoj9WjvJXxn7e5nZ7u7+7PYWaGbLa4wa09dwIiIiWRWx5fo7YBPwUjN7Z3qEhYO1J6YGNTcwl4iISC4aXlzd/Rng4vj0MjObYmZ7mtmBwPeBg1OTb+3lMg+p1gEP5JteRERk+4pqoWkm8HLg/cBVqeGbgTOA/4nP1zY4l4iISGaFFFd33wQcZ2ZvBt4JtAAPEbZck5ad7o/TiYiI7FAKbVvY3W8Gbk4PM7MPx4dLGh6owqxZs+jo6ACgqalnmxgdHR0sXbq06/nEiRMbmg2ULyvly0b5slG+bMqer1QN98cTmk6PT+cUmQVgzJgxTJgwoeq4lpYWWlpaGpyoJ+XLRvmyUb5slC+bsucr5JZzZvYKM9u7YthQQrOIbwDmufvvisgmIiKSVV53xekws1uTDhgch9+a6jpSs7wVeMTMlprZD8xsMfAwcApwPXBaHrmyOumkkzCzXnXKp3zKp3zKp3yJvHYLtwCHVxl+eMU0iTuAHwETgNcSrnv9IzAXmOvudW9XJyIiUmZ5Ndw/D5jXh+n/CHwwj9cWEREpm0KOuYqIiOzMVFxFRERypuIqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFxFRERyZjtzM75mtnzkyJFjp0+fXnSUmpqbm1m/fn3RMWpSvmyULxvly0b5+m/+/Pl0dnaucPdD+rUAd99pO2B5c3OzA6XtZsyYUXgG5VO+snbKp3wFd8v7W3+0W1hERCRnKq4iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxVVERCRnKq51tLe3M3PmTBYvXsyaNWt6tL4xd+7couMpn/Ipn/IpX0nzDSo6QJnNnj2byZMnFx2jJuXLRvmyUb5slC+bsufLbcvVzMab2dlmdpWZPWxmbma+nXn2M7Nvmtn9ZrbJzP5uZsvM7Atm1pxXtjx0dnYWHaEu5ctG+bJRvmyUL5sy5stzt/Bs4CvAFGD/7U1sZq8C/gCcDuwK/BS4EXg5cC5wq5ntmWO+Plu0aBFTp06lra2N9vb2IqNUpXzZKF82ypeN8mVT9nx57hZeCiwDbo/daqCpzvRfBVqAi4EZ7v4CQCyoPwcmAP8C/GuOGftkwYIFXY9bW1uLilGT8mWjfNkoXzbKl03Z8+VWXN39q+nnZra9WY6M/S8lhTUu5xkz+xpwFfD6vPKJiIg0SpFnC2/qxTRPDXgKERGRnBVZXG+I/dlmtmsyMO4W/lx8+t2GpxIREcmoyEtxZgLjgU8C7zKzO4AhwJuA54Bp7n5jbxZkZstrjBqTR1AREZG+KKy4uvtjZnYUsBB4B9CWGn0VcEcBsURERDIrrLia2WuAnwEvAO8FfgMMB95PuKTnKDN7o7v/eXvLcvdDarzGcmBsbqFFRER6oZDiama7AT8C9gNe7+53xlFrgYviMdivA18EphaRUUREpL+KOqFpAvAqYFWqsKYtiv0jq4wTEREptaJ2C78s9p+pMT4ZPrIBWWqaNWsWHR0dADQ19WwPo6Ojg6VLl3Y9nzhxYkOzgfJlpXzZKF82ypdN2fMVVVwfi/0DzazZ3ddXjE8aj1jduEjbGjNmDBMmTKg6rqWlhZaWlgYn6kn5slG+bJQvG+XLpuz5itotvBR4nHAC0zfNrOtnh5ntB/xnfPqjArKJiIhkkuddcTrM7NakAwbH4bemug4Ad38O+DjwPPBh4AEzu9rMrgfuBV4N3An8e175+uOkk07CzHrVKZ/yKZ/yKZ/yJfLcLdwCHF5l+OEV0wDg7leb2RuAswgnLr0L2AysBH4IXOjuG3PMJyIi0hB5Ntw/D5jXx3nuAj6UVwYREZEyKLJtYRERkZ2SiquIiEjOVFxFRERypuIqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOzN2LzjBgzGz5yJEjx06fPr3oKDU1Nzezfn3lHffKQ/myUb5slC8b5eu/+fPn09nZucLdD+nXAtx9p+2A5c3NzQ6UtpsxY0bhGZRP+craKZ/yFdwt72/90W5hERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFxFRERypuIqIiKSMxVXERGRnKm41tHe3s7MmTNZvHgxa9as6dH6xty5c4uOp3zKp3zKp3wlzTeo6ABlNnv2bCZPnlx0jJqULxvly0b5slG+bMqeL5ctVzMbb2Znm9lVZvawmbmZeZ3pvRfdr/PIlpfOzs6iI9SlfNkoXzbKl43yZVPGfHltuc4G3tuH6S+rM64DeAlwc6ZEOVi0aBELFy7ktttuA2D16tXFBqqgfNkoXzbKl43yZVP2fHkV16XAMuD22K0GmmpN7O4nVhtuZiOA4+PT+Tll67cFCxZ0PW5tbS0wSXXKl43yZaN82ShfNmXPl0txdfevpp+bWX8XdRyhKN/q7iuz5hIRESlC2c4Wnhb7VxSaQkREJIPSFFczewXwZmAL8IOC44iIiPRbmS7F+RBgwHXu/lRfZjSz5TVGjcmcSkREpI9Ks+WKdgmLiMhOohRbrmbWDowF1gLX9HV+dz+kxnKXx+WKiIg0TFm2XJOt1kXuvqnQJCIiIhkVXlzNbFdKdG2riIhIVmXYLXw0sC/wF0rQKlParFmz6OjoAKCpqWebGB0dHSxdurTr+cSJExuaDZQvK+XLRvmyUb5syp6vDMU12SU8391rtkdchDFjxjBhwoSq41paWmhpaWlwop6ULxvly0b5slG+bMqer9DdwmY2DJgSn+osYRER2SnkdVecDjO7NemAwXH4ramuo8qsk4Hdgdvd/c95ZMnTSSedhJn1qlM+5VM+5VM+5UvktVu4BTi8yvDDK6ap1LVLOKccIiIihcur4f55wLx+zPeuPF5fRESkTAq/FEdERGRno+IqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFxFRERypuIqIiKSMyvZXd5yZWbLR44cOXb69OlFR6mpubmZ9evXFx2jJuXLRvmyKXu+lpYW9thjj6Jj1LRu3Trl66evfOUrPPbYYyvc/ZB+LcDdd9oOWN7c3OxAabsZM2YUnkH5lK+sXdnzXXTRRV5mytd/Y8eOdWC597P+aLewiIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxVVERCRnKq4iIiI5U3EVERHJmYqriIhIzlRc62hvb2fmzJksXryYNWvW9Gh9Y+7cuUXHUz7lU74dOB/A1q1bmTNnDkceeSSjRo1iyJAhjB49mlNPPZWVK1cWHU/5MhhU6KuX3OzZs5k8eXLRMWpSvmyULxvly2bDhg1MmjSJJUuW9Bi+atUq5syZw+WXX87ChQuZMmWK8u2A+XLZcjWz8WZ2tpldZWYPm5mbmfdivt3M7NNm9jszW2dmz5rZfWb2XTPbP49seens7Cw6Ql3Kl43yZaN8fXfaaaf1KAxjx47lmGOOYejQoQBs2rSJ448/nnvvvVf5dsB8ee0Wng18BZgC9KoomtlewFLgP4GXAb8EbgCeA04CXplTtn5btGgRU6dOpa2tjfb29qLjbEP5slG+bJSv/5YtW8YVV1zR9fy4447jnnvu4dprr+WWW25h0KCwU3Hz5s2cc845yreD5YP8dgsvBZYBt8duNdBUa2IzM+BHwHjgC8C/ufvzqfGjgXU5Zeu3BQsWdD1ubW0tMEl1ypeN8mWjfP2XLgwAZ555JuFrEcaNG8fRRx/N9ddfD8A111zD2rVrGTFihPLtIPkgpy1Xd/+qu5/r7te4+2O9mOU44C3AInc/L11Y4/IedPcn88gmIlI2S5cu7XpsZowbN67H+PHjx3c93rJlC3fccUfDsoHy5aGos4U/Fvv/XdDri4gUJn0m66hRoxg8eHCP8fvss0+P5/fdd19DciWUL7uGny1sZrsBRwDPA78zs9cQtmT3Bh4BfuLudzc6l4hIo6xdu7brcXICTtqwYcN6PH/mmWcGPFOa8mVXxKU4o4EhwBrgM8D59NyCPs/MLnL3z/R2gWa2vMaoMf1OKSLSAO7bXlhRbVhRlK9/itgtPDL2RxHOML6EUARfApwMbAQ+bWanF5BNRGTA7bnnnl2PN27cuM34ymHp6RtB+bIrorgmrzkIuM7dT48nMD3l7t8FPhvHz+ztAt39kGod8EDO2UVEMjvggAO6Hnd2drJp06Ye4x999NGa0zeC8mVXRHF9NvW4Whtk82J/fzP7h4GPIyLSWBMnTux67O7cddddPcanz27dbbfdepz92gjKl10RxfUvqcerK0e6+9+Bx+PTvRsRSESkkaZNm9bj+QUXXNB1nPDOO+/kxhtv7Bo3adKkhl+jqXzZNfyEJnd/xsxWEVpgGlk53sx2AZJ34tnK8Y00a9YsOjo6AGhq6tkmRkdHR49rrdK/pBpF+bJRvmyUr/8OO+wwpk2bxvz58wG48sorOfTQQ2ltbWXJkiU8/3y49H/w4MGcf/75Dc2mfPkoquH+xcAZwFGEJg/TJgCDCSc2/bmxsXoaM2YMEyZMqDqupaWFlpaWBifqSfmyUb5slC+bSy65hIceeoibbroJgBUrVrBixYqu8U1NTSxYsICDDz5Y+XbAfEU1InEhsBn4lJl1rf1m9pI4DmCuu2+qNrOIyI5u+PDh/OpXv+LSSy/liCOOYMSIEQwePJi2tjZOOeUUli1bxrHHHqt8O2i+Hvc47G8HdAC3prqtgFcM66iY56Nxus3ATYSt2SfjfHcAu+eQa3lzc7PHZZaymzFjRuEZlE/5ytqVPd9FF13kZaZ8/Td27FgHlns/609eu4VbgMOrDD+8Ypou7v5dM3sQODtONxR4kNAk4gXuviGnbCIiIg2VS3F193l0X0LTl/mWAEvyyCAiIlIWRR1zFRER2WmpuIqIiORMxVVERCRnKq4iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiOTMPN5gdmdkZstHjhw5dvr06UVHqam5uZn169cXHaMm5cum7PlaWlrYY489io5R07p165Qvg8cff7zU61+ZPx/z58+ns7Nzhbsf0q8F9LfF/x2hQ3fFUT7lq9uV+a4k7uW+a4p7+fOVff0rez4y3BVHu4VFRERypuIqIiKSMxVXERGRnKm4ioiI5EzFVUREJGcqriIiIjlTcRUREcmZiquIiEjOVFxFRERypuJaR3t7OzNnzmTx4sWsWbOmR+sbc+fOLTqe8ilf4bZu3cqcOXM48sgjGTVqFEOGDGH06NGceuqprFy5suh4ypdB2de/sucbVHSAMps9ezaTJ08uOkZNypeN8mWzYcMGJk2axJIlS3oMX7VqFXPmzOHyyy9n4cKFTJkyRfl2wHxlX//Kni+XLVczG29mZ5vZVWb2sJm5mXmd6c9LpqnR/XseufLU2dlZdIS6lC8b5eu70047rUdhGDt2LMcccwxDhw4FYNOmTRx//PHce++9yrcD5ksr4/qXVsZ8ee0Wng18BZgC7N+H+W4BLqvS3ZFTrkwWLVrE1KlTaWtro729veg421C+bJSv/5YtW8YVV1zR9fy4447jnnvu4dprr+WWW25h0KCwU2zz5s2cc845yreD5YNyr39Q/nx57RZeCiwDbo/daqCpF/N9293n5ZQhdwsWLOh63NraWmCS6pQvG+Xrv3RhADjzzDMxMwDGjRvH0UcfzfXXXw/ANddcw9q1axkxYoTy7SD5oNzrH5Q/Xy5bru7+VXc/192vcffH8limiJTX0qVLux6bGePGjesxfvz48V2Pt2zZwh13NHZnlPJJ0XS2sIj0WfpM1lGjRjF48OAe4/fZZ58ez++7776G5EoonxSt6LOF32pmrwWGAA8D17m7fqKJlNzatWu7Hicn4KQNGzasx/NnnnlmwDOlKZ8UrejiOr3i+ZfM7ErgRHd/trcLMbPlNUaN6XcyEekV920vDKg2rCjKJ0Uoarfw/cBZwCHA7sDLgQ8BjwDvA66oPauIFG3PPffserxx48ZtxlcOS0/fCMonRStky9Xd51cM2gAsMLMbgT8Ck81sgrvf2svlHVJteNyiHZsprIhs44ADDuCJJ54AwjWGmzZtoqmp+wKBRx99dJvplW/HySfZleqEJnf/G5C0W/XOIrOISG0TJ07seuzu3HXXXT3Gp89u3W233Xqc/doIyidFK1VxjZLT6PYtNIWI1DRt2rQezy+44IKu44R33nknN954Y9e4SZMmNfwaTeWTohV9QlM1I2N/Q6EpgFmzZtHR0QHQY5cNQEdHR49r1dK/RBtF+bJRvv477LDDmDZtGvPnhyM8V155JYceeiitra0sWbKE559/HoDBgwdz/vnnNzSb8uWjzOsflD9fqYqrhSZKklaq7ywyC8CYMWOYMGFC1XEtLS20tLQ0OFFPypeN8mVzySWX8NBDD3HTTTcBsGLFClasWNE1vqmpiQULFnDwwQcr3w6Yr+zrX9nzNXy3sJm1mNnpZtZcMXx34H+Bw4HHgKsanU1Eem/48OH86le/4tJLL+WII45gxIgRDB48mLa2Nk455RSWLVvGscceq3w7aD7JKH0PvP52QAdwa6rbCnjFsI44bVsctx74NfA94AbgyTj8aeBNOeVa3tzc7HG5pexmzJhReAble/Hmu+iii7zMlC+bsq9/Zc8HLPd+1p+8dgu3ELY4Kx1eMQ3AU8BXgQnAAcAbgReAVcA84D/d/ZGccomIiDRcLsXVw51t5vVy2vXA2Xm8roiISBmV8VIcERGRHZqKq4iISM5UXEVERHKm4ioiIpIzFVcREZGcqbiKiIjkTMVVREQkZyquIiIiOVNxFRERyZmKq4iISM7M4w16d0ZmtnzkyJFjp0+fXnSUmpqbm1m/fn3RMWpSvmxaWlrYY489io5R07p165Qvg8cff7zU61/ZPx9lzjd//nw6OztXuPsh/VpAf1v83xE6dFcc5Su4K/tdU5Qvm7Kvf8qXuev3XXG0W1hERCRnKq4iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuIqIiORMxVVERCRnKq51tLe3M3PmTBYvXsyaNWt6tL4xd+7couMp306eD2Dr1q3MmTOHI488klGjRjFkyBBGjx7NqaeeysqVK4uOp3wZlH39U75sBhUdoMxmz57N5MmTi45Rk/JlU/Z8GzZsYNKkSSxZsqTH8FWrVjFnzhwuv/xyFi5cyJQpU5RvB8xX9vVP+bLJZcvVzMabdVzr3gAAIABJREFU2dlmdpWZPWxmbmbex2X8MpnPzF6WR648dXZ2Fh2hLuXLpoz5TjvttB6FYezYsRxzzDEMHToUgE2bNnH88cdz7733Kt8OmC+tjOtfmvL1XV67hWcDXwGmAPv3dWYzOxE4mtBQcmksWrSIqVOn0tbWRnt7e9FxtqF82ZQ537Jly7jiiiu6nh933HHcc889XHvttdxyyy0MGhR2Om3evJlzzjlH+XawfFDu9Q+UL6u8dgsvBZYBt8duNdDUmxnNrAX4OnADcCDQmlOmzBYsWND1uLW1NLG6KF82Zc6XLgwAZ555JmYGwLhx4zj66KO5/vrrAbjmmmtYu3YtI0aMUL4dJB+Ue/0D5csqly1Xd/+qu5/r7te4+2N9nP1CYBjwyTyyiOwMli5d2vXYzBg3blyP8ePHj+96vGXLFu64446GZQPlE9meQs8WNrN3AicA57v7A0VmESmT9Jmso0aNYvDgwT3G77PPPj2e33fffQ3JlVA+kfoKK65mNhz4X+Be4GtF5RApo7Vr13Y9Tk7ASRs2bFiP588888yAZ0pTPpH6irwU54tAG3CUu2/OsiAzW15j1JgsyxUpA/dtz/OrNqwoyieyrUK2XM2sHTgDuMzdbyoig0iZ7bnnnl2PN27cuM34ymHp6RtB+UTqa3hxNbNdgW8Da4Gz8limux9SrQN0HFd2SAcccEDX487OTjZt2tRj/KOPPlpz+kZQPpH6ithy/TQwDvicuz9ZwOuLlN7EiRO7Hrs7d911V4/x6bNbd9tttx5nvzaC8onUV0RxfTehsYiPmNmSdAckp/AtisPeWUA+kcJNmzatx/MLLrig6zjhnXfeyY033tg1btKkSQ2/RlP5ROor6oQmA46sM35C7M8b+Ci1zZo1i46ODgCamnq2idHR0dHjWrr0L+VGUb5sypzvsMMOY9q0acyfPx+AK6+8kkMPPZTW1laWLFnC888/D8DgwYM5//zzG5pN+fJR5vUPlC+rhhdXdz+q1jgzW01ooenl7v5wozLVMmbMGCZMmFB1XEtLCy0tLQ1O1JPyZVP2fJdccgkPPfQQN90UzvlbsWIFK1as6Brf1NTEggULOPjgg5VvB8xX9vVP+bLRLedESmr48OH86le/4tJLL+WII45gxIgRDB48mLa2Nk455RSWLVvGscceq3w7aD7ZyaXvgdffDugAbk11WwnHVdPDOnqxnNVxvpfllGt5c3Ozx2WWspsxY0bhGZRv4LqLLrrIy0z5sin7+qd8mbvl3s/6k9du4Rbg8CrDD6+YRkREZKeXS3F193nkcPKRu7dlXYaIiEjRdMxVREQkZyquIiIiOVNxFRERyZmKq4iISM5UXEVERHKm4ioiIpIzFVcREZGcqbiKiIjkTMVVREQkZyquIiIiOTOPNxDeGZnZ8pEjR46dPn160VFqam5uZv369UXHqEn5smlpaWGPPfYoOkZN69atU74MHn/88VKvf2X/fJQ53/z58+ns7Fzh7of0awH9bfF/R+jQXXGUr+Cu7Hd1Ub5syr7+KV/mrt93xdFuYRERkZypuIqIiORMxVVERCRnKq4iIiI5U3EVERHJmYqriIhIzlRcRUREcqbiKiIikjMVVxERkZypuNbR3t7OzJkzWbx4MWvWrOnR+sbcuXOLjqd8O3k+gK1btzJnzhyOPPJIRo0axZAhQxg9ejSnnnoqK1euLDqe8mVQ9vVP+bIZVHSAMps9ezaTJ08uOkZNypdN2fNt2LCBSZMmsWTJkh7DV61axZw5c7j88stZuHAhU6ZMUb4dMF/Z1z/lyyaXLVczG29mZ5vZVWb2sJm5mXmd6d9jZpeZ2R/N7Ekz22Jmj5vZtWY2KY9Meevs7Cw6Ql3Kl00Z85122mk9CsPYsWM55phjGDp0KACbNm3i+OOP595771W+HTBfWhnXvzTl67u8dgvPBr4CTAH278X0HwaSW9XcBlwJPAgcA1xjZl/OKVcmixYtYurUqbS1tdHe3l50nG0oXzZlzrds2TKuuOKKrufHHXcc99xzD9deey233HILgwaFnU6bN2/mnHPOUb4dLB+Ue/0D5csqr93CS4FlwO2xWw001Zn+fODj7v5UeqCZHQ78EjjbzBa6+x9zytcvCxYs6Hrc2tpaYJLqlC+bMudLFwaAM888EzMDYNy4cRx99NFcf/31AFxzzTWsXbuWESNGKN8Okg/Kvf6B8mWVy5aru3/V3c9192vc/bFeTH9XZWGNw28DfgAY8JY8sonsiJYuXdr12MwYN25cj/Hjx4/verxlyxbuuOOOhmUD5RPZnjKeLbwl9jcXmkKkQOkzWUeNGsXgwYN7jN9nn316PL/vvvsakiuhfCL1laq4/v/27j+4rvus8/jnKbYU2yixa243oQ0S0eLiH11juRCpuCFDKOC5hjqGjEORh3oJ6Ybumu2a7sQbqQuUwFI8TDPsgo2XVdKo8k6F01SGbFNILZe6agqxwROpqb2lbl1MnMDFjuJ6FRs/+4eu1Hvle2X5fr+63yPl/Zo5c3+co+v3jDJ9eo7OPcfM3iZpq8YH7J8nzgGSOXfu3OTziRNwSi1evLjs9fnz52e9qRR9wPSSfhXHzH5K0s9IWijpeyS9Q+OD9Zfc/avX8TnDVVa1BkcCiblffeJ9pfdSoQ+4Wuo917WSfkHSeyRtkDQm6T9Ieny6HwLmu5tuumny+cWLF69aP/W90u3rgT5gekmHq7v/prubpEWS3iapR9IfSfqUmTVM+8Pln7O60iJpxnu/QJasWLFi8nmhUNDY2FjZ+jNnzlTdvh7oA6aXes9VkuTu/8/dn3f390v6fUmbNL4HC7wudXR0TD53dx07dqxsfenZrQsXLiw7+7Ue6AOml4nhOsXEIeF3J60AEurs7Cx7vXv37sm/Ex49elSHDh2aXLdp06a6f0eTPmB6Wby28D8WH3NJKyR1dXUpn89Lkhoby6+Jkc/ny75LV/r/lOuFvjBZ7lu7dq06OzvV29srSTpw4IDWrFmj5uZmDQ4O6vLly5KkhoYGPfzww3Vtoy+OLP/3J9EXKovD9UeKj8n/Xtra2qr29vaK63K5nHK5tPOfvjBZ79uzZ49Onz6tw4cPS5JGRkY0MjIyub6xsVF9fX1auXIlfXOwL+v//dEXpu6Hhc0sZ2a/ZGaLK6x7l6SPFF+mv2cQkNCSJUv0zDPPaO/evdqwYYOWLl2qhoYGtbS06L777tPx48e1ZcsW+uZoH+a50nvg1bpIykv6YslyRZJPeS9f3LaluO6CpM9J6pP0KUlfLr7vkn4vUtdwU1OTl3xu5pYdO3Ykb6Bv9pZHHnnEs4y+MFn/74++4GXYa5w/sQ4L5yTdXuH926dsI0kvSfrPku6UtFrS2zW+B/0Pkv63pL3uPhipCwCAuosyXN39UUmPznDbb0n63eICAMC8k8Wv4gAAMKcxXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVwBAIjMvHgD4fnIzIaXLVu2atu2balTqmpqatLo6GjqjKqy3pfL5XTjjTemzqjqlVdeyXTfSy+9lOnfb9b/+6MvTJb7ent7VSgURtx9dU0fUOsV/+fCIu6KM+/7sn7XlKz3Zf33Sx99iZea74rDYWEAACJjuAIAEBnDFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7hOo62tTbt27dLAwIDOnj1bdvWNnp6e1Hn0RXDlyhXt27dPd9xxh5YvX64bbrhBt912m+6//36dPHkydV6m+7L++6WPvpQWpA7Isu7ubm3evDl1RlX0hblw4YI2bdqkwcHBsve/9rWvad++ffrYxz6m/fv36+6776avgqz/fukLQ1+YKHuuZrbezB40syfM7Jtm5mbmVbZ9g5m908w+YmbPmdmomY2Z2VfNbI+ZfW+MptgKhULqhGnRd/0eeOCBssG1atUqbdy4UYsWLZIkjY2N6d5779ULL7xA3zVk8fdbir4w9F2/WIeFuyX9tqS7Jb35GtveJulzkj4o6bslfVbSn0lqlPQ+SX9rZhsidQXp7+/X1q1b1dLSora2ttQ5V6GvdsePH9fjjz8++fqee+7R888/r6eeekpHjhzRggXjB3Vee+01PfTQQ/RVkOXfr0RfKPrCxDosPCTpuKS/Ki6nND4sK3FJfy7pv0k65D5++xoza5S0R9J7JX3czP61u1+K1FeTvr6+yefNzc0JSyqjr3alg0uSdu7cKTOTJK1bt0533XWXnn76aUnSwYMHde7cOS1dupS+Eln+/Ur0haIvTJQ9V3f/HXf/kLsfdPcXr7HtV939x939sxODtfj+mKRflnRe0vdIekeMNqCSoaGhyedmpnXr1pWtX79+/eTzS5cu6bnnnqtbm5T9PgDTy9TZwu5+UdKJ4svvTtmC+a30TNvly5eroaGhbP3NN99c9vrEiROqp6z3AZhepoarmb1B0sT+/bR7wECIc+fOTT6fOEGo1OLFi8tenz9/ftabSmW9D8D0svZVnJ+T9CZJL0v6wkx/yMyGq6xqjRGF+a3krxPTvpdK1vsAXC0ze65mdqukjxZffqj4N1hgVtx0002Tzy9evHjV+qnvlW5fD1nvAzC9TOy5mtkSSU9I+i5JT7r7nuv5eXdfXeVzhyWtCi/EfLNixQq9/PLLksa/Izc2NqbGxm+f4H7mzJmrtqcPwEwl33M1s4WS+iW9XdLnJb0nbRFeDzo6Oiafu7uOHTtWtr707NuFCxeWnZ1bD1nvAzC9pMO1eALTY5I2SvobST9VPGMYmFWdnZ1lr3fv3j35d8yjR4/q0KFDk+s2bdpU9++QZr0PwPRSHxb+fY2fxHRC0k+4+7lrbF9XXV1dyufzklR2SE6S8vl82XcRS/c06oW+2q1du1adnZ3q7e2VJB04cEBr1qxRc3OzBgcHdfnyZUlSQ0ODHn744bq2zYU+Kdu/X4m+UPSFSTZczew3NX7RiG9Iepe7v5SqpZrW1la1t7dXXJfL5ZTL5epcVI6+MHv27NHp06d1+PBhSdLIyIhGRkYm1zc2Nqqvr08rV66kr4Ks/37pC0NfmCSHhc3sA5Ie0vh3WX/M3b+RogOvb0uWLNEzzzyjvXv3asOGDVq6dKkaGhrU0tKi++67T8ePH9eWLVvoA3D9Su+BV+siKS/piyXLFY1fQ7j0vXxx2x8oWf8FSY9WWTZE6Bpuamry4r+VyWXHjh3JG+Zy3yOPPOJZlvW+rP9+6aMv8TLsNc6fWIeFc5Jur/D+7VO2kaSlkqz4vKO4VDKo8bOHAQCYU6IMV3d/VON7mzPZdlDfHq4AAMw7yb/nCgDAfMNwBQAgMoYrAACRMVwBAIiM4QoAQGQMVwAAImO4AgAQGcMVAIDIGK4AAETGcAUAIDLz4g2Y5yMzG77llltWPfjgg6lTqnrllVd04403ps6oir4wWe976aWXNDo6mjqjqqamJvoC0Fe73t5eFQqFEXdfXdMH1HrF/7mwSBq+9dZba75jSD1k/a4p9IXJel/W70pCH32Jl5rvisNhYQAAImO4AgAQGcMVAIDIGK4AAETGcAUAIDKGKwAAkTFcAQCIjOEKAEBkDFcAACJjuF7DlStXtG/fPt1xxx1avny5brjhBt122226//77dfLkydR59NGXTFtbm3bt2qWBgQGdPXu27Oo0PT09Sdvooy+5Wi/tNBcWBV7+8NVXX/U777yz6qWxGhsb/Yknnqj5893DLo9HH32hfSGXn/vkJz9Z9XN7enqSXx6PPvoiLGkvf2hm683sQTN7wsy+aWZuZj7N9rea2S+b2aNm9mUzu1L8mTtj9MTywAMPaHBwcPL1qlWrtHHjRi1atEiSNDY2pnvvvVcvvPACffS97vpKFQqF1AnToi8Mfdcv1mHhbkm/LeluSW+ewfY/I+l/SPoFSd8vySJ1RHP8+HE9/vjjk6/vuecePf/883rqqad05MgRLViwQJL02muv6aGHHqKPvtdVnyT19/dr69atamlpUVtbW5KG6dAXhr4wsYbrkKQPS/ppSbdIGrvG9n8n6aOSfl7SCkmfidQRTen/sEnSzp07ZTb+/wHWrVunu+66a3LdwYMHde7cOfroe930SVJfX58+8YlP6Otf/3rd/+2ZoC8MfWGiDFd3/x13/5C7H3T3F2ew/YC7f8Dd+9z9pMaPbWfK0NDQ5HMz07p168rWr1+/fvL5pUuX9Nxzz9WtTaIvFH0AZhNnC1dReibm8uXL1dDQULb+5ptvLnt94sSJunRNoC8MfQBmE8O1itLDbBMnkJRavHhx2evz58/PelMp+sLQB2A2LUgdEIOZDVdZ1Rrj892vPmpd6b1U6AtDH4DY2HOt4qabbpp8fvHixavWT32vdPt6oC8MfQBm07wYru6+utIi6au1fuaKFSsmnxcKBY2NlZ8AfebMmarb1wN9YegDMJvmxXCdDR0dHZPP3V3Hjh0rW196dubChQvLzt6sB/rC0AdgNjFcq+js7Cx7vXv37sm/cx09elSHDh2aXLdp0yYtXbqUPvpeN30ApjcvTmiaDWvXrlVnZ6d6e3slSQcOHNCaNWvU3NyswcFBXb58WZLU0NCghx9+mD76Xld9ktTV1aV8Pi9JamxsLFuXz+fLvqtbuideL/SFoS8Mw3Uae/bs0enTp3X48GFJ0sjIiEZGRibXNzY2qq+vTytXrqSPvtddX2trq9rb2yuuy+VyyuVydS4qR18Y+sJwWHgaS5Ys0TPPPKO9e/dqw4YNWrp0qRoaGtTS0qL77rtPx48f15YtW+ij73XZB2Aatd5Op3SRlJf0xZLlisYvaVj6Xr5k+1umrDtX3H6k5L3uCF1Bt5yrh5BbktUDfWGy3hdyy696LPTRl3ip+ZZzsQ4L5yTdXuH926dsM6Gxyvalx7fS30cLAIAaRBmu7v6opEevY/tTyuBt5gAAiIG/uQIAEBnDFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMjMizdgno/MbPiWW25Z9eCDD6ZOqeqVV17RjTfemDqjqqz3vfTSSxodHU2dUVVTUxN9AegLQ1/tent7VSgURtx9dU0fUOsV/+fCIu6KEyzrfVm/qwZ99NE3d/sUcFccDgsDABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVwBAIiM4QoAQGQMVwAAImO4AgAQGcP1Gq5cuaJ9+/bpjjvu0PLly3XDDTfotttu0/3336+TJ0+mzqMvQFtbm3bt2qWBgQGdPXu27OoqPT09Sdvooy81+sIsSB2QZRcuXNCmTZs0ODhY9v7XvvY17du3Tx/72Me0f/9+3X333fTNwb7u7m5t3rw5yb89E/SFoS8MfWGi7Lma2Xoze9DMnjCzb5qZm5nP4Ofea2ZfMrNXzaxgZk+Z2TtiNMXwwAMPlA2GVatWaePGjVq0aJEkaWxsTPfee69eeOEF+uZgX6lCoZA6YVr0haEvDH3XL9Zh4W5Jvy3pbklvnskPmNlHJfVIWiPpLyR9SdK7JH3OzJL/35Hjx4/r8ccfn3x9zz336Pnnn9dTTz2lI0eOaMGC8Z3+1157TQ899BB9c6xPkvr7+7V161a1tLSora0tScN06AtDXxj6wsQ6LDwk6bikvyoupyQ1VtvYzH5M0q9I+idJHe5+svh+h6RBST1mNuju5yL1XbfSwSBJO3fulJlJktatW6e77rpLTz/9tCTp4MGDOnfunJYuXUrfHOmTpL6+vsnnzc3Ndf23Z4K+MPSFoS9MlD1Xd/8dd/+Qux909xdn8CP/qfj4mxODtfg5Q5L2SFoq6RdjtNVqaGho8rmZad26dWXr169fP/n80qVLeu655+rWJtEHAFlW97OFzWyRpB8tvvyTCptMvPdT9SmqrPRM1uXLl6uhoaFs/c0331z2+sSJE3XpmkAfAGRXiq/ivFXjh4xfdvdvVlh/tPj4b+qXdLVz5759RHriBJxSixcvLnt9/vz5WW8qRR8AZFeKr+J8T/Gx0mCVu18ws3OSlplZk7uPXusDzWy4yqrWGhunNs3ovVToA4BsSbHn+p3Fx29Ns82F4mPTLLdUddNNN00+v3jx4lXrp75Xun090AcA2TUvrtDk7qsrLZK+WutnrlixYvJ5oVDQ2NhY2fozZ85U3b4e6AOA7EoxXF8tPi6eZpslxcdrHhKeLR0dHZPP3V3Hjh0rW196duvChQvLzn6tB/oAILtSDNdvFB/fUmmlmS3R+Fdx/nkmf2+dLZ2dnWWvd+/ePfl3wqNHj+rQoUOT6zZt2lT372jSBwDZleKEpq9IGpOUM7M3u/vfT1k/camN4/XNKrd27Vp1dnaqt7dXknTgwAGtWbNGzc3NGhwc1OXLlyVJDQ0Nevjhh+mbY32S1NXVpXw+L0lqbCy/5kk+ny/7rm7pnni90BeGvjD0han7cHX3i2b2WUkbJd0j6aNTNvnZ4uPBuoZVsGfPHp0+fVqHDx+WJI2MjGhkZGRyfWNjo/r6+rRy5Ur65mBfa2ur2tvbK67L5XLK5XJ1LipHXxj6wtAXJtUJTb9XfOwys++beLN4+cP3STon6Y9ThJVasmSJnnnmGe3du1cbNmzQ0qVL1dDQoJaWFt133306fvy4tmzZQt8c7QOAWVN6D7xaF0l5SV8sWa5I8inv5af8zEeL21yQ9KSkpyRdknRZ0uZIXcO33nqrZ9kjjzySOmFaWe/bsWOHF/87yuRCH330zd0+ScNe4/yJdVg4J+n2Cu/fPmWbSe7+H83sbyT9e43fDec1jd8d58Pu/oVIXQAA1F2U4eruj0p6tF4/BwBAls2Li0gAAJAlDFcAACJjuAIAEBnDFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiMy/ewHo+MrPhZcuWrdq2bVvqlKqampo0OprsnvDXRF8Y+sLQF4a+2vX29qpQKIy4++qaPqDWK/7PhUXScFNTU+q7Kszpu0LQRx999L1e+xRwVxwOCwMAEBnDFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZw3UabW1t2rVrlwYGBnT27Nmyq2/09PSkzqOPPvrooy+jfQtSB2RZd3e3Nm/enDqjKvrC0BeGvjD0hcl6X/I9VzNbaWYfN7N/MLMxMztlZv/dzL4rdVupQqGQOmFa9IWhLwx9YegLk8W+pMPVzH5U0l9Leo+kc5L+VNKYpPdLOmZmb0mYp/7+fm3dulUtLS1qa2tLmVIRfWHoC0NfGPrCZL0v2WFhM1ssqU/SYkm/4e7/tfi+SfqIpF+V9MeSfiJVY19f3+Tz5ubmVBlV0ReGvjD0haEvTNb7Uu65bpH0ryR9RdKvT7zp4/eK+y+STkn6cTNbm6QOAIAapRyu64uPn3P3K6Ur3P2SpCPFl++uaxUAAIFSDtclxcd/rrL+n4qP7LkCAOaUlF/Febn4WO1g+fdeY/0kMxuusqr1eqMAAAiVcs/1c8XH/NSv3ZjZmyW9q/iyqa5VAAAESrnn+hlJRyW1Sfo/ZvZ+SSOS3iZpb0nblco//m3uvrrS+8U92lVRagEAmKFke67Fs4K3SBqW9HZJz0oalfQFSW+S9GvFTav9TRYAgExKevlDd/+6mf2ApLslvUPSIo0P249rfPCq+BoAgDkj+bWF3f2ypP7iMsnM3lF8OljvpgldXV3K5/OSpMbGxrJ1+XxeQ0NDk687Ojrq2ibRF4q+MPSFoS9M1vuSD9dKzOxmST+r8a/jPJGqo7W1Ve3t7RXX5XI55XK5OheVoy8MfWHoC0NfmKz3pb628Bozu2HKe2+R9CmNnyW8090vJokDAKBGqe+K86uSzprZITPrM7O/kPR/Jf2QpA+7+2Mp47Zv3y4zm9FCH3300UcffRNSHxZ+UtLNGr8K0w9r/MzgT0v6qLsPJuwCAKBmqc8WflLjAxYAgHkj9WFhAADmHYYrAACRMVwBAIiM4QoAQGQMVwAAImO4AgAQGcMVAIDIGK4AAETGcAUAIDKGKwAAkZm7p26YNWY2vGzZslXbtm1LnVJVU1OTRkdHU2dURV8Y+sLQF4a+2vX29qpQKIy4++qaPsDd5+0iabipqcklZXbZsWNH8gb66MvqQh99iZfhWucPh4UBAIiM4QoAQGQMVwAAImO4AgAQGcMVAIDIGK4AAETGcAUAIDKGKwAAkTFcAQCIjOE6jba2Nu3atUsDAwM6e/Zs2dU3enp6UufRRx999NGX0b4FqQOyrLu7W5s3b06dURV9YegLQ18Y+sJkvS/5nquZ/aCZfcLMzpjZJTM7Z2Z/aWbbzcxS900oFAqpE6ZFXxj6wtAXhr4wWexLOlzN7GckDUm6R9I/SHpC0lFJ7ZL+l6TedHVSf3+/tm7dqpaWFrW1taVMqYi+MPSFoS8MfWGy3pfssLCZLZD0B5K+Q9LPu3tfybqVkj4v6T1m9j/d/VCKxr6+ySQ1NzenSJgWfWHoC0NfGPrCZL0v5Z7r90t6k6SvlA5WSXL3L+vbe60/WO8wAABCpByuYzPc7p9mtQIAgMhSDte/k/RVSW81s/eUrigeFu6U9M+SPpmgDQCAmiX7m6u7/4uZ/YKkP5X0cTPbKemkxg8Vv1PSiKT3uvs1TwMzs+Eqq1pj9QIAMFNJv+fq7kfM7Ec0vnfaVlwk6TVJf67xvVsAAOaU1F/F+TlJX5J0WtLtkr5T0gpJj0raKemzZtZ4rc9x99WVFo0fdgYAoK6SDVcz+z5Jj0n6R0mb3P1L7n7B3U+6+/s0fri4TdK/TdUIAEAtUu653itpoaRPu/urFdZ/ovh4R/2SAAAIl/Jvrm8pPp6vsn7i/WV1aKmoq6tL+XxektTYWH50Op/Pa2hoaPJ1R0dHXdsk+kLRF4a+MPSFyXpfyuH6YvHx7VXWT1w84tTvKEbTAAAKWklEQVTsp1TW2tqq9vb2iutyuZxyuVydi8rRF4a+MPSFoS9M1vtSHhb+VPHxDjN7oHSFmbVL+kDx5Z/UtQoAgEDJhqu7H5W0u/jyD8zs+eLdcT4v6YikJZL+yN3/IlXj9u3bZWYzWuijjz766KNvQurvuX7QzL4g6d9JWi/prZJGJR2WtM/d96fsAwCgFslvlu7unxSXOAQAzCPJb5YOAMB8w3AFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwBQAgMnP31A2zxsyGly1btmrbtm2pU6pqamrS6Oho6oyq6AtDXxj6wtBXu97eXhUKhRF3X13TB7j7vF0kDTc1NbmkzC47duxI3kAffVld6KMv8TJc6/zhsDAAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVyn0dbWpl27dmlgYEBnz54tu/pGT09P6jz66KOPPvoy2rcgdUCWdXd3a/PmzakzqqIvDH1h6AtDX5is9yXbczWzO83MZ7B8KFVjqUKhkDphWvSFoS8MfWHoC5PFvpR7ri9KeqzKuu+Q1Fl8/pf1yblaf3+/9u/fr2effVaSdOrUqVQpFdEXhr4w9IWhL0zW+5INV3d/QdJ7K60zs40aH66nJQ3Wr6pcX1/f5PPm5uZUGVXRF4a+MPSFoS9M1vuyekLTxF7rx30+33AWADAvZW64mtkSSe8uvnw8ZQsAALXI3HCVtEXSEknH3H0kdQwAANcri1/FmTgkPOO9VjMbrrKqNTwHAIDrk6k9VzO7RdJdkv5F0v7EOQAA1CRre64/p/Gv4Xza3V+c6Q+5++pK7xf3aFdFagMAYEYyteeqGg4JAwCQNZkZrma2UtI6Sa9KejJxDgAANcvSYeFtxccn3P1bSUuKurq6lM/nJUmNjY1l6/L5vIaGhiZfd3R01LVNoi8UfWHoC0NfmKz3ZWK4mplJek/xZWYOCbe2tqq9vb3iulwup1wuV+eicvSFoS8MfWHoC5P1vqwcFn6npGZJfy/ps4lbAAAIkpXhOnEiU5+7X0laUmL79u0ysxkt9NFHH3300Tch+XA1s0ZJP1t82ZuyBQCAGJL/zdXdxyS9MXUHAACxJN9zBQBgvmG4AgAQGcMVAIDIGK4AAETGcAUAIDKGKwAAkTFcAQCIjOEKAEBkDFcAACJjuAIAEJm5e+qGWWNmw8uWLVu1bdu2a2+cSFNTk0ZHR1NnVEVfGPrC0BeGvtr19vaqUCiMuPvqmj7A3eftImm4qanJJWV22bFjR/IG+ujL6kIffYmX4VrnD4eFAQCIjOEKAEBkDFcAACJjuAIAEBnDFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhOo22tjbt2rVLAwMDOnv2bNnVN3p6elLn0UcfffTRl9G+BakDsqy7u1ubN29OnVEVfWHoC0NfGPrCZL0vE3uuZpYzs91m9hUzu2hmBTM7ama/m7ptQqFQSJ0wLfrC0BeGvjD0hcliX/LhambrJX1Z0k5JlyR9StIXJb1R0gcSpqm/v19bt25VS0uL2traUqZURF8Y+sLQF4a+MFnvS3pY2Mxykj4taZGkd7v7wJT1P5QkrKivr2/yeXNzc8KSyugLQ18Y+sLQFybrfan/5vrrkr5L0vunDlZJcvcv1T8JAIAwyQ4Lm9kiSZ2SLkhKf2oXAACRpNxzfbukJkmfd/eLZrZR0rsk3SDphKRPuPuZhH0AANQk5XBdVXx8ycyelPTuKet/y8x+0d33X+uDzGy4yqrWkEAAAGqR8mzhZcXHn5b0k5LeL+lNklok7db4SU6PmdkPJKkDAKBGKfdcJwb7AkkPufsflKz7oJk1S7pH0gcl/fx0H+Tuqyu9X9yjXVVpHQAAsyXlnuurJc8rndA08d6P1KEFAIBoUg7Xrxcfv+XuL1dYf6r4+Kb65AAAEEfKw8LHio+LzKzR3cemrH9j8fFVJdLV1aV8Pi9JamxsLFuXz+c1NDQ0+bqjo6OubRJ9oegLQ18Y+sJkvS/ZcHX3b5jZ30paq/FDv5+ZssnE4eBjSqS1tVXt7e0V1+VyOeVyuToXlaMvDH1h6AtDX5is96W+tvBHio+7zeyWiTeLZwjvLL7cU/cqAAACJB2u7t4n6TFJb5M0YmZ/Zmaf1bcv3L/P3ftT9W3fvl1mNqOFPvroo48++iakvrawJG2XdETS+yTdKcklHZW0190fS9gFAEBNkg9Xd3dJ+4oLAABzXuq/uQIAMO8wXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVwBAIjMxq8+OD+Z2fCyZctWbdu2LXVKVU1NTRodHU2dURV9YegLQ18Y+mrX29urQqEw4u6ra/oAd5+3i6ThpqYm1/jNADK57NixI3kDffRldaGPvsTLcK3zh8PCAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwnUZbW5t27dqlgYEBnT17tuzqGz09Panz6KOPPvroy2jfgtQBWdbd3a3NmzenzqiKvjD0haEvDH1hst6XdM/VzAbNzKdZfjJlX6lCoZA6YVr0haEvDH1h6AuTxb6s7LkekPRqhff/vt4hpfr7+7V//349++yzkqRTp06lzLkKfWHoC0NfGPrCZL0vK8P1V939VOqIqfr6+iafNzc3JyypjL4w9IWhLwx9YbLexwlNAABExnAFACCyrBwW/kUzWy7piqQTkp50928kbgIAoCZZGa5dU17vNrMPu/uHZ/LDZjZcZVVrWBYAANcv9WHhz0napvEhuFjSWyU9JOmypN8ws19J2AYAQE2S7rm6+4emvHVC0m+Z2V9LelrSr5nZH7n7xWt8zupK7xf3aFdFiQUAYIZS77lW5O6fkfTXkpZKuj1xDgAA1yWTw7XoZPHxlqQVAABcp6yc0FTJsuLjhVQBXV1dyufzkqTGxsaydfl8XkNDQ5OvOzo66tom0ReKvjD0haEvTNb7MjlczSwn6Z3Fl0dTdbS2tqq9vb3iulwup1wuV+eicvSFoS8MfWHoC5P1vmSHhc3sHWa22cy+Y8r7LZI+KWmJpAF3/2aCPAAAapbyb64rND5Ev2lmf2ZmHzezz0v6sqQfljQs6ZcS9mn79u0ysxkt9NFHH3300Tch5WHhZyX9ocbPBv5Bjf+N9YKkv5HUL+kPr/UVHAAAsijZcHX3L0v65VT/PgAAsyXLX8UBAGBOYrgCABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVwBAIiM4QoAQGQMVwAAIjN3T90wa8zslTe84Q1NV65cSZ1S1Rvf+EYVCoXUGVXRF4a+MPSFoS/YqLvfWMsPzvfh+qKkxZJOp24BAMwpt0r6lrvfXMsPz+vhCgBACvzNFQCAyBiuAABExnAFACAyhisAAJExXAEAiIzhCgBAZAxXAAAiY7gCABAZwxUAgMgYrgAARMZwBQAgMoYrAACRMVwBAIjs/wMeoGoObw66IQAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 2250x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bSBDX6kwl9I6"
+ },
+ "source": [
+ "dataset=index.reshape(-1,3)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "kkdGrivDmPU3",
+ "outputId": "f2375268-4f2b-4e11-987e-20411417bf50"
+ },
+ "source": [
+ "dataset_labels=labels.flatten()\n",
+ "dataset_labels"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0.,\n",
+ " 1., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 0.,\n",
+ " 0., 0., 1., 1., 1., 1., 1., 1., 0., 0., 1., 1., 1., 1., 1., 0., 0.,\n",
+ " 0., 0., 0., 1., 1., 1., 1., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 49
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "pN3GqS63Zpb4",
+ "outputId": "52d463db-b015-4640-fcdc-b7da7d51fbf8"
+ },
+ "source": [
+ "from sklearn.model_selection import KFold\n",
+ "k=5\n",
+ "kf = KFold(n_splits=k,random_state=7,shuffle=True)\n",
+ "bce=nn.BCELoss()\n",
+ "\n",
+ "ssc=SafetyClassifier(agent.support,action_dim=0,size=51,state_dim=3).to('cuda')\n",
+ "dsc=SafetyClassifier(agent.support,action_dim=2,size=51,state_dim=0).to('cuda')\n",
+ "sdsc=SafetyClassifier(agent.support,action_dim=2,size=51,state_dim=3).to('cuda')\n",
+ "optimiser_ssc=torch.optim.Adam(ssc.parameters())\n",
+ "optimiser_dsc=torch.optim.Adam(dsc.parameters())\n",
+ "optimiser_sdsc=torch.optim.Adam(sdsc.parameters())\n",
+ "s_results=np.zeros([k,2,2])\n",
+ "d_results=np.zeros([k,2,2])\n",
+ "sd_results=np.zeros([k,2,2])\n",
+ "ssc.train()\n",
+ "dsc.train()\n",
+ "sdsc.train()\n",
+ "c=0\n",
+ "for train_index, test_index in kf.split(dataset):\n",
+ " X_train, X_test = dataset[train_index], dataset[test_index]\n",
+ " y_train, y_test = dataset_labels[train_index], dataset_labels[test_index]\n",
+ "\n",
+ " for i in range(2500):\n",
+ " optimiser_ssc.zero_grad()\n",
+ " optimiser_dsc.zero_grad()\n",
+ " optimiser_sdsc.zero_grad()\n",
+ " indices=np.random.randint(len(X_train),size=batch_size)\n",
+ " sample_x=X_train[indices]\n",
+ " sample_y=y_train[indices]\n",
+ " dist=agent.dqn.dist(torch.tensor(sample_x).to('cuda').float()).detach()\n",
+ "\n",
+ " pred=ssc(None,torch.tensor(sample_x).to('cuda').float())\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " optimiser_ssc.step() \n",
+ "\n",
+ " pred=dsc(dist)\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " optimiser_dsc.step() \n",
+ "\n",
+ " pred=sdsc(dist,torch.tensor(sample_x).to('cuda').float())\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " optimiser_sdsc.step() \n",
+ " print(c)\n",
+ " pred=ssc(None,torch.tensor(X_test).to('cuda').float()).to('cuda').float()\n",
+ " out=(pred>0.5).float().flatten().cpu().numpy()\n",
+ " s_results[c]=confusion_matrix(y_test,out)\n",
+ " print(s_results[c])\n",
+ " dist=agent.dqn.dist(torch.tensor(X_test).to('cuda').float())\n",
+ "\n",
+ " pred=dsc(dist).to('cuda').float()\n",
+ " out=(pred>0.5).float().flatten().cpu().numpy()\n",
+ " d_results[c]=confusion_matrix(y_test,out)\n",
+ " print(d_results[c])\n",
+ " pred=sdsc(dist,torch.tensor(X_test).to('cuda').float()).to('cuda').float()\n",
+ " out=(pred>0.5).float().flatten().cpu().numpy()\n",
+ " sd_results[c]=confusion_matrix(y_test,out) \n",
+ "\n",
+ " print(sd_results[c])\n",
+ " c+=1 "
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "[[ 2. 0.]\n",
+ " [ 3. 29.]]\n",
+ "[[ 1. 1.]\n",
+ " [ 1. 31.]]\n",
+ "[[ 2. 0.]\n",
+ " [ 2. 30.]]\n",
+ "1\n",
+ "[[ 3. 2.]\n",
+ " [ 0. 29.]]\n",
+ "[[ 4. 1.]\n",
+ " [ 0. 29.]]\n",
+ "[[ 3. 2.]\n",
+ " [ 1. 28.]]\n",
+ "2\n",
+ "[[ 3. 3.]\n",
+ " [ 1. 27.]]\n",
+ "[[ 5. 1.]\n",
+ " [ 0. 28.]]\n",
+ "[[ 5. 1.]\n",
+ " [ 1. 27.]]\n",
+ "3\n",
+ "[[ 1. 3.]\n",
+ " [ 0. 30.]]\n",
+ "[[ 4. 0.]\n",
+ " [ 0. 30.]]\n",
+ "[[ 2. 2.]\n",
+ " [ 0. 30.]]\n",
+ "4\n",
+ "[[ 5. 0.]\n",
+ " [ 4. 25.]]\n",
+ "[[ 5. 0.]\n",
+ " [ 1. 28.]]\n",
+ "[[ 5. 0.]\n",
+ " [ 3. 26.]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "9FgVvpW1kdJh",
+ "outputId": "74631e85-641e-443e-fe79-768f5ae2244b"
+ },
+ "source": [
+ "print(s_results.mean(axis=0))\n",
+ "print(d_results.mean(axis=0))\n",
+ "print(sd_results.mean(axis=0))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[[ 2.8 1.6]\n",
+ " [ 1.6 28. ]]\n",
+ "[[ 3.8 0.6]\n",
+ " [ 0.4 29.2]]\n",
+ "[[ 3.4 1. ]\n",
+ " [ 1.4 28.2]]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "g0W3lgMslM6A"
+ },
+ "source": [
+ "print(s_results)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3GVxdl4fmmcq"
+ },
+ "source": [
+ "print(d_results)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KL-LQoVTmrVI"
+ },
+ "source": [
+ "print(sd_results)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "eohVf9H7vK3n"
+ },
+ "source": [
+ "**Code below is not used in report**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Tvjg5jHFkFp1"
+ },
+ "source": [
+ "from sklearn.model_selection import train_test_split"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SfpgsMovrbB5"
+ },
+ "source": [
+ "X_train, X_test, y_train, y_test = train_test_split(dataset, dataset_labels, test_size=0.5, random_state=42)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "LDFWv2O-suZj",
+ "outputId": "e0772578-251e-45a6-d0be-7087af37501c"
+ },
+ "source": [
+ "y_train,y_test"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([0., 1., 0., 0., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
+ " 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1.,\n",
+ " 1., 1., 1., 1., 0., 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 0.]),\n",
+ " array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1.,\n",
+ " 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 1., 0.,\n",
+ " 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1., 1., 0.,\n",
+ " 1., 1., 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 0., 1., 1.,\n",
+ " 1., 0., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]))"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "zKW0Fj6vtV0w"
+ },
+ "source": [
+ "weight=np.ones(y_train.shape)\n",
+ "total_one=np.count_nonzero(y_train==1)\n",
+ "total_zero=np.count_nonzero(y_train==0)\n",
+ "weight[y_train==0]=total_one/total_zero\n",
+ "weight=weight/np.sum(weight)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "3_P1ao979vmm",
+ "outputId": "f34ddd78-098f-4944-d9e6-433a03af0892"
+ },
+ "source": [
+ "#with state\n",
+ "losses=[]\n",
+ "bce=nn.BCELoss()\n",
+ "ssc=SafetyClassifier(agent.support,action_dim=0,size=51,state_dim=3).to('cuda')\n",
+ "optimiser=torch.optim.Adam(ssc.parameters())\n",
+ "ssc.train()\n",
+ "for i in range(3000):\n",
+ " optimiser.zero_grad()\n",
+ " indices=np.random.randint(len(X_train),size=batch_size)\n",
+ " #indices=np.random.choice(np.arange(len(X_train)),size=batch_size,replace=True,p=weight)\n",
+ " sample_x=X_train[indices]\n",
+ " sample_y=y_train[indices]\n",
+ " #dist=agent.dqn.dist(torch.tensor(sample_x).to('cuda').float())\n",
+ " pred=ssc(None,torch.tensor(sample_x).to('cuda').float())\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " losses.append(loss.item())\n",
+ " optimiser.step()\n",
+ "plt.plot(losses)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "IzbTLrK2eIDK",
+ "outputId": "a5c38861-61e4-439c-f2ae-20f6f64c9d45"
+ },
+ "source": [
+ "pred=ssc(None,torch.tensor(X_test).to('cuda').float()).to('cuda').float()\n",
+ "out=(pred>0.5).float().flatten().cpu().numpy()\n",
+ "confusion_matrix(y_test,out)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 7, 3],\n",
+ " [ 3, 72]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 73
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "AGKYKr0bicGb",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "outputId": "c687406b-b447-4789-a465-43ace3fd8043"
+ },
+ "source": [
+ "#with ret dist\n",
+ "losses=[]\n",
+ "bce=nn.BCELoss()\n",
+ "dsc=SafetyClassifier(agent.support,action_dim=2,size=51,state_dim=0).to('cuda')\n",
+ "optimiser=torch.optim.Adam(dsc.parameters())\n",
+ "dsc.train()\n",
+ "for i in range(2000):\n",
+ " optimiser.zero_grad()\n",
+ " indices=np.random.randint(len(X_train),size=batch_size)\n",
+ " sample_x=X_train[indices]\n",
+ " sample_y=y_train[indices]\n",
+ " dist=agent.dqn.dist(torch.tensor(sample_x).to('cuda').float())\n",
+ " pred=dsc(dist).to('cuda').float()\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " losses.append(loss.item())\n",
+ " optimiser.step()\n",
+ "plt.plot(losses)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "BMv_TcxJrpFF"
+ },
+ "source": [
+ "dist=agent.dqn.dist(torch.tensor(X_test).to('cuda').float())\n",
+ "pred=dsc(dist).to('cuda').float()\n",
+ "out=(pred>0.5).float().flatten().cpu().numpy()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "evldt5e0dClw"
+ },
+ "source": [
+ "from sklearn.metrics import confusion_matrix"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "dns3JgQWr5QK",
+ "outputId": "ebb07402-fab0-4e3d-96ca-736ca8382198"
+ },
+ "source": [
+ "confusion_matrix(y_test,out)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[10, 0],\n",
+ " [ 1, 74]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 69
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "h9TKIa1B165S",
+ "outputId": "61432488-5da6-4980-a265-499d96e480db"
+ },
+ "source": [
+ "y_test!=out"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([False, False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False, False, False, True, False, False,\n",
+ " True, False, False, False, False, False, False, False, False,\n",
+ " False, True, False, False, False, False, True, False, False,\n",
+ " False, True, False, False, False, False, False, False, False,\n",
+ " False, True, False, False, False, True, False, False, False,\n",
+ " True, False, False, False, False, False, False, False, False,\n",
+ " False, False, True, False, False, False, True, False, False,\n",
+ " False, False, False, False, False, False, False, False, False,\n",
+ " False, False, False, False])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 218
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "Oz2lhdX22E4X",
+ "outputId": "42e19349-f726-4479-8b43-b641c83452ea"
+ },
+ "source": [
+ "#with state + ret dist\n",
+ "losses=[]\n",
+ "bce=nn.BCELoss()\n",
+ "sdsc=SafetyClassifier(agent.support,action_dim=2,size=51,state_dim=3).to('cuda')\n",
+ "optimiser=torch.optim.Adam(sdsc.parameters())\n",
+ "sdsc.train()\n",
+ "for i in range(2000):\n",
+ " optimiser.zero_grad()\n",
+ " indices=np.random.randint(len(X_train),size=batch_size)\n",
+ " #indices=np.random.choice(np.arange(len(X_train)),size=batch_size,replace=True,p=weight)\n",
+ " sample_x=X_train[indices]\n",
+ " sample_y=y_train[indices]\n",
+ " dist=agent.dqn.dist(torch.tensor(sample_x).to('cuda').float())\n",
+ " pred=sdsc(dist,torch.tensor(sample_x).to('cuda').float())\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " losses.append(loss.item())\n",
+ " optimiser.step()\n",
+ "plt.plot(losses)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "EsJA56TMgfzk",
+ "outputId": "e621e0ca-3908-438f-ba6e-015fbde45eb0"
+ },
+ "source": [
+ "dist=agent.dqn.dist(torch.tensor(X_test).to('cuda').float())\n",
+ "pred=sdsc(dist,torch.tensor(X_test).to('cuda').float()).to('cuda').float()\n",
+ "out=(pred>0.5).float().flatten().cpu().numpy()\n",
+ "confusion_matrix(y_test,out)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 9, 1],\n",
+ " [ 1, 74]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 77
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "cwD1KKyPA6U3",
+ "outputId": "e7e60b9a-ee53-470b-c486-d25ca4c349d9"
+ },
+ "source": [
+ "for i in X_test[y_test!=out]:\n",
+ " j=np.array(np.where(np.all(index==i,axis=-1))).flatten()\n",
+ " print(j,final_label[tuple(j)])\n",
+ " if final_label[tuple(j)]==1:\n",
+ " final_label[tuple(j)]=0\n",
+ " else:\n",
+ " final_label[tuple(j)]=1"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[8 5] 0.0\n",
+ "[7 5] 0.0\n",
+ "[7 6] 0.0\n",
+ "[11 1] 0.0\n",
+ "[9 3] 0.0\n",
+ "[6 5] 0.0\n",
+ "[10 1] 0.0\n",
+ "[6 7] 0.0\n",
+ "[6 6] 0.0\n",
+ "[10 0] 0.0\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 792
+ },
+ "id": "ZxqUpWVWA-VX",
+ "outputId": "6dfcbae4-879e-4fbf-ca1c-8b347a29b100"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[15,6],dpi=150)\n",
+ "ax = plt.gca()\n",
+ "ax.imshow(final_label,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.xaxis.tick_top()\n",
+ "ax.set_xticks(list(range(10)))\n",
+ "ax.set_xticklabels(dealer_cards)\n",
+ "ax.set_yticks(list(range(17)))\n",
+ "ax.set_yticklabels(player_cards) \n",
+ "ax.set_title('SC Predictions')\n",
+ "f='bold'#'extra bold'\n",
+ "for i in range(policy.shape[0]):\n",
+ " for j in range(policy.shape[1]):\n",
+ " plt.text(j, i,'0' if final_label[i,j]==0 else '1' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\" if final_label[i,j]==1 else 'black',fontweight=f)\n",
+ " highlight_cell(j,i, color=\"gray\", linewidth=0.3)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2250x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 265
+ },
+ "id": "GZ9cXYuHBpwG",
+ "outputId": "d3eae622-d4ab-4572-831c-6252aed5b69c"
+ },
+ "source": [
+ "#with state\n",
+ "losses=[]\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SafetyClassifier(agent.support,action_dim=2,size=51).to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters())\n",
+ "classifier.train()\n",
+ "for i in range(1000):\n",
+ " optimiser.zero_grad()\n",
+ " #indices=np.random.randint(len(X_train),size=batch_size)\n",
+ " indices=np.random.choice(np.arange(len(X_train)),size=batch_size,replace=True,p=weight)\n",
+ " sample_x=X_train[indices]\n",
+ " sample_y=y_train[indices]\n",
+ " dist=agent.dqn.dist(torch.tensor(sample_x).to('cuda').float())\n",
+ " pred=classifier(dist)\n",
+ " loss=bce(pred.squeeze(),torch.tensor(sample_y).float().to('cuda'))\n",
+ " loss.backward()\n",
+ " losses.append(loss.item())\n",
+ " optimiser.step()\n",
+ "plt.plot(losses)\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XKuSCwCxBsXD"
+ },
+ "source": [
+ "dist=agent.dqn.dist(torch.tensor(X_test).to('cuda').float())\n",
+ "pred=classifier(dist)\n",
+ "out=(pred>0.5).float().flatten().cpu().numpy()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "-UEAYUm0FW11",
+ "outputId": "4cf9fc93-18b3-4dde-a741-340dfc56c4c6"
+ },
+ "source": [
+ "from sklearn.metrics import confusion_matrix\n",
+ "confusion_matrix(y_test,out)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([[ 3, 2],\n",
+ " [ 5, 47]])"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 452
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "A_7v0voUFa2g"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/c51.ipynb b/code/c51.ipynb
new file mode 100644
index 0000000..b70937a
--- /dev/null
+++ b/code/c51.ipynb
@@ -0,0 +1,1395 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "colab": {
+ "name": "c51.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ax_tMYQiHvmT"
+ },
+ "source": [
+ "## Configurations for Colab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U4LL8VVQmz5L"
+ },
+ "source": [
+ "#windy cliff walking\n",
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " #Add next line in to rebalance environment under U\n",
+ " #reward=min(0,reward) \n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBE-ev78Hvmi"
+ },
+ "source": [
+ "## Network\n",
+ "\n",
+ "The parametrized distribution can be represented by a neural network, as in DQN, but with atom_size x out_dim outputs. A softmax is applied independently for each action dimension of the output to ensure that the distribution for each action is appropriately normalized.\n",
+ "\n",
+ "To estimate q-values, we use inner product of each action's softmax distribution and support which is the set of atoms $\\{z_i = V_{min} + i\\Delta z: 0 \\le i < N\\}, \\Delta z = \\frac{V_{max} - V_{min}}{N-1}$.\n",
+ "\n",
+ "$$\n",
+ "Q(s_t, a_t) = \\sum_i z_i p_i(s_t, a_t), \\\\\n",
+ "\\text{where } p_i \\text{ is the probability of } z_i \\text{ (the output of softmax)}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 300), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300, 300), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(300, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.95,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -3,\n",
+ " v_max: float = 3,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim = 2#env.observation_space.shape[0]\n",
+ " action_dim = env.action_space.n\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ "\n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action\n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 200):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " \n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ "\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " def test_(self,printout=False):\n",
+ " self.is_test=True\n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ "\n",
+ " while not done:\n",
+ "\n",
+ " action = self.greedy_action(state)\n",
+ " if printout:\n",
+ " print(state,action)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " self.is_test=False\n",
+ " return score \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ "\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EMEkNhyJHvmk"
+ },
+ "source": [
+ "## Environment\n",
+ "\n",
+ "You can see the [code](https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py) and [configurations](https://github.com/openai/gym/blob/master/gym/envs/__init__.py#L53) of CartPole-v0 from OpenAI's repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jsi7CspeHvml"
+ },
+ "source": [
+ "## Set random seed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gw0lyYO0dmaq",
+ "outputId": "17c3691c-ff45-4de2-9424-5d32c3d2758f"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "7EhNf_08dxoM"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m_ELje9LE150",
+ "outputId": "fec26fdb-a1d8-4f8a-c88f-ec077cb14297"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aChSfk4uHvmn",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c0e0d484-7b35-4edd-fd15-24823a90b189"
+ },
+ "source": [
+ "# parameters\n",
+ "env=cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "num_frames = 150000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 300\n",
+ "epsilon_decay = 1 /140000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay,gamma=0.9)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cpu\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "wWNU5lWca__0",
+ "outputId": "06b08634-a482-41da-9581-7189b4468b58"
+ },
+ "source": [
+ "agent2 = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay,gamma=0.99)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cpu\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mC5KKHXLHvmo",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "outputId": "3dbe16f5-6165-4315-e7ff-1610a8cf7933"
+ },
+ "source": [
+ "agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "2mSQLoZNbVej",
+ "outputId": "7c7f17fc-cd2e-4d27-c724-ae14be254b83"
+ },
+ "source": [
+ "agent2.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ehzu1VRAgNPI",
+ "outputId": "58c122be-d79f-423a-f028-b16e15ab180f"
+ },
+ "source": [
+ "#optimal_agent.pt has been trained with the default hyperparameters on WCW environment\n",
+ "agent.dqn.load_state_dict(torch.load('optimal_agent.pt',map_location=torch.device('cpu')))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 18
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "mWMyoR0jMabl",
+ "outputId": "3faf0d93-ba97-4b4f-adf5-c143669f622b"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])-1\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "ax = plt.gca()\n",
+ "plt.title('Windy Cliff Walking')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='bold'#'extra bold'\n",
+ " if i==3 and j>0 and j<6:\n",
+ " plt.text(j, i,'C' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ " elif j>0 and j<6:\n",
+ " plt.text(j, i,'W' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==3 and j==0:\n",
+ " plt.text(j, i,'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==3 and j>5:\n",
+ " plt.text(j, i,'G' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ "\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "sowOAk6QB9ck",
+ "outputId": "08c83482-56b7-4268-a004-055fbee4fc6c"
+ },
+ "source": [
+ " d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=0).argmax().cpu().item()]\n",
+ " pol2[i,j]=arrow_list[agent.dqn(torch.tensor([i,j]).float()).argmax().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Greedy Policy\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol2[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 729
+ },
+ "id": "FCt9wK1OajQt",
+ "outputId": "df72ffc3-7713-45ce-be3a-bad3a24335fa"
+ },
+ "source": [
+ " d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=0).argmax().cpu().item()]\n",
+ " pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=8).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Risk-sensitive Policy, with $\\alpha =8$\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol2[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 729
+ },
+ "id": "90g7BT0aT5gN",
+ "outputId": "eacfa531-6948-461b-daa9-47c2b7da560b"
+ },
+ "source": [
+ "#c51 trained under a utility function output\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=0).argmax().cpu().item()]\n",
+ " pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=3).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"c51 Policy under $U=min(0,r)$\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol2[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 729
+ },
+ "id": "hyQKNulCHxuz",
+ "outputId": "edfc0c4e-a418-461b-87f0-da84baa3566f"
+ },
+ "source": [
+ " d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=0).argmax().cpu().item()]\n",
+ " pol2[i,j]=arrow_list[agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=3).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Risk-sensitive Policy, with $\\alpha =3$\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol2[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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57IHz7TCtcTiF3pEuwTRsVK34OZVSbp3k/r3mD9SFJwGcxrgcu1LKXkk+kq7MTN+b5tlv2yR/OGLVMbXWMxdz7lrryzI7Sb19ZifBJ+0vRrT9n1rrcUs9UK316lrryBIltSu11L8OPX2JtfePSjc6f9g/Tnmisp8Q3yqzf4c3G9x8GF5/bWb/jI5e4Jz9xH3NAuVzptxYP8NW2Rsye8LkV9VaPzLfTrXWc5N8tdf8gCTDkyn/R6314yvv4hbjzulubmx63TnJ/hk9D0bS/Y7cs9bav4kOwDKoMQ/A2JRSNtXXvUu6kcJPyuja8r9Xa/3xGLsy6svFRWM834IGoy/fm9l9e3WSP19OsnaFmr1HpZRfy+z63S+ttS44weQI70xXV/bAobZH5oa6vov1vcweqTqnWuuvSinvSfLHQ823LKXsWWv9+Ry7TV0cTqNa68WllI9kZn3sB5VSbj5XMnOEp2X2AJST5tthSuNyrAY1uh+broTNniM2+Urmf8rgUZk9AerHa60fXWJX/jBdqYThEa+/V0p5Ra31V0s81qorpRyW5N695m+l+xmPw/FJjhxa3i3dz2mxtZz7k77+Ksk/r0K/xunMdGWrhm/cH5XZo+A3uWu692WTL9Zazy6lfCc3/N7daYHrRj8xf0at9cIl9ntatPgMWxWllIdmZnwnXbJ91NMzo5yRbs6VTX63t/5Pl9m1Uc5LctNVPN5SrMa8LavpzCR/WGv93KQ7ArCeSMwDsFL3K6UsN4n8i3Q1n9+5mh0a4eJ0I+GGH5k+NF1d9+ZKKb+X7gv08OfwdUmeOyiFMQkt36NH95avyAIlRuZSa62llE9kZgL08HR1wpfipGWMJv2vEW23TzJXUmOq4nDKvSUzE/PbJHlyRk8SOMrTesvnp6sVPJ9pjMuVOKSUcvoc626U7umNm82z/0+SPGaBm4SjRiSfsMj+bVZr/W4p5d8z88bIbknuluTLSz3eGDx8RNv/rbVeN6bzfTRd6ZnbDLU9J4tIzJdSbpHZpZ8+OO5k60oNfmdOSfK4oeb5StH0120acf/pzPy9OyrJrJJKpZSt0/1OjjrGWtTiM2y19J8oSpIXz1cqrOfi3vLwTdgP1lpX7ZpRa21W3mcNuHOSDaWUDyb5q8HTYQCskFI2AEzCFekmwbxDg6R8BrVW+2UVXlRK+Z1xn3tY6fx1kn/MzKT8FUkeOsGkfOv36H695a/PVQ93kfp1+O+6jGP8xzL2+e6Itl3m2nha4nCN+FRml1k4ZjE7llJ+M91j+MPeuoinUKYxLldix8wsTzD8ul3mT8qfnuTIWusPFjjHfXvLV2Xu2ukLee8ijj8ph/eWf5Xk3eM62SBBeWKv+T6llH7Zs1GeldmDr964Kh0bv35i/IBBWapR+on5T89xjLnK2dw9yU0WOP9aMvbPsNVQSrlNZpfM+c9a64YlHGauG2LXJ3npsjq2Bau17lRrLZteSbZN9+TKXdJdTz6VG+r6b53upvlXSykvm0iHAdYZI+YBmISvJnl9rfVHDc/5liSvG1reIckHSilfSvK2JB8bZzmdQX3gt2XmaMCkSz4+ZEpGHo39PRqMUrxXr/nO84zsXYx+gnGXUsq2SyyB8Z1lnHdU3fGFkhoTjcO1otZ6fSnl5CQvGWq+Uynl7rXWryyw+zH9wyV563w7THFctvbDdE/z/N1C/Syl7JjZEwJ+YwmjXvtG/VzvtsxjrZrBtbt/U+X0WusvxnzqN6crlbPDUNtzM7tMzWaDOH5Wr/msWutykraTMCoxfnR6ZXgGP5NDh5ouzQ11xzekS9BuKovUL5myST+xf02StVyio9Vn2Eo9LrMHBy51UEKZo/2kxUzCzPwGTwJdOHidkeTNpZS7pPsc3TRXyjZJ/rKUslut9Y9HHgiARZGYB2ClrkpXg7Nv2yS7Znb94SQ5IslXSinH1FrfNc7ODTkxyROT3KPXfq/B642llPOSfD7dF/zPJzlzFWu9fyYzEwlJN3r6IStJxJZSnpOuxMFSfKTWOmoiwxbv0W6ZmWhKujjZdQnHWIybJfmfJWzffzR+MUYlLrddYJ9Jx+Fa8pbMTMwn3SSwcybmB8nix/SaT1nEyO9pjctxuSbJ5ekSmucm+VqS/0yyYQnlMHbL7ATZ2Svo07eTbMzMpF1/suRJ2D0za98nyX+P+6S11gsHNcCHyzI9uZTy4lrrFXPs9vAk+/Ta+iPvp1at9fxSyncz84mXozK7Pv5905Vj2uTUTTeEaq2XllK+lhuusfuUUu5Yaz2rd4x+Yv6LDW62jFOrz7CV6t8ouSZzzyMwl1FP/f8iybHL6RALq7WeXko5NN3fsvccWvWCUsoXa63vm1DXANY8iXkAVuqrtdbD51pZSrlZunq3f5bkDkOrtkvyL6WUK5cxUeCS1VqvKaU8JMm7Mnfd2gMGr2MGyxeXUj4+2OeTy6jfOqyflP9hksNqrZev4JhJcvPcMIJpsUaOAm70Hu22wPrVcqOFN7lBq1HMUxCHa0at9TullM9n5u/O40spL6i1XjPHbo9OslOv7S2LON1UxuUK/cd81+ZVMOqmxXImyk2S1Fo3llIuz8yJFucrt9PKqD5c0ujcb8jMxPxO6SZRnyvZ3r9J+4skJ4+hX+P0mcxMzB9ZSim9m5NzlbEZPsbwzc+jkmxOzJdSbpzZk/mu5TI2zT7DVqKUsm1ml6f60jJuiIwaMf/3tdafLq9nLEat9cpSyuPS3YAd/ix7bSnlQ2OccwNgXVNjHoCxqrVeXGt9W7palf2avFsneXspZd9GfbkwyQPSPUr9tUXscrN0E07+W5KzSimPWsHp+19Ybp2uTMFUafAerfYI5DVnwnG41vST6rsmeeQ82z+9t3xZkg8u4jxbfFwuw84j2q5a4TH7+486R2v9OuRJcmWLEw/KNvUn6BxZyqaUckBm11N/Z611VLmSadZPkO+R2Tef55r4da7l/va/mW5wwHz7sPrulm7ui2HLKR/Uz2FcnOQ1y+oRSzJ4+qw/CfWoeQMAWCQj5gFoYjBS+ClJ9kpXymaTm6R7TH2uOrCr3Y+abpLB95ZSDkryW+kmfbxP5h81e/sk7y+lnJjk95dRWuRRg/NuP9T2x6WUnZI8d5pGQY/5PfrliLb31Fofv8JurykTjMO15r1J/iEzkzlPT/Ke/oallP3SJdyGvbvWOirm+sTl0o0qp9JPui1Vf/+5Sra0NOqppv5TGeN0fGaOer9zKeU+tdYv9LZ7dmaPJF4zZWyGbMjskkZHZfCkVynlpkl+Y2jdj2qt5/aO8YV0TwvceLB8eCllm6ERvf2/Ny7LDTXqGZ+DRrR9axnH6Ze4Om5cN6BKKW9Ocsg4jr0IJ9Zap/F3+BNJfq/Xdni6wQMALJHEPADN1FqvK6U8Nd0XseFRiPcvpTyu1jor2Tbm/pyd7pHc45KklHJgksPSfcF4cEbXN35Okh8n+aslnusjpZSHJvlwbkgWJN2Xmx0H9faX/BhwrfXYjLGu6hjeowtHtE1DuYqJaRmHa83g0fn3ZeaErkeXUvaptf6kt/nTMjsxuZgyNom4XI5R5VxuOqJtUUopW2X26PTl1M1ebReNaGv5hMV7010b9hhqe2665HOSzZOh9p8W+UqtdTFP5EyVWutFg0mXhyf+PSrJ3w7+//6ZmbTvl7HZNBDgc7lhFO/O6epinzZ0vGGba9QzVgeOaPv2Ug4wuAH7xF7z95bdo4UdkKWXC1wtN5/QeRdy/oi2/Vp3AmC9UMoGgKYGE52Omnj01YP6oxNTa/1OrfWkWutT030helhGTzT50lLKkiclrLV+Jl0Jk/7IricleV8ppf9o/dRZhffogiT9Ud63WeVurmnjjsM1qJ9c3yrJU4cbSiklM2txJ8lZtdYvL/Ic4nLpLszs92zUiNjFun1mfzcZdcOktQszuxTZnVudfDCfwpt6zY8upQw/WfOYzH7S5o1j7dh49cvKHDb0+bhQGZu52o9KksE1s59oVcamjVHJ26VOhv3XmV2GyE2Vtq4e0TYNZccA1iSJeQAm4Y2ZPcJpvyTPmEBfRqq1Xl9r/Vi60iKf6K2+cZIHLfO4p6Ub8ddPOD0yyUcHk9KtCct5j2qtVyc5o9d8u1LKXuPp5do2rjhcYz6X5Lu9tn4S/ojMTqQvdrS8uFyGwYSN5/Sa71JK2XqZh7z7iLaJj/iutV6b5Ou95ruUUlZatmcpTszM5OMOmTlCvl93/pLMntNlLeknym+cGyYNHU7M1ySfXeQxNtXfPzKzn6yRmG9j1OTXi574tZRyr3Q3ofok5tvac0TbNNxEBViTJOYBaG6Q6HjViFUvHTySPzUG5WVeMmLVskdM1lq/nq6e+P/rrXpAkk+WUtbUyKNlvEezSg8k+Z3V69H6M444nMeokkrLTbau2KCOfj/JfvtSyn2GlvtlPK7L7AnqFiIul65f53ynLP9m0aiEW//4k3Jqb3mbJM3mH6i1/ijJR3rNzy6dO6e7cTfsbYucW2FafT7JNb22o0opt87Mcihn1lp/Pscxzkj3JMwm9xzM6dIfcf/jWuuSyqks01RdVydkVO5hKYMRjpujfWyJ+Vrr4bXWMqHXseP6d63QvUa0/ax5LwDWCYl5ACbl7Um+02u7VZJnTaAvCxn1pX2XlRyw1npWujri5/dWHZbks6WUtVbfeinv0YdHtL2olGLum/mtehzOYdSEmy0nuxzl5HQTQg47JkkGN7L6CfSP11qXWiJBXC7dv49oe85SD1JKuW1mJ/QvzOyR6pPyoRFtL2gcG8f3lg9Il2Qe9X5P44SRiza4qXBar/moLL6MzaYbeqcMNW2Tbt6O/sSvc424X23TeF1trX+zJUluu5gdSylPyuwbUJv0PxsYk0HZuFE3Jb/Yui8A64XEPAATMZhobdSo+ZeUUnZo3Z8F7D2i7YIRbUtSa/1uukR8vxzE3ZOcusbKaCz6PRqU8zm117xf5h4NR2cscTjCqEk9Jzqx22DEcD8J97hSyo2SPC6zR10uuozN0DnE5dJ9KLOf/HloKeXBSzzO69MlTof9Y631V8vu2SqqtX4pyX/2mu+U5JUN+3BKkrN6zX+S5Mm9tlNqrf3PlLWo//v+G0kevcA2ff2nYJ6d2YngVmVspu66OgGj4vIhC+1USrl5ktfNs8mWMNfKtHhmkrv22q7J7FJ7ACySxDwAk/TOzB4FfIssY8TlfEop9ymlPH8FJWKeP6KtX496WQaT4f5mkjN7q349yX+WUm61GudZyATeoz/P7Ikj/6iU8srBiKwlK6X8Winl5FLKrsvZf9ymOQ57vjmi7bfGcJ6lOqm3fJMkj8rsMjY/T/JvyzzHFheXKzFInL9+xKqTSyl3XMwxSimvyuzk3NVJTlhh91bbXDeS/3ipByqlbD9INi7VG3rLR2f2pIsrGi1fStm3lFL7r5Ucc5n6I9m3TjJ8w+fazL5Z0tdPuj90EecZl2m9rrb03yPanlVK2WOuHQY3Xz+Ymcn3fumah69C39a1UspdSyl/spJ5jEopj8nsa1CSnFRrvXL5vQPYsknMAzAxtdaNGT3i8E8HX8ZWy57pRlv9uJRyYinlAaWUbRfaqZSyUynlNUn+V2/VZUk+tlqdG9TIPTzJl3urbpfkc6WU/VfrXPNo+h4NRieP+tn/RZJTSimHLabTpZTdSinPLKV8Ot3Njadkeuv2TnUcblJrvTizb5g9vZTyglLKOErnLNa/Zvao05dmdnmDty93pPUWGpcrdVyS03ttu6d76udxc+1USrlpKeXNSV4+YvWf1lp/uop9XLFa62eT/N9ec0lyXCnl/aWU2y10jFLKbUspL0tXwmw5tfhPTnL5POt/lu73ZD34apJL51n/hcEExHOqtf4gyXnzbHJWrbX/xMdYTPF1taVPJLmq17ZHkg+UUnbrb1xK2TvJp5Lce6j5/yV5Y2/TR5RSzAcyv12S/E2S75dSXltKOWSxOw5uLr8zyXuT9P9m+Z90n8MALJOamQBM2nvTjVK901DbzZP8QZK/XeVz3STdo+zPTnJFKeXrSb6R5PvpEgBXJdkhyS2T3C3JAzO6hveLVntivVrrJaWUo5J8NF2SfpPbpEvOHzWoSz9uLd+jVyW5Q2bXK5eb14gAACAASURBVD083dMC56YrLfKtJBene1z6pkl2TXLHdKUNDsraS3hObRwOOSndl/hNtk6XlDyulPLjdDcF+qMWT6y1jq22da31mlLKu5L8/lDzQSM2XXIZm54tNS6XpdZ6bSnliUn+KzNrZu+R5N2llGPTJYvPS1dne68k90w3ynXU0yP/ltGj8KfBn6Yr43BEr/1RSX67lPLVdCOwf5AuNnZIcrMkv5auRNnBKzl5rfXKUsrJSf5wjk3ePC3lf1aq1rqxlLIhyW/PscliS9B8Jl09/pUcY7VM3XW1pVrr5aWUdyT5vd6qw5J8u5TytnQ3+a5NN8HoM9J9Xm5ybbrSZbfMzN+BkuT9pZT/TvKrJO+ttQ6/z9xgzyQvSjd/yv+km8fj9HQ3PC5N8ot01/Fd01237jn47yiXJXlkrXW+G2gALEBiHoCJGnz5fmW6BP2wF5dS3lhr7Y+uWi07J7nf4LUUf1drffMY+rMp6fLgJB/IzEfc907yH6WUB9ZaW06GONb3qNZaBxO6fTfdiKt+qZDbDV7r2dTF4cAbkjwtM2+YJd3P6FaDV99ySnMs1UmZmZjv+2qtdVTJiEUTl0tXaz27lHJEuqT6nr3Vd0jyZ4s81AeTPGkwcefUqbVeV0r5rXRx+ITe6q2S3GPwGqc3pLtx3Y/L65P805jP3dpnMndivl8/fr5jzFUer3Viflqvqy29NMkDkuzba989yQvn2e/6JE+rtX5u8ITBJemSx5uUJHce/P84PxvXk73SlYda6pwgSfLjJL9Ta/3K6nYJYMujlA0A0+D9mV1jfY8kz1ul41+WrmbxSlyQ5Bm11iXXE16KWuvVSR6Z7j0Ztnu6Uhr9sh2rZSLvUa11Y631z9PdiFhpvfTL0n0hn9Zap2spDn+RbqT+YpNfTdRav5bRdYo36dehX+55tqS4XBW11q+mKzmxnGTnVUlekeQxg2vg1Kq1Xl1rfWK6z6efL/Mw1yW5aJnn/3ZG10X/t8EkySs1qt73t1bhuMsxVyxdmq7UzWKckmTjiPbrMnuy57Ga1utqS7XWi5I8It0TYot1dZIn1FrfPTjGZemeUpmrlFG/tBbde9h/GmM5fpWuJN9BkvIAq0NiHoCJG4yOPHbEqhetYKLM4eNvSLJbuoT3Cem+tC32C8rX0j32e2CtdVWSfgsZlCJ4fJK39lbtkuRTg5I3q33Oib5HtdZP1lrvkuRh6UbNXrzIXb+X5E1JHp1k71rrs6Y1sTfp93ipaq0/qbU+IF1plr9O8u/pamNfmi6pNSlzlaq5Osm7VvNEW0JcrqZa6/dqrUenGxH70cyuJ933vSR/l+SAWuurBvOOrAm11uOT7JfkxenK+CzU943p5hF5eZJ9a60fXcHpRyWl+3W3l6tfpicZ/fk8drXWc5OMutmwYbGxUmu9JN31s++/aq1XrKR/yzHF19Vmaq1nppvg/jVJfrLA5mcnOaLW+r7eMTakKw31ziQXDq3amNkDPbZ4tdYvpXua6QlJ/jndzbbFXm9/meS0JH+U7vPsBSZ7BVg9ZUqfFAWAsSql7JjkwHSJlZunKymybbpRrZelq4d85mBk1hZpku9RKaWk++K+f7pk9m7pBhRckS6B8d0kZ6/12qbicG3ZUuJytZRStktXo/hW6UZi75hupPgFSb45SLyuC6WUXdPVkd8r3b/1RuluTFyc5Nx0E43ON3HrYs+zdbrRxsNlT76X7ubGir/YlVI+mW5U9yZnJLnrtJYXYm0bXFPvlu5zcM90NeWvTjep6Jm11pU+scQchv7+uG26vz92SjcvxlXpJpm+NN2ExWfXWldjtD0AI0jMAwAArAGllIcl+Uiv+c9qra9ZhWNvm652945DzY+stX54pccGAGA2pWwAAADWhv7cK1dnleZWSDdx7XBS/quS8gAA4yMxDwAAMOVKKXdPcnSv+Z211gtW6RT9+vKvWKXjAgAwgsQ8AADAFCulbJPk+F5zTfK6VTzN/Yf+/0u11o+v4rEBAOiRmAcAAJhSpZTbp6srf4/eqnfXWv97lc6xfZJ7DzX9xWocFwCAuZn8FQAAYEqUUk4f/O9WSW6RZLcRm12Z5M611u836xgAAKtqm0l3AAAAgM0OXsQ2fyQpDwCwtillAwAAsDZcm+T5tda3TLojAACsjBHzAAAA06mmK1vz3SSnJDmx1vqdyXYJAIDVoMY8AAAAAAA0pJQNAAAAAAA0JDEPAAAAAAANScwDAAAAAEBDEvMAAAAAANCQxDwAAAAAADQkMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQ0DaT7sB6U0rZJcn9hpp+lOTaCXUHAAAAAIDRtktyq6Hl/6i1XtbixBLzq+9+ST486U4AAAAAALAkj0jykRYnUsoGAAAAAAAakpgHAAAAAICGlLJZfT8aXnj961+fgw8+eFJ9YZ06//zz89SnPnVG28knn5x99913Mh1i3RJrtCLWaEWs0YpYoxWxRitijVbEGq1ccsklecQjHtFv/tGobcdBYn71zZjo9eCDD85hhx02qb6wTu2+++6z2g455JAcdNBBE+gN65lYoxWxRitijVbEGq2INVoRa7Qi1mjlggsuGNV87ajGcVDKBgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAACAId/4xjdy6KGHbn6dc845k+4S65RYoxWxNn22mXQHAAAAAKbJZZddltNOO23z8hVXXDHB3rCeiTVaEWvTx4h5AAAAAABoaE2MmC+l7J/kHklumWS7JJck+XaSL9Rar55k3wAAAAAAYCmmOjFfSnlkkpcnudscm1xZSnlrklfWWi9s1jEAAAAAAFimqSxlU0rZvpTy9iQfytxJ+STZKckfJjmrlPKbTToHAAAAAAArMHWJ+VLKVknek+RJvVXXJ/l+ktOTXNZbt0eST5RS7j3+HgIAAAAAwPJNXWI+yZ8keUSv7cQkt6617ldrvWuSmyX5nSQ/HNrmxkneW0rZpU03AQAAAABg6aYqMV9K2S3Jy3rNL6m1PrfW+tNNDbXWjbXWDyW5T5Lzh7a9ZZI/HntHAQAAAABgmaYqMZ/kxUl2Hlr+zySvmWvjWutPkjyz1/yCQYKfde7yyy9PrXXS3WALINZoRazRilijFbFGK2INAFhrpiYxP6gt//Re87F1gb+uaq2fTfK5oaadkzx2lbvHlLn44otzv/vdL8997nP9Ac5YiTVaEWu0ItZoRazRilgDANaibSbdgSH3STeJ6ybfS3LqIvf95ySHDS0/MskbV6dbTJtLLrkkRx99dE4//fScfvrp2XrrrfOGN7xh0t1iHRJrtCLWaEWs0YpYoxWxBgCsVVMzYj7JQ3rLn15otPzwtr3lw0spO65Cn5hCX/3qV3PmmWduXj7hhBPyvOc9b4I9Yr0Sa7Qi1mhFrNGKWKMVsQYArFXTlJi/S2/5C4vdcTAx7PlDTdslueMq9IkpdPTRR+dd73pXttnmhgc+jj/++Dz/+c+fYK9Yj8QarYg1WhFrtCLWaEWsAQBr1TQl5g/qLZ+1xP372/ePxzry6Ec/Ou94xzuy9dZbb277h3/4h7zgBS+YYK9Yj8QarYg1WhFrtCLWaEWsAQBr0VQk5kspN0py617zj5Z4mP72t19+j1gLHvvYx+btb3/7jD/AX/e61+VFL3rRBHvFeiTWaEWs0YpYoxWxRitiDQBYa6Zl8tfdk5Sh5V8l+fkSj/GT3vKeK+pRklLKnpk5Ie1i7L/S87J4j3/847Nx48Y89alPzfXXX58kOe6447LVVlvlb/7mbybcO9YTsUYrYo1WxBqtiDVaEWsAwFoyLYn5nXrLv1jCxK+bXLXAMZfj95O8YhWOs0U75phj8ra3va3pOV/72tdmu+22y1/+5V82PS+TJdZoRazRilijFbFGK2INAKAzFaVsMjuJfvUyjvHLBY7JFub973//pLvAFkKs0YpYoxWxRitijVbEGgAwbaYlMb9Db/naZRzjmt7yjZbZF9aJm9zkJpPuAlsIsUYrYo1WxBqtiDVaEWsAwLSZllI2/RHy2y3jGNsvcMzlOCHJ+5a4z/5JPrwK5143HvWoR+WAAw4Y2/EvvfTSvO51r9tcRzJJbnWrW+Xd73732M7JdBJrtCLWaEWs0YpYoxWxxjQ699xzU0rJgQceuOh9zjjjjOy+++7ZZ599xtgz1huxRitibW2YlsT8lb3l/gj6xeiPkO8fc8lqrT/PEiehLaUsvNEW5mEPe1ge9rCHjeXYl112WY4++ugZf3jf8pa3zIYNG7LffvuN5ZxML7FGK2KNVsQarYg1WhFrTJvzzjsv97///ZMkp5566qJuHJ155pk58sgjs9tuu+XUU0/N3nvvPe5usg6INVoRa2vHtJSy6SfRb1yWnuHecYFjss5cfvnleeADH5ivfOUrm9v22WefbNiwIfvvv/8Ee8Z6I9ZoRazRilijFbFGK2KN5Tj//PNz//vfPz/5yU/yk5/8JEcccUS+973vzbvPN7/5zRx55JG56KKLcu655+aII47Iz3++pPF8bIHEGq2ItbVlWhLzFyapQ8vbJtlzicfoP2chgtaxK664Ig960IPy5S9/eXPbLW5xi2zYsGGsj8ay5RFrtCLWaEWs0YpYoxWxxnLtsssu2WOPPTYv//jHP87hhx+e73//+yO3P+uss3LkkUfmwgsv3Ny29957Z6eddhp7X1nbxBqtiLW1ZSoS87XWXyb5Ya/51ks8TH/7by+/R0yzK6+8Mg960IPyxS9+cXPb3nvvnQ0bNiypdhYsRKzRilijFbFGK2KNVsQaK7HrrrvmM5/5TO5617tubvvRj36UI444Iueff/6Mbc8555wceeSRM0aRHnbYYfnYxz6WG9/4xq26zBol1mhFrK0tU5GYH+gn0u+4xP0PWuB4rBOf+9zn8qUvfWnz8s1vfvNs2LAht7vd7SbYK9YjsUYrYo1WxBqtiDVaEWus1KYk1sEHH7y57Qc/+EGe85znzNjuGc94Rn72s59tXr7vfe+bj3/849lxx35VXRhNrNGKWFs7pikxf3pv+T6L3bGUsneSfYeafpXkrFXoE1PowQ9+cN761rdmq622yl577ZVTTjklt7/97SfdLdYhsUYrYo1WxBqtiDVaEWushpvd7Gb57Gc/mzvf+c6b26655poZ2wwv3/ve984nPvEJpR5YMrFGK2Jtbdhm0h0Y8rEkfzq0fFQppdRa61w7DHlAb3lDrdXkr+vYU57ylGyzzTY5+OCDc9BB/YclYPWINVoRa7Qi1mhFrNGKWGM17LbbbvnsZz+bI444It/85jfn3O4e97hHPvnJT2bnnXdu2DvWE7FGK2Jt+k1TYv4L6SaB3X2wvF+Sw5NsWMS+z+gtf3j1usW0esITnjDpLrCFEGu0ItZoRazRilijFbHGath9991zyimn5Igjjsi3vvWtWesPOeSQfOpTn8pNbnKTCfSO9USs0YpYm25TU8qm1roxyVt7za8opZT59iulHJnksKGmK5K8d3V7BwAAAKx3e+yxR0455ZRZT1/c7W53y6c+9anssssuE+oZ641YoxWxNr2mJjE/8JokwyVo7peZ5W1mKKXsk+TNvea/r7VeOIa+AQAAAOvcnnvumQ0bNuQOd7hDkuQud7lLPv3pT2fXXXedcM9Yb8QarYi16TRNpWxSa72wlPLqJK8eav4/pZRbJ/nLWutPk6SUslWShyf5+yS3Htr2p0mOa9VfAAAAYP3Za6+9cvbZZ0+6G2wBxBqtiLXpM20j5pNu1PzHem3PTfLDUsp3SylfT3JRkg9lZlL+l0keW2u9tE03AQAAAABg6aYuMT+oNf+YJO/urdo63YSwd01y0966i5L8Vq31tPH3EAAAAAAAlm/qEvNJUmu9utb6hCSPTnL6PJteleSEJHestZ7aom8AAAAAALASU1Vjvq/W+oEkHyilHJDknkn2SbJdkkuTnJ3ktFrr1RPsIgAAAAAALMlUJ+Y3qbWel+S8SfcDAAAAAABWaipL2QAAAAAAwHolMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQkMQ8AAAAAAA0JDEPAAAAAAANScwDAAAAAEBDEvMAAAAAANCQxDwAAAAAADQkMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQkMQ8AAAAAAA0JDEPAAAAAAANScwDAAAAAEBDEvMAAAAAANCQxDwAAAAAADQkMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQkMQ8AAAAAAA0JDEPAAAAAAANScwDAAAAAEBDEvMAAAAAANCQxDwAAAAAADQkMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQkMQ8AAAAAAA0JDEPAAAAAAANScwDAAAAAEBDEvMAAAAAANCQxDwAAAAAADQkMQ8AAAAAAA1JzAMAAAAAQEMS8wAAAAAA0JDEPAAAAAAANCQxDwAAAAAADUnMAwAAAABAQxLzAAAAAADQ0DaT7sB6d/7552f33XefdDdYZ77zne/MajvvvPMm0BPWO7FGK2KNVsQarYg1WhFrtCLWaEWs0crFF1880fOXWutEO7DelFLulOSbk+4HAAAAAABL8mu11m+1OJFSNgAAAAAA0JDEPAAAAAAANCQxDwAAAAAADZn8dcxOPvnkHHLIIZPuBuvMeeedl4c//OEz2j784Q/nwAMPnFCPWK/EGq2INVoRa7Qi1mhFrNGKWKMVsUYrF198cQ499NCJnV9ifsz23XffHHTQQZPuBluAAw88UKzRhFijFbFGK2KNVsQarYg1WhFrtCLWGIcLLrhgoudXygYAAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5AAAAAABoSGIeAAAAAAAakpgHAAAAAICGJOYBAAAAAKAhiXkAAAAAAGhIYh4AAAAAABqSmAcAAAAAgIYk5gEAAAAAoCGJeQAAAAAAaEhiHgAAAAAAGpKYBwAAAACAhiTmAQAAAACgIYl5pt6vfvWrPOtZz8qhhx6a5z3vedm4ceOku8Q6JdZoRazRilgDgOX5xje+kUMPPXTz65xzzpl0l1inxBqtiLXps82kOwDzue666/KEJzwhH/jAB5Ikp512Wq644oqcdNJJ2Wor95VYPWKNVsQarYg1AFi+yy67LKeddtrm5SuuuGKCvWE9E2u0Itamj29lTK3rrrsuT3ziEzcnFDZ529velmc961mptU6oZ6w3Yo1WxBqtiDUAAIDpJjHPVLr++uvz5Cc/Oe973/tGrj/ppJPy7Gc/W2KBFRNrtCLWaEWsAQAATL+pLWVTStknyT2S3HPw30OS7Dy0yQ9qrftOoGuM2fXXX5+nPOUpec973jPvdm9605uy9dZb54QTTkgppVHvWE/EGq2INVoRawAAAGvDVCXmSyn3TfLCdMn4W0y4O0zAxo0b87SnPS3vete7FrX9iSeemK233jrHH3/8mHvGeiPWaEWs0YpYAwAAWDumrZTN3ZP8diTlt0gbN27MMccck3e84x2b23bYYYe89KUvnbHdy172smy77babl9/whjfk+c9/frN+svaJNVoRa7Qi1gAAANaWaUvMz+fKSXeA8dm4cWN+93d/N//yL/+yuW377bfPv/7rv+boo4+ese0jH/n/2bvzaL3r+l70718mhJBgJjAklDFCZIgoB0ohV0KMDB6EFo2CRakWq96LVJbH67XrXnvO8tQB6UEUbI8TULKCB6jCVaQgSYiKIKgMkZQQSCjgxSRkBBUw+d4/otskJjsDe3+fYb9ea/0WPL/xk+RtZL/3s7/PWZk9e3aGDPnDD3xcfvnlufjii6vNS+eSNWqRNWqRNQAAgM7TrsX8uiTzklyS5G1JDkhyRgvnoZ899NBDue6663pe77bbbvnmN7+ZU045Zavnn3322Zk1a1YGDx7cs+8rX/lKlixZ0u+z0tlkjVpkjVpkDQAAoPO0WzH//yY5PMkrSynTSikfLaXcUEp5otWD0b+mTJmSG2+8McOGDcuwYcNy44035rTTTuv1mpkzZ+aaa67JoEGDsueee+aWW27JgQceWGliOpWsUYusUYus0c7Wrl2bUkqrx2AAkDUAoNO01Ye/llIea/UMtM6b3/zm3HDDDT3/viPOPffclFKy33775cQTT+zP8egiskYtskYtskY7WrlyZaZPn57jjjsuX/rSl9I0TatHokvJGgDQidqqmIczztj5FYve+c539sMkdDtZoxZZoxZZo52sWrUqM2bMyP3335/7778/gwcPzhVXXNHqsehCsgYAdKp2W8oGAADocPfdd18efPDBntdXXnllLrzwwhZORLeSNQCgUynmAQCAPjVjxozMnj07Q4b84Qd0v/jFL+aiiy5q4VR0I1kDADqVYh4AAOhzb33rWzNr1qwMHjy4Z9/ll1+eD3/4wy2cim4kawBAJ1LMAwAA/WLmzJm59tprNytML7vssnzkIx9p4VR0I1kDADqND3/tRdM0eycZt5OXHdwfswAAQCd6xzvekQ0bNuRd73pX1q9fnyS59NJLM2jQoHz2s59t8XR0E1kDADqJYr53H0zyiVYPAQAA/en888/P1VdfXfWZl1xySYYNG5ZPfvKTVZ9La8kaAMBGlrIBAABa4oYbbmj1CAwQsgYAtBvFPAAA0BIjR45s9QgMELIGALQbS9n07sok1+/kNQcnuakfZgEAgH5x9tln55BDDum3+69evTqXXXZZz7rfSbLffvvluuuu67dn0p5kjXa0aNGiNE2TSZMm7fA1DzzwQMaOHZsJEyb042R0G1mjFlnrDIr5XpRSliVZtjPXNE3TT9MAAED/OOOMM3LGGWf0y73XrFmTGTNmbFaUTpw4MXPnzs1BBx3UL8+kfcka7Wbx4sU5+eSTkyTz5s3boW8cPfjgg5k+fXrGjBmTefPmZfz48f09Jl1A1qhF1jqHpWwAAIB+sXbt2pxyyim59957e/ZNmDAhc+fOzcEHH9zCyeg2ssauWLp0aU4++eQ8/fTTefrppzNt2rQ8/vjjvV6zYMGCTJ8+Pc8++2wWLVqUadOmZdmynXo/HwOQrFGLrHUWxTwAANDn1q1bl1NPPTX33HNPz7599903c+fO7delTBh4ZI1dtddee2XcuHE9r5966qmcdNJJWbJkyVbPf/jhhzN9+vSsWLGiZ9/48eOz55579vusdDZZoxZZ6yyKeQAAoE8999xzOfXUU/OjH/2oZ9/48eMzd+7cnVrrFLZH1ng5Ro0ale9973s5+uije/Y9+eSTmTZtWpYuXbrZuY888kimT5++2btIp06dmm9/+9vZY489ao1Mh5I1apG1zqKYBwAA+tT3v//93H333T2vX/WqV2Xu3Ll59atf3cKp6Eayxsv1+xJrypQpPfueeOKJvP/979/svPe+97155plnel6fcMIJueWWWzJ8+PBqs9LZZI1aZK1zKOYBAIA+ddppp+Wqq67KoEGDss8++2TOnDk59NBDWz0WXUjW6AujR4/OHXfckaOOOqpn3wsvvLDZOZu+Pv744/Pd737XUg/sNFmjFlnrDENaPQAAANB9zjvvvAwZMiRTpkzJ5MmTWz0OXUzW6AtjxozJHXfckWnTpmXBggXbPO/YY4/NrbfemhEjRlScjm4ia9Qia+3PO+YBAIB+cc45f5qx1gAAIABJREFU5+Q1r3lNq8dgAJA1+sLYsWMzZ86cHH744Vs9fswxx+S2227LyJEjK09Gt5E1apG19qaYBwAAAEgybty4zJkz549++uJ1r3tdbrvttuy1114tmoxuI2vUImvtSzEPAAAA8Dt777135s6dm8MOOyxJ8trXvja33357Ro0a1eLJ6DayRi2y1p7abo35pmlOSLL7Vg5N2eL1K5qmeeM2bvOLUsrDfTsZrXLSSSellNLqMRgAZI1aZI1aZA0Ads0+++yThQsXtnoMBgBZoxZZaz9tV8wnmZVk/x04b58kt2/j2NVJzu+rgQAAAAAAoK9YygYAAAAAACpSzAMAAAAAQEVtt5RNKeWAVs8AAAAAAAD9xTvmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgoiGtHqDbLV26NGPHjm31GHSZRx999I/2LV68uAWT0O1kjVpkjVpkjVpkjVpkjVpkjVpkjVpWrlzZ0uc3pZSWDtBtmqY5PMmCVs8BAAAAAMBOOaKU8vMaD7KUDQAAAAAAVKSYBwAAAACAihTzAAAAAABQkQ9/7WfXXHNNjjnmmFaPQZdZvHhx3vKWt2y276abbsqkSZNaNBHdStaoRdaoRdaoRdaoRdaoRdaoRdaoZeXKlTnxxBNb9nzFfD874IADMnny5FaPwQAwadIkWaMKWaMWWaMWWaMWWaMWWaMWWaMWWaM/LF++vKXPt5QNAAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmaXsvvfRSLrjggpx44om58MILs2HDhlaPRJeSNWqRNWqRNWqRNaDb/OxnP8uJJ57Ysz3yyCOtHokuJWvUImvtZ0irB4De/Pa3v80555yTG2+8MUnywx/+MOvWrcvXvva1DBrk+0r0HVmjFlmjFlmjFlkDutGaNWvywx/+sOf1unXrWjgN3UzWqEXW2o//UqZt/fa3v825557b80Xe71199dW54IILUkpp0WR0G1mjFlmjFlmjFlkDAIBd05bvmG+apklyQJIjk0xM8sokLyRZleTRJPeWUn7TsgHpd+vXr89f/uVf5vrrr9/q8a997WsZPHhw/vmf/zkb4wK7RtaoRdaoRdaoRdYAAGDXtU0x3zTNqCRnJTk1yclJxvZy+ktN03wnyWWllDtrzEc969evz3nnnZdvfOMbvZ735S9/OYMHD86VV17piz12iaxRi6xRi6xRi6wBAMDL0xZL2TRNc0WSZ5J8LcnM9F7KJ8nQbCzx5zVNc3XTNCP7eUQq2bBhQ9797ndn9uzZO3T+P/3TP+XCCy/s56noRrJGLbJGLbJGLbIGAAAvX1sU80mOSzJsK/vXJ3kqyU+SPJhkzVbOeVeS25um2bP/xqOGDRs25Pzzz8+sWbN69r3iFa/Ixz/+8c3O+7u/+7sMHTq05/UVV1yRiy66qNqcdD5ZoxZZoxZZoxZZAwCAvtEuxfymVie5Msmbk4wqpexXSjmmlDIlyZgk05J8f4trjk1yVdUp6VMbNmzIe97znvzLv/xLz77ddtst3/rWtzJjxozNzj3rrLMye/bsDBnyh5WYLr/88lx88cXV5qVzyRq1yBq1yBq1yBoAAPSddirmlyb56yT7llL+91LKLaWUdZueUEpZX0qZl43l/P/c4vqzm6aZVmVS+txDDz2U6667ruf1brvtlm9+85s55ZRTtnr+2WefnVmzZmXw4ME9+77yla9kyZIl/T4rnU3WqEXWqEXWqEXWAACg77RLMf+JJIeWUr5aSvn19k4upaxP8sEk921x6K/7Yzj635QpU3LjjTdm2LBhGTZsWG688cacdtppvV4zc+bMXHPNNRk0aFD23HPP3HLLLTnwwAMrTUynkjVqkTVqkTVqkTXa2dq1a1NKafUYAAA7bMj2T+l/pZTv7MI165um+WyS/7XJ7q2/XYeO8OY3vzk33HBDz7/viHPPPTellOy333458cQT+3M8uoisUYusUYusUYus0Y5WrlyZ6dOn57jjjsuXvvSlNE3T6pEAALarLYr5l2HLtebHNE2zRynlVy2ZhpftjDPO2Olr3vnOd/bDJHQ7WaMWWaMWWaMWWaOdrFq1KjNmzMj999+f+++/P4MHD84VV1zR6rEAALarXZay2VWrtrJvr+pTAAAAUN19992XBx98sOf1lVdemQsvvLCFEwEA7JhOL+YnbGXfs9WnAAAAoLoZM2Zk9uzZGTLkDz8M/sUvfjEXXXRRC6cCANi+Ti/mp27x+olSyostmQQAAIDq3vrWt2bWrFkZPHhwz77LL788H/7wh1s4FQBA7zq9mH/PFq9vackUAAAAtMzMmTNz7bXXblbOX3bZZfnIRz7SwqkAALatYz/8tWma05P8b1vsvqqPn7F3knE7ednBfTkDAAAA2/eOd7wjGzZsyLve9a6sX78+SXLppZdm0KBB+exnP9vi6QAANteRxXzTNKOT/PMWu79VSvlxHz/qg0k+0cf3BAAAGJDOP//8XH311VWfeckll2TYsGH55Cc/WfW5AAC96bilbJqmGZTk2iQTN9m9JsmHWjMRAAAA7eyGG25o9QgAAJvpuGI+ySVJTtti39+UUp5sxTAAAAC0t5EjR7Z6BACAzXTUUjZN03woycVb7P5sKeUb/fTIK5Ncv5PXHJzkpn6YBQAAoKOdffbZOeSQQ/rt/qtXr85ll13Ws8Z8kuy333657rrr+u2ZdL5FixalaZpMmjRph6954IEHMnbs2EyYMKEfJ6PbyBq1yFpn6Jhivmmac5NctsXuq5J8rL+eWUpZlmTZzlzTNE0/TQMAANDZzjjjjJxxxhn9cu81a9ZkxowZm5XyEydOzNy5c3PQQQf1yzPpfIsXL87JJ5+cJJk3b94OfePowQcfzPTp0zNmzJjMmzcv48eP7+8x6QKyRi2y1jk6Yimbpmn+c5Krk2zaev9rkr8upZTWTAUAAEA7WLt2bU455ZTce++9PfsmTJiQuXPn5uCDD27hZLSzpUuX5uSTT87TTz+dp59+OtOmTcvjjz/e6zULFizI9OnT8+yzz2bRokWZNm1ali3bqffzMQDJGrXIWmdp+2K+aZpp2biczKbv7r89yTmllPVbvwoAAICBYN26dTn11FNzzz339Ozbd999M3fu3H5dNofOt9dee2XcuHE9r5966qmcdNJJWbJkyVbPf/jhhzN9+vSsWLGiZ9/48eOz55579vusdDZZoxZZ6yxtXcw3TXNckpuTvGKT3Xcl+fNSyoutmQoAAIB28Nxzz+XUU0/Nj370o55948ePz9y5c3dqXV0GplGjRuV73/tejj766J59Tz75ZKZNm5alS5dudu4jjzyS6dOnb/Yu0qlTp+bb3/529thjj1oj06FkjVpkrbO0bTHfNM1RSb6bZNNv0fwsyemllOdbMxUAAADt4vvf/37uvvvuntevetWrMnfu3Lz61a9u4VR0kt+XWFOmTOnZ98QTT+T973//Zue9973vzTPPPNPz+oQTTsgtt9yS4cOHV5uVziZr1CJrnaMti/mmaQ7NxuVqRm2ye2GSU0opa1ozFQAAAO3ktNNOy1VXXZVBgwZln332yZw5c3LooYe2eiw6zOjRo3PHHXfkqKOO6tn3wgsvbHbOpq+PP/74fPe737XUAztN1qhF1jpD2xXzTdPsn+R7SfbeZPeSJDNKKctbMxUAAADt6Lzzzsu1116bOXPmZPLkya0ehw41ZsyY3HHHHTniiCN6Pe/YY4/NrbfemhEjRlSajG4ja9Qia+2vrYr5pmnGJ7kjycRNdj+dZHop5enWTAUAAEA7O+ecc/Ka17ym1WPQ4caOHZs5c+bk8MMP3+rxY445JrfddltGjhxZeTK6jaxRi6y1t7Yp5pumGZ2Ny9ccvMnu5dn4Tvmtf3QwAAAAQB8ZN27cVn/64nWve11uu+227LXXXi2ajG4ja9Qia+2rLYr5pmlGJLk1yabfvlmd5E2llIWtmQoAAAAYaPbee+/MnTs3hx12WJLkta99bW6//faMGjVqO1fCzpE1apG19jSk1QP8zs1J/tMW+/4xydimad64k/f6SSllVd+MRTs46aSTUkpp9RgMALJGLbJGLbJGLbIGdJt99tknCxd6nyD9T9aoRdbaT7sU8ydtZd9/28V7TUsyb5cnAQAAAACAftQWS9kAAAAAAMBAoZgHAAAAAICK2mIpm1JK0+oZAAAAAACgBu+YBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAioa0eoBut3Tp0owdO7bVY9BlHn300T/at3jx4hZMQreTNWqRNWqRNWqRNWqRNWqRNWqRNWpZuXJlS5/flFJaOkC3aZrm8CQLWj0HAAAAAAA75YhSys9rPMhSNgAAAAAAUJFiHgAAAAAAKlLMAwAAAABART78tZ9dc801OeaYY1o9Bl1m8eLFectb3tLqMQAAgDZ00003ZdKkSa0egy6zta9DZY3+IGvUsnLlypx44okte75ivp8dcMABmTx5cqvHAAAAYICYNGmSr0OpQtaoRdboD8uXL2/p8y1lAwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFCRYh4AAAAAACpSzAMAAAAAQEWKeQAAAAAAqEgxDwAAAAAAFSnmAQAAAACgIsU8AAAAAABUpJgHAAAAAICKFPMAAAAAAFDRkFYPwMC0YsWK/OQnP8nTTz+d1atX5/nnn88rXvGK7LHHHhkzZkwmTJiQiRMnZv/998+gQb5/BAAAQN9YsWJFFixYkCeffDIrVqzIr371qwwaNCjDhw/PnnvumVe96lU55JBDcuCBB2bo0KGtHpcOJmvUImudSTFPNcuXL8/Xvva1fP3rX88jjzyyQ9fsvvvuOfTQQzNlypQcf/zxOeGEE3LEEUf086QAAAB0kx/84AeZPXt2br311jz++OM7dM2QIUNy9NFH5w1veEOmT5+eN77xjRkyRI1C72SNWmStC5RSbH24JTk8Sfn9Nn/+/EIpX//618tee+1VNv292ZVt+PDhrf6ltIWHH374Zf9e2mw2m81ms9lstu7cHn744VZ/ydI2brrppnLUUUf1ye/r3nvvXS666KKyYcOGVv+yWmJrX4fK2h/IWt+Rtd7JWt9ZtmzZ1n5fDi+VemRrhNDvPv7xj+ev/uqvsmbNmlaPAgAAwACwfPnynHXWWTnzzDPz4IMP9sk9ly1bls9//vNZv359n9yP7iBr1CJr3cfPKtCvrrrqqnzqU59q9RgAAAAMEAsXLszpp5+epUuXtnoUupysUYusdae2LOabphmW5LAkBySZkGREkqFJ1iZ5NsmDSRaWUnw7p40999xz+djHPrbd8wYNGpRx48Zl9913z7p167J69WrfqQMAAGCnPfroo5k2bVp++ctf7vA1w4YNy9ixY1NKybPPPpsXX3yxHyekW8gatcha92qbYr5pmrcmeWOSE7KxlN/ebGuappmd5POllH/v7/nYedddd12vf2m87W1vy4UXXpjjjjsuw4YN69m/YcOGLFq0KD/5yU8yf/783HzzzXnmmWdqjAwAAECHev7553PmmWfuUHk1Y8aMvPOd78z06dMzceLEzY6tWrUq9957b+65557cfPPNue+++/prZDqUrFGLrHW5WovZb29L8lR27YMKXkzy90maVv8aig9/3cxf/MVfbPPP7YILLtjh+2zYsKHMnz+/nHvuuWX06NH9OHHn8OGvNpvNZrPZbDabbVvbQP2QxAsvvHC7vzcTJ04s8+bN26n7Pvzww+Xiiy8uu+++e0lSXnrppX76FbQ3H8j5B7LWv2TtD2Stf/nw1979JsmiJPcm+UmSJ7LxN2hTQ5N8IslX6o7G9jzwwAPbPPa+971vh+/TNE2mTp2aWbNmZdGiRX0xGgAAAF1k0aJFufLKK3s9Z/Lkybnnnnvyhje8YafuPXny5Fx66aV57LHH8v73vz9N07ycUelwskYtstb92mYpm9/5RZLvJJmf5EdJlpRSNmx6QtM0o5K8Ncn/k2TTn8t4T9M0PyilfL3WsPRu+fLl2zy2q/+DHzNmzK6OAwAAQJf6x3/8x14/q2z33XfP9ddfn3333XeXnzF+/Ph86Utf2uXr6Q6yRi2y1v3a6R3zpyeZWEp5Xynl2lLKY1uW8klSSllVSvlykqOS/HSLw/+9aZp2+jUNaL/+9a+3eezqq6+uOAkAAADd6sUXX8x1113X6zkXX3xxDj/88EoT0a1kjVpkbWBomxK7lPJgKWXLZWp6O39Vkr/M5kvbjM/GD4+lDYwePXqbx77whS/k3e9+d3760y2/twIAAAA7bv78+VmzZs02jw8dOjQf/OAHK05Et5I1apG1gaFtivldUUpZmI1rz29qcitm4Y9Nntz7H8U111yT17/+9ZkwYUJmzpyZz33uc7nzzjuzbt26ShMCAADQ6X70ox/1evxP//RPX9ZSD/B7skYtsjYwtNsa87visSTHbPJ6bKsGYXNvetObMm/evO2e94tf/CLXX399rr/++iTJ4MGDc/TRR2fatGk566yz8md/9mf9PCkAAACdasGCBb0eP/744ytNQreTNWqRtYGhG4r5V2zxenVLpuCPXHDBBfnkJz+ZX/3qVzt13fr163PfffflvvvuyyWXXJKDDz44H/3oR/Oe97wnQ4Z0Q2QBAADoK//xH//R6/HDDjus1+OllMyaNWunnvnnf/7nGT58+E5dQ+eTNWqRtYGho1vOpmmaJP9pi91bLm1Di4wdOzaf+9znXvaaV4899lj+5m/+Jl/84hfzr//6rznkkEP6aEIAAAA63erVvb8/b9SoUb0eX79+fc4777ydeuaSJUsUWAOQrFGLrA0MHb3GfJL3JNl0QaV/T/LjFs3CVnzgAx/IJz7xiT6510MPPZTjjjsujz32WJ/cDwAAgM73wgsv9Hp8xIgRlSah28katcjawNCxxXzTNO9OcuUmuzYk+T9KKaVFI7ENf//3f5/vfOc7mTRp0su+18qVK3POOef0wVQAAAB0g912263X488991ylSeh2skYtsjYwtO1SNk3TvDrJn2yya2iSUUmOSHJmktdscuzFJO8rpdzRxzPsnWTcTl52cF/O0C1OP/30zJgxI9/85jfz1a9+NfPmzcuLL764S/e69957c+utt+bUU0/t4ykBAADoNHvttVevx1etWlVpErqdrFGLrA0MbVvMJ/lgkou2c05JcmuS/6uU8kA/zdA367CQoUOHZubMmZk5c2aef/753HnnnZk/f37uuuuu3HvvvfnNb36zw/f61re+pZgHAAAg+++/f+65555tHn/kkUcqTkM3kzVqkbWBoWOXsvmd65P8934q5elHw4cPz+mnn55Pf/rTmT9/ftauXZv58+fnox/9aMaPH7/d63v7ywkAAICB4/DDD+/1+Pa+fhwyZEhKKZttc+fO7csR6RKyRi2yNjB0ejE/M8kPmqaZ3zTNIa0ehl03dOjQTJ06NZ/5zGeyZMmSXHDBBb2ev2zZskqTAQAA0M6OP/74Xo/fddddWb58eaVp6GayRi2yNjC0bTFfSvnbUkrz+y3JHkn2S/Kfk3w1ya83OX1qknubpjmmj8e4MhvXtN+Z7cw+nmHA2W233XLFFVdk//333+Y5O7PsDQAAAN3rDW94Q0aOHLnN4y+88EK+/OUvV5yIbiVr1CJrA0PbFvNbKqX8upTyVCnlO6WUv05yVJL7NznllUm+1TTNK/vwmctKKT/fmS3JY331/E730ksv7fK1Q4cOzetf//ptHh89evQu3xsAAIDuMWzYsLzjHe/o9ZxPf/rTWbp0aZ2B6FqyRi2yNjB0TDG/pVLK4iQzkjy5ye4JSf5LayZiS29/+9vz8Y9/PM8+++wuXb969eptHttnn312dSwAAAC6zN/+7d9m8ODB2zy+bt26vP3tb+/160zYEbJGLbLW/Tq2mE+SUsqKJJ/YYvf5LRiFrVi7dm0+9alP5cADD8yHPvSh/PSnP93ha+++++7ceeed2zy+vbW2AAAAGDgmT56c973vfb2e8+Mf/zhTp07NggULKk1FN5I1apG17tfRxfzvfDNJ2eT1vk3TbHtxcqpbt25dvvCFL+T1r399jjzyyHz0ox/NzTffnGXLlqWUstm5S5YsyT/8wz9kxowZWb9+/Tbv+aY3vam/xwYAAKCDfOYzn8khhxzS6zkLFizIlClTct555+Xmm2/O2rVrNzteSsnPfvaz3HDDDf05Kh1O1qhF1rrbkFYP8HKVUlY3TbMyyZhNdr8qyRMtGoleLFiwIAsWLMgll1ySJBkyZEjGjh2b3XbbLStXrsy6deu2e48pU6bkjW98Y3+PCgAAQAcZMWJEbr755kydOrXXJVU3bNiQa6+9Ntdee23PdaNHj87zzz+fVatW9fomMUhkjXpkrbt1wzvmt2bXP3WUqn7729/mmWeeyRNPPLFDpfzgwYNz6aWXpmmaCtMBAADQSSZPnpzbb78948eP3+Fr1q1blyeeeCIrVqxQXrHDZI1aZK17dXwx3zTNiCSjt9j9y1bMQv+78sorM3369FaPAQAAQJs6+uij8+Mf/zgnnXRSv9x/2LBhGTZsWL/cm84ia9Qia92p44v5JG9Osunbp5cn+f9aNAubeNvb3pYjjjiiT+6133775dantigFAAAcXUlEQVRbb93uh14AAADAxIkTM2fOnFx11VXZf/+++Ri6Qw89NJdcckmeeuqp7Lvvvn1yTzqfrFGLrHWfZssP3+wkTdPsnuT+JK/eZPfXSynvadFIaZrm8CQ9H4U8f/78TJ06tVXjtIXHH388//Zv/5a77rord999dxYvXrxD1w0dOjTHH398zj///Lz97W/PHnvs0c+Tdo6FCxfmNa95TavHAAAA2tDDDz+cyZMnt3qMtrF+/fp8+9vfzje+8Y3cdtttva7TvKlx48bl2GOPzYwZMzJjxowB/zXY1r4OlbXNyVrfkLXtk7W+sXz58uy9995b7j6ilPLzGs9viw9/bZrms0muL6XcuxPXjE7yjWxeyq9P8j/6eDxepoMOOigf+MAH8oEPfCBJ8vzzz+fxxx/PkiVL8stf/jLPPfdcnn/++QwZMiQjR47MK1/5yhx66KE58sgj/RgNAAAAL8vgwYNz5pln5swzz0yy8c1jCxcuzFNPPZU1a9bkhRdeyO67754RI0Zk5MiR+ZM/+ZNMnjw5o0dvuWou9E7WqEXWukNbFPNJ3pTkvzRN8+NsLNvnJPl5KWWzD3FtNn7i56FJ3pbkQ0nGbnGf/1FKeajCvLwMw4cPz5FHHpkjjzyy1aMAAAAwwBx00EE56KCDWj0GA4CsUYusdaZ2KeZ/79jfbUnyYtM0TydZneTFJCOS7Pe7f27N1Un+z36fEAAAAAAAXoZ2K+Y3NSzJgTtw3tokH0vyT6WTF8wHAAAAAGBAaJdi/pwkZySZkY3vmB+5nfNLkoeS/EuSq0spy/t3PAAAAAAA6BttUcyXUhYmWZjks03TDEoyKckhSf4kG0v6oUnWJVmTZGmSn5ZS1rZmWgAAAAAA2HVtUcxvqpSyIckjv9sAAAAAAKCrDGr1AAAAAAAAMJAo5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhoSKsH6HarVq3K8uXLWz0GXWblypWtHgEAAGhTK1eu9HUofW5rX4fKGv1B1qhlxYoVLX1+U0pp6QDdpmmao5P8tNVzAAAAAACwU15XSvlZjQdZyqbv7dfqAQAAAAAA2GnVul3FPAAAAAAAVKSYBwAAAACAiqwx38eaptkryRs22fVkkhdbNE6nODjJTZu8PjPJYy2ahe4ma9Qia9Qia9Qia9Qia9Qia9Qia9Qia7tmWDZfvubOUsqaGg8eUuMhA8nv/uBubvUcnaRpmi13PVZK+XkrZqG7yRq1yBq1yBq1yBq1yBq1yBq1yBq1yNrLUuXDXrdkKRsAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoCLFPAAAAAAAVKSYBwAAAACAihTzAAAAAABQkWIeAAAAAAAqUswDAAAAAEBFinkAAAAAAKhIMQ8AAAAAABUp5gEAAAAAoKIhrR4AkixP8l+3eA39QdaoRdaoRdaoRdaoRdaoRdaoRdaoRdY6TFNKafUMAAAAAAAwYFjKBgAAAAAAKlLMAwAAAABARYp5AAAAAACoSDEPAAAAAAAVKeYBAAAAAKAixTwAAAAAAFSkmAcAAAAAgIoU8wAAAAAAUJFiHgAAAAAAKlLMAwAAAABARYp5AAAAAACoSDEPAAAAAAAVKeYBAAAAAKCiIa0eAJqmOTjJsUkmJhmWZFWSf09yVynlN62cDWBHNE3TJDkgyZHZ+HfZK5O8kI1/nz2a5F5/n9EXmqYZluSwbMzbhCQjkgxNsjbJs0keTLKwlLK+VTMCQCdpmubQJFOy8b/h9kjy6yS/TLIoyQOllBdaOB7ADmmaZrckRyeZnGRUkt2z8WuEZUl+mmRxKaW0bkK2pvFnQqs0TXNWkv87yeu2ccpzSa5K8l9LKStqzUV3aZpmQjZ+4+e43/3zmGwssn7viVLKAS0YjQ7XNM2oJGclOTXJyUnG9nL6S0m+k+SyUsqdFcajizRN89Ykb0xyQjaW8tt7Y8WaJLOTfL6U8u/9PB4DUNM0s5O8Y4vd/v8U6BhN04xI8v+3d+fRklXVAca/zSiTIDgBCoIoiooaRRKNMhqjoAwCK5pEEIzRmIBJQCAOKBoSERMhETTKskmUBDVGBZwYRYZEBDFEBhVBhAYEtJHRbpqdP041XXW6ul/Ve/XufVXv+63Vq/uce+5du9eqVffWvufs8xfAW4CtVjF0MfBd4IuZeWITsWn8RMQC4MARXc77qYYSES8C/hLYD1h7FUNvBU6l/Eb4ZROxaWom5tW4zlu8U4E/HPCUO4H9MvOi2YtKkyQiXgb8NSUZv9kUw33w0dAi4uOUH3JrTeP0fwX+IjN/PdqoNKki4hbK7PhhLQGOo7zg9oFPIxERrwW+2ueQ91MNLCLeDxwzg0uclpkHjSYazTcRsSfwaeBJQ5x2R2Y+eZZC0pgbcWL+x5n5zBFdSxMsIlajPOsfwXClyu8ADsrMb8xKYBqKpWzUqM4XxxnAXtWhpcDNlFl+WwEbdh17AvD1iNg9My9rJFCNux2AfdoOQhNtR/on5ZcCt1EedtYEtqT3+wzgTcCzImK3zLxvVqPUJHuI5ffN1SgrNrYAomvMmpTE11OBQ5oOUJMnIjYETmk7Dkmaroj4S+Cj9N4vodxXFwJ3Uco/bMqqV0NKs+WstgPQ2PgkZbJY7QHgBkpZrk2Aren9znsS8JWI2Dszvz7rUWqVTMyraUewYlL+E8AHM3MhPJq83wv4GCXJAKXW3+cj4rmZeU9TwWoi3Qes33YQmiiLgNMppWq+k5n3LjsQEasDLweO7fy9zEsopbr2ay5MjbmFlM/YRcBlwI2Z+Uj3gE55pf2A91Hq5C5zcERcnJmfaSpYTayPsHz1xv3Aei3GIklDiYhDgH+our8OnARcUNeSj4jNKOUK96Y8u0krczzw2Wmc9wzg5KpvwYyj0cTrlLqsk/LXUHJu38rMh7vGPgF4O/Bulk8uWws4LSK2zcxfNRCyVsJSNmpMRGwC3Ehvfe+jM/PvVzJ+c+BiygZ3yxybmTNZ9qp5ICLeCfwjcC9wBXA5pTbk5ZQVGRd0DXfpvYYWEd+jzD74EHB6Zj44xfjVKQ/db60O7ZqZF/Q5RXpURGwPXD1oOZpOgv5cevdwuQ14Sp3MlwYVETsD51NmXD0CHEVJRCzj/VQD61PK5nDgB0NcYmFmXjPSoDTRImIb4GrgMZ2uJcCBmfnvA57/OJNXGrWIOA44uqvr+5m5sj34pEdFxNXAc7u6vgfsnJn3r+KcXYFv0jtJ+28y8+9mJ0oNwsS8GhMRHwbe1dV1EeWLY6UfwojYjZJcWOZeYKvMvHt2otQkiIinUzY9ua7PjNKdMTGvGYqIPYBzMnPxEOesDvw3ZQPiZU7PzEH325AGFhHPBn5I77LVV2Tmd1oKSWMsItahJLSe3uk6Efgy3k81TX0S87tk5oXtRKP5ICLOB3bp6jogM7/QVjxSp1LAz+hd5XhYZp7UUkgaExGxNaVUTbeXZOblA5z7CeBPu7ouy8yXjjI+DWeYzQGkaevcdN5cdb9/qtl/mXke0J1E2AA4YMThacJk5g2ZeY0zQzVbMvPsYZLynXOW0ju7FOBVo4tKWi4zr6WsGOr27DZi0UT4IMuT8jcD72kxFkkaSkTsRW9S/gsm5TUH7E5vUn4JpTymNJVtq/YtgyTlO/6zam8zgng0Aybm1ZSXUjZxXeanwIUDnntq1d57FAFJUgvq2cqbRMS6rUSi+aCeSeMmdhpaROwAvLOr6x1uXC1pzNSlBD/QShRSrwOr9lmZeVcrkWjcbFy1fz7EuTdX7Y1mGItmyMS8mrJH1T5n0Fq5wDlVe+eIcLMxSeOoX23SDRuPQvPFY6r2olai0NiKiDUpEyRW73R9ITPPajEkSRpKZ9+y7hWKV2XmD9uKRwKIiMcC+1TdC1oIRePpnqq9zhDn1mN9GdQyE/Nqyguq9qWDnpiZC4GburrWArYbQUyS1LTN+/S5Z4ZGLiIC2KHqrkvbSFM5Gnhe59+LgENbjEWSpuP3Wf5yEXr3xpDacgC9CdJfAF9rKRaNn6uq9rOHmLz6kqr93RHEoxkwMa+m1HVtrxny/Hq8dXIljaOXV+2fDVurXhrQwcBmXe3r8MFbQ4iI7YB3d3UdmZm3txWPJE1T/ZL6B8v+EREvjIiTIuIHEfGriHggIm6KiHMi4vDObHtpNhxUtT+XmQ+3EYjGT2beQu9k17UZYPJERKxNb3lCWLF0tBq2RtsBaPJFxDrAFlX3MDWw+o2vN7uQpHFwcNV2ZoxGLiIOBE7u6noE+PMhSshpnouI1Sg/1NbqdH0H+FR7EWk+6CQMtgY2oWyCeDewMDMfaDUwjbs6Mf/TiFgfOJEVn8sAtuz82R04NiI+BhyTmUtmN0zNFxGxDfCyqvszbcSisXYk8G2WT7g+NiIWZuZp/QZHxEbAv9E7yfXMzDxzdsPUVEzMqwmPB6KrvYSyVGsYt1btJ84oIklqWES8BnhF1b2ghVA05iLimfS+8F4TeBzwXGAvesu9LQbempnnNRehJsChwG93/r3sM+SLHc2mj1OS8vXeGA9HxBXA14GTM/POxiPTuNumaj8CXAS8cIBz16GU9NohIvbNzHtHHZzmpXrT1ysz8+pWItHYysyLI+LPKffPoOR3F0TEO4AvAdcDD1LycTsCb6R309hzgDc0GrT6MjGvJqxftR+Yxo+7+6e4piTNWRGxMfDJqvvLmWlpEU3HnwGHTTEmgW8AR2fmD6YYKz0qIrYCPtTV9XeZeV1b8WjeWNn+UWtQEgo7AkdGxAnABzJzaWORaWx1Vv9sUHWfxPKkfAJnUVYw3gKs1zn2x/SWg9udMpni9bMYruaBzh5Ab6q6F7QQiiZAZp4SEddTvtee0+negRVXCnX7KXA88KnMfGSWQ9QArDGvJtRJ9IemcY0Hp7imJM1JnR+FnwWe0tV9D26iqNn1BeBvTcprGv6FkpyCsjfBcS3GInVbB3gvcG6nFIk0lQ3pXbkN8Fudv+8GdsrM12XmJzLzrMw8IzOPopRNPb06b9+IqBOq0rB2pXfV42JW/KxJA8vM8ymJ+BOAqV5a39wZd7pJ+bnDxLyaUC9Jnc5Gh7+p2uv0HSVJc89HgFdXfX+amcPutSEN4wDg4oi4qFPLVJpSRBxCmRkKZSbpW92gWrMoKZvXvRt4JeUF9rqU3w6bA6+lrDarJ/XsDPxHRKzeWKQaVyt7gbMU2CMzv9PvYGbeR5k1/63q0N90ZjxL01WXsTkrM+9uJRJNhIh4G3ADcDgw1X1xC8o+VDdFRL89NtQCE/NqQv0wvVbfUau29hTXlKQ5JyIOBf6q6j4+M89oIx5Nhsx8Z2bGsj+URNZTgT0pG3Z2rzJ7OXB5RLy4hVA1RiJiU8osqmU+vbKklTQC3wKelZkvy8zjMvPczLw1Mx/MzN9k5sLODOa3Ac8ALqnO34NS1ktalZX9Zvx0Zv7Pqk7szCZ9O6Um/TLbAjuNKDbNM52VPvtW3QtaCEUTICLWjIgvAqcAm3a6fwkcC7yEsv/UWpSyXK8D/ovyQhxKrflTI+IjjQatvkzMqwn3Ve16Bv0g6hny9TUlaU6JiDcCH6u6FwBHNR+NJlknkXVLZp6dmW8Btgeu6hqyEfDliNionQg1Jj5O+awA3A68q8VYNOEy89LM/NGAY2+hrOS4rDr0nohYd+TBaZKs7DfjpwY5OTN/CpxbdZuY13Ttz/JScQB3UDa2lqbjFHr3vfgu8JzMPCYzL8/MRZm5JDNvy8wzM3NfYG96X1geHhFvbjJorcjEvJpQPxCtO40lgOtVbRPzkuasiNgTOI3euqZfAt4yjc2vpaFk5k8oZSG6yyVtDhzRTkSa6yJif2Cfrq7DMnNRW/FItcx8iLJh4sNd3U8Efq+diDQOMvNBVqy5fC/w/SEu8+2q7Qo0TddBVftzmflwv4HSqkTEzsAhXV2/APbMzNtXdV5mfhV4R9X9kYiwVHSLTMyrCXexfMkMwJqUB+lhbF61fzGjiCRplkTELpSNN9fo6j4HeENmTrUhjzQSmXkXcEzVfVALoWg8dC9lPjszP99aJNJKdF46frXqNjGvqdS/G38y5KaH11ftYX/HSkTE1pTygt0WtBCKJsOhVftjmXnngOcuALpXrG3CiiWW1CAT85p1nZkKN1fdW/Qbuwr1+OumH5EkzY6I2JGSNOgu2XUpsI8bKKoF3bUkATaLiC3bCkZzWneZoz0iIqf6A1xQXWPLPuNe0OD/QfPDeVV721ai0Di5tmr/esjz6/GPm0Esmr/eRO9K2isz8+q2gtH46lSf2LXqPnPQ8zsvJs+uul8x07g0fSbm1ZQ6kb7dkOc/e4rrSVKrImJ7Sp3I9bu6vw+8JjPvbycqzWedUiS/rLqf3EYskjQiP6/aT2glCo2Ta6r22kOeX++P9sAMYtE81Emkvqnq/kwbsWgiPA7YsOq7cchr1OPrChVqkIl5NeWqqv3SQU+MiE2Bp3V1LWHFByxJak1EbEspV9M9i+pa4FWZeU87UUl9LWk7AEmagfo7bM1WotA4ubJqP2nI8+vSNXfPIBbNTzsBW3W1FwOntxSLxl+/l4vD7lVQ30tXn2YsGoE1ph4ijcRZwJFd7d0jIgbcBLGuHXlBZrr5q6Q5oVMa5Fx6f7jdCLxyiFp/0shFxAbAxlX3HW3EojlvL4ZPcD4fOKGrfQfwR9WYn8wkKKmPetWP91lN5WzgEZZPStwqIjbOzHpF2cq8qGrXNeelqRxYtc8c4vMn1fq9HNyM4WbN1zPkvZe2yMS8mnIpZRPYx3faWwM7s2J90n4OqdpfGV1YkjR9nRU95wFP6eq+FdgtM29tJyrpUXvQW8/0TuC2lmLRHJaZ3x72nIioZ2c9lJnnjigkaWV+t2rXpW2kHpn5i4i4hN6NN/cFPj3VuRGxBrBP1X3h6KLTpIuI9YD9qu4FLYSiCZGZiyPiNmDTru5dgVOHuMxuVfuGGQemabOUjRrR2WBiQdV9TKfe2kpFxG70PkTdC3x+tNFJ0vAiYmNK+Zqnd3XfSZkpP2ydP2mkImId4ANV91md+7EkjZ2I2Ah4fdVdbwYr9fPJqn1ERAxSa/5P6F2l8WvgmyOLSvPB6+ndf+p24BstxaLJUd/73tl5kTiliNgJ+J0prqcGmZhXkz4MdJeg2Yne8jY9ImJzVpzJcGJm3jULsUnSwDolQr4BPKerexHwe5l5bTtRaRJFxPERscOQ52wMfBV4Zlf3UuAfRxmbJDXsBGCjrvZiyqbr0lT+Hbi6q/1M4JMRsdJ8SETsCBxfdZ/s3kEa0kFV+3OZOWw9cKn22ar9XODkVX2nAUTENqy4v8GPgctGGJuGFIOV+JZGIyKOBo6ruk8BPpSZCztjVgNeB5wIbNE1biHwnMxc1ESsGm8R8TJgnT6HBqmJu8zCzHSjYa0gIi6glOPq9j6m91BzRWb+asZBaSJFxFWU763vAmcA5wM/zMwl1bgAtgX2Bw5leem4ZU7IzCNmP2LNFxGxM70lCX+WmU9rJxqNk4g4CjgnM68YcPwalAk+f1UdOikzDxt1fJpMnZXY59Bb4u1c4Kjuz2JEbEgppfoBemc6/wh4cWbe20C4mgCdfahupPcz97zM/L+WQtIEiYjzgV2q7ouB9wPf7n4BFBGbUF4SvRfYsDpn/8z84uxFqqmYmFejOkn3rwB7VoeWAj8D7qHsWL5RdfxBSnmIS2Y9SE2EiLgJ2HKGlzktMw+aeTSaNBExypvnLpl54QivpwnSlZjvtpiyl8Gizr83AJ7a+buf04CDLWOjUTIxr+mKiAspK2cvpZSoPA+4rp5F2kmQvgZ4F/CC6jI3ADtmZr9N8KS+IuJI4O/7HLoduAVYj1KicK3q+N2U57Wr6xOllYmI9wLHdnVdkZkvbiseTZaIeDLlPrpVn8P3UV4KPQhsQtnjsV8Z6Y9m5uGzFqQG4uavalRmPhIR+wOfAf6g69DqlC+Lfu4G9jMpL0kSUBIG/R7Ca78GjgI+kc7EkDT3vLTzB+A3EXELZZLOUkoi4Wn0L716O/Bqk/IaVmZ+OCIeAD4KrNl16Mn01pLvdj3w2sz88WzHp4lzYNVe0EYQmkyZeXunXvy/suJK7vWB563i9CWU2fN1uS61wBrzalxmPpSZb6DsTn7VKobeD5wMbOdsUknSPPUGyn4s51IS7VNJ4H+BI4BtMvMUk/KSxsDalJnKvwXsQJmw0++36teA55sk1XRl5j8B21PKwy1ZxdAbgcOA7f28aVgR8buU77RlFrNibW9pRjLz58BuwAHAhcBUq2PvoZSSfl5mftjfCHODpWzUus4GFDsCm1NmAS4CrgUuycyH2oxNkqS5olMO7hnANpQ9WB5LmfF3L+VB+ybgyswcJIEvSa2IiFcC+wIvB55FWTm7KvdRNnn958y8aJbD0zwSEY+lrNp4BqXu8n2U/aeuzMzr24xNkoYVERsAL6a83N4IeAxlYs/dlIk711jacu4xMS9JkiRJalxErAtsRylbsyll+f1qlIk6vwKuAa7OzKVtxShJkjRbTMxLkiRJkiRJktQga8xLkiRJkiRJktQgE/OSJEmSJEmSJDXIxLwkSZIkSZIkSQ0yMS9JkiRJkiRJUoNMzEuSJEmSJEmS1CAT85IkSZIkSZIkNcjEvCRJkiRJkiRJDTIxL0mSJEmSJElSg0zMS5IkSZIkSZLUIBPzkiRJkiRJkiQ1yMS8JEmSJEmSJEkNMjEvSZIkSZIkSVKDTMxLkiRJkiRJktQgE/OSJEmSJEmSJDXIxLwkSZIkSZIkSQ0yMS9JkiRJkiRJUoNMzEuSJEmSJEmS1CAT85IkSZIkSZIkNcjEvCRJkiRJkiRJDTIxL0mSJEmSJElSg0zMS5IkSZIkSZLUIBPzkiRJkiRJkiQ1yMS8JEmSJEmSJEkNMjEvSZIkSZIkSVKDTMxLkiRJkiRJktQgE/OSJEmSJEmSJDXIxLwkSZIkSZIkSQ0yMS9JkiRJkiRJUoNMzEuSJEmSJEmS1CAT85IkSZIkSZIkNcjEvCRJkiRJkiRJDTIxL0mSJEmSJElSg0zMS5IkSZIkSZLUIBPzkiRJkiRJkiQ1yMS8JEmSJEmSJEkNMjEvSZIkSZIkSVKD/h8e4kq3qHER5gAAAABJRU5ErkJggg==\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 357
+ },
+ "id": "WdVTjvL6282f",
+ "outputId": "edda30a6-235c-4663-e522-928b4e4e7a15"
+ },
+ "source": [
+ "#graph showing average variance of all return distributions, at each state\n",
+ "var_=np.copy(pol)\n",
+ "for i in range(var_.shape[0]):\n",
+ " for j in range(var_.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " p=agent.dqn.dist(torch.tensor([i,j]).float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " m=agent.dqn(torch.tensor([i,j]).float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ " variance=np.sum(p*y.T,axis=1)\n",
+ " var_[i,j]=np.mean(variance)\n",
+ "plt.figure(figsize=[12,6])\n",
+ "plt.title('Average variance at each state')\n",
+ "plt.imshow(var_.astype(float))\n",
+ "plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x432 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "RkMeASvD1frO"
+ },
+ "source": [
+ "The next two graphs are obtained with different agents, one with gamma 0.9 and the other with 0.99"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 500
+ },
+ "id": "jEJ-blyFtVIJ",
+ "outputId": "1b47829c-ba05-4c14-8c41-f52d7a67129c"
+ },
+ "source": [
+ "plt.figure(figsize=[15,4],dpi=300)\n",
+ "plt.suptitle('State $[1,2]$ with $\\gamma=0.9$')\n",
+ "pos=torch.tensor([1,2])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('std dev:',np.sqrt(variance))\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "plt.show()\n",
+ "\n",
+ "print(m-8*np.sqrt(variance))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [0.40873522 0.45606893 0.18959813 0.30365545]\n",
+ "std dev: [0.4378094 0.48149902 0.51484907 0.49422762]\n",
+ "Entropy: [1.7736992 1.8976512 2.1443422 2.1005144]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 4500x1200 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ },
+ {
+ "output_type": "stream",
+ "text": [
+ "[-3.09374 -3.3959231 -3.9291945 -3.6501656]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 500
+ },
+ "id": "aUDhV2oZfQbD",
+ "outputId": "5aab51fa-efe4-48c4-a107-39d09138ea2a"
+ },
+ "source": [
+ "plt.figure(figsize=[15,4],dpi=300)\n",
+ "plt.suptitle('State $[1,2]$ with $\\gamma=0.99$')\n",
+ "pos=torch.tensor([1,2])\n",
+ "p=agent2.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent2.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent2.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('std dev:',np.sqrt(variance))\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(agent2.support.cpu().numpy(),p[i])\n",
+ "print(m-8*np.sqrt(variance))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [0.79247266 0.81917477 0.3063814 0.76759624]\n",
+ "std dev: [0.5263818 0.4937571 0.9088148 0.5163132]\n",
+ "Entropy: [1.8954196 1.8433561 2.193319 1.9571303]\n",
+ "[-3.4185817 -3.130882 -6.964137 -3.3629093]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 4500x1200 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/mdp.ipynb b/code/mdp.ipynb
new file mode 100644
index 0000000..d7656c0
--- /dev/null
+++ b/code/mdp.ipynb
@@ -0,0 +1,951 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zp9phBMGIOvD"
+ },
+ "source": [
+ "code for investigating risk-aware action selection on a 4 state MDP"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "U4LL8VVQmz5L"
+ },
+ "outputs": [],
+ "source": [
+ "from gym import spaces\n",
+ "class random_mdp():\n",
+ " def __init__(self):\n",
+ " self.s=0\n",
+ " self.action_space=spaces.Discrete(2)\n",
+ " def reset(self):\n",
+ " self.s=0\n",
+ " return self.s\n",
+ " def step(self,a):\n",
+ " if int(a)==0:\n",
+ " reward=np.random.normal(1,1)\n",
+ " else:\n",
+ " reward=np.random.normal(0.8,0.1)\n",
+ " self.s+=1\n",
+ " if self.s>=3:\n",
+ " isdone=True\n",
+ " else:\n",
+ " isdone=False\n",
+ " return self.s,reward, isdone, None\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "outputs": [],
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBE-ev78Hvmi"
+ },
+ "source": [
+ "## Network\n",
+ "\n",
+ "The parametrized distribution can be represented by a neural network, as in DQN, but with atom_size x out_dim outputs. A softmax is applied independently for each action dimension of the output to ensure that the distribution for each action is appropriately normalized.\n",
+ "\n",
+ "To estimate q-values, we use inner product of each action's softmax distribution and support which is the set of atoms $\\{z_i = V_{min} + i\\Delta z: 0 \\le i < N\\}, \\Delta z = \\frac{V_{max} - V_{min}}{N-1}$.\n",
+ "\n",
+ "$$\n",
+ "Q(s_t, a_t) = \\sum_i z_i p_i(s_t, a_t), \\\\\n",
+ "\\text{where } p_i \\text{ is the probability of } z_i \\text{ (the output of softmax)}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "outputs": [],
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 200), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200, 200), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(200, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "outputs": [],
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.95,\n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 8,\n",
+ " atom_size: int = 51,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim = 1\n",
+ " action_dim = 2\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.tensor([state]).to(self.device).float() \n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ "\n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.tensor([state]).to(self.device).float()\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action\n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ " if not self.is_test:\n",
+ " #print('transition',[reward, next_state, done])\n",
+ " self.transition += [reward, next_state, done]\n",
+ " #print(self.transition)\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 200):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ "\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " def test_(self,printout=False):\n",
+ " self.is_test=True\n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ "\n",
+ " while not done:\n",
+ "\n",
+ " action = self.greedy_action(state)\n",
+ " if printout:\n",
+ " print(state,action)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " self.is_test=False\n",
+ " return score \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "EMEkNhyJHvmk"
+ },
+ "source": [
+ "## Environment\n",
+ "\n",
+ "You can see the [code](https://github.com/openai/gym/blob/master/gym/envs/classic_control/cartpole.py) and [configurations](https://github.com/openai/gym/blob/master/gym/envs/__init__.py#L53) of CartPole-v0 from OpenAI's repository."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Jsi7CspeHvml"
+ },
+ "source": [
+ "## Set random seed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "outputs": [],
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "gw0lyYO0dmaq",
+ "outputId": "63401ec4-cd4d-4713-ac2e-80280445ba03"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ]
+ }
+ ],
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "7EhNf_08dxoM"
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "m_ELje9LE150",
+ "outputId": "f2eb6db6-650f-4af7-f00f-605be3943979"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ]
+ }
+ ],
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "aChSfk4uHvmn",
+ "outputId": "66a797b9-8e79-456d-fc1c-b7fc2472b91c"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ]
+ }
+ ],
+ "source": [
+ "# parameters\n",
+ "env=random_mdp()\n",
+ "num_frames = 100000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay,gamma=0.9)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Uk4YSH1NHvmo"
+ },
+ "source": [
+ "## Train"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 336
+ },
+ "id": "mC5KKHXLHvmo",
+ "outputId": "11118e76-fb10-49f7-841d-a03be989e0b8"
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 3 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "agent.train(num_frames)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ehzu1VRAgNPI",
+ "outputId": "3b151381-19db-4ef4-d82d-e574f2fcd3c6"
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "agent.dqn.load_state_dict(torch.load('normal_mdp.pt'))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "swY9x_K4d1hl"
+ },
+ "outputs": [],
+ "source": [
+ "torch.save(agent.dqn.state_dict(),'normal_mdp.pt')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 815
+ },
+ "id": "jEJ-blyFtVIJ",
+ "outputId": "c0b8bab9-bfcc-4860-ce18-7a44c89956e3"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [2.8445053 2.6744578]\n",
+ "std dev: [1.6104404 1.347314 ]\n",
+ "Entropy: [3.515364 3.3332715]\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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+NTa6fvfyWt0wc7H2Hqpv8dgvv2ucrj49+sHmHeNqNGlQT80PWTXx+MLNWrB+l44dRG8KAAAAICpJMfHoHurZOT/9If7j/LF6anGtVm1t/vFO7Z5Duu6hN/WTK4JtUwEA+VQsKymCH9qHr/Vr2YFW5sxWoWJbKuknki6WNFpSD0nVSqzCOF3SdZI2Bc4ZKulhM+ufRYwt+aykBRl+PZjjGAAgL5bX7tUVv3xB33hgQasJii+cd4w+M6PlHGxrqyl+ymoKAAAAQO6uFyKSFKeMyG2pp1Stln16jbJPABC3YklSdAps12Uxx6HAducsYwmKO7bVks5297Hu/kV3/4u7r3D3Pe5e7+5b3P05d/+2pBGS/idw/jBJt2URIwC0K3X1jfqvJ5fpwluf0Surd7R6/OfOHq1/ayH5kOqc8TU6dlCP0H2PLdyshRt2ZxQrAAAAUG5Wb9uvzbuDH5ck5LofRdAJw/voaso+AUDRKJYkRXB1Qocs5ujYypzZijU2d1/t7rPSmdTdD7r7v0i6JbDr/WZ2QmYhAkD78ca6nbrov5/Vjx9fqrqGxhaPrawwfemdY/XF849Je34z07+9g9UUAAAAQJSoUk8VJp2Yh34UQf/xzrEa2bdr6L6msk8AgHgUS0+KvYHt4OqFdARXJwTnzFYxx9bkS5Leo0RZqCYflfT3HM1/m6R7MjxnlCj5BKDI7K+r148fW6o7nlvVYmPsJpMG9dQNl07WhKPDV0W05LwJ/TVhYA8t3Nh81cQjb27Soo27NX5g5vMCAAAA5SC6H0XPvPWjSNWpulI3XjZZl//PCwrrinrfa+t14aSBOndCritqAwCCimUlRfBD+y5mUdUBIwXT3/lKUoSn2VuWr9gkSe5eL+mngeHzczh/rbu/mcmXpBW5uj4A5MKzy7bqnbfM0e3Ptp6g6FRdoa9fOF73f3Z6VgkKKdmb4lxWUwAAAABB7h6ZpDhlZP5XUTRprezTV++fr537s6n6DQDIRLEkKbZKSv3IqKlBdCYGBbZr2xRR9DyDs5gjX7GlejKwPSaLRA8AlKU7nlulj97+ktZtP9DqsaeNPkqPXnOm/unMkaqqbNufyfMn9I9cLTFzwSYt3kRvCoN6imQAACAASURBVAAAALQ/q7buK1g/iqCWyj5t2XNI33loYazxAEB7VBRJCnc/IGltYHhohtMEj1+cfURHWBLYHpLFHMFzchVbqnWB7SpJvfNwHQAoKb96ZqWuS+PBokenKt142WTddfXJGnZUNovmmjMzfb6FZtv/9eTynFwHAAAAKCUvrtweOl5hidUNcepUXambLp+sqNc873ttvZ5YuDnWmACgvSmKJEVS8IP7CRmeP76V+bK1RlLqq7ddzWxYuicnj+2SMrRPzRMKuXA4ZCz/RRwBoIj9YvYKfe9vi1o97t2TBuqJL56lD5wwRLlehHb+hP4aN6B76L6HF2zUkk17cno9AAAAoNgVuh9F0LRhffSp0yn7BACFUkxJitcD29PTPdHMBkoanjJ0WFJO1uO5u0uaFxhOOzZJpwW25yXnzLUBgW2XFP5XHwDagZ89vVzXz2w5X92/R0f98mPT9LOPTFVN9055iaOiIno1hbv006foTQEAAID2o6V+FKeOirfUU6ovnt9y2acbHslHUQwAgFRcSYq/BrbPzaCnQrBJ9NPunsvm1MHYzsvg3OCxD7UxliinB7Y3JhtqA0C789Mnl+mmR4PV+o704ZOH6vEvnKXzJwZzvLn3zokDNLZ/xGqK+Ru1dDOrKQAAANA+rNq6T7V7ovpRxFvqKVVrZZ/unbteuw+GFbEAALRVMSUpnleigXaTkZJmpHnu1YHtB3MRUIq/BLYvN7NurZ1kZt0lXR4YznVsTYI/g2AjbQAoe+6umx9fqp88vrTF4757ybH6wfsmqUeneJaSV1SYPn9u9GqKW59kNQUAAADah2LqRxHUUtmnuvpGvb52Z8wRAUD7UDRJCndvlHRnYPhbra2mMLNzJJ2RMrRH0t05jm2epFdShrpJujaNU6+VlLpW8EV3z0kZqlRm9lE1T+g8kOvrAEAxc3f95PGlrX7g/4P3TdLHTkm7tVDOvKuF1RR/m7dRb27YFXNEAAAAQPxeiCj1dOygnrG9RNSSL54/Vn26dgjdN3ftjpijAYD2oWiSFEk3SEot03SWpC9HHWxmgyT9KjB8q7tvDTs+5TwPfM1II7ZvBra/YmZntnCNsNj/s5W4Pmhm78+gzJXM7ENq/jN4XdL96c4BAKXO3XXjo0v0X08tjzzGTLrx0sn68MlDY4zsbRUVpn+L6E0hSTe3svoDAAAAKHUt9aM4ZWTh+lGk6lRdqWnDeofum8tKCgDIi6JKUiSTCz8IDP/QzG4zs6ObBsyswswuUaJE1PCUYzdI+nGeYntE0mMpQ9WSHjWzz5tZl5TYuprZNZIeSR7T5GF3b60E0zhJ90paambfN7NTzaxZ1yYz62Bm7zCzByX9XlLHlN0HJX0mT825AaDouLuun7lYP5+1IvIYM+mmy6boAycOiTGy5i44doDGDQhfTfHEolq9xptZAAAAKGMrt+7Tloh+FKcWSZJCkqYODU9SvLZ2hxob+bgFAHKtqJIUSTeoeaPqz0haa2YrzGyupG1KrBRIfR32gKQPuHs+09ofl7QqZbuTpFskbTWzBWb2phJ9NW5O7muyQtKVGVxntKSvKZGE2W1ma8zsDTN70cwWSdqlRM+JiwLnHZb0EXd/MYNrAUDJcnd972+L9D9zVkYeU2HSTz4wRZdNGxxjZBGxVJi+eP7YyP0/fozVFAAAAChfUasoEv0owhMDhTB1aK/Q8T0H67V8y97QfQCA7BVdkiLZm+JySX8M7KpUopn28ZKCfy22SbrQ3Z/Lc2ybJZ0t6Y3Ars6SJkqaoCOTE1Ki9NLZ7r4ly8tWKJGMmSzpZCVWWwSvIUlLJZ3q7vdleR0AKCnuruseWqjbn10VeUyFSTdfcZzed3zhExRNzh1foymDe4bue3b5Vr2wIvzBDQAAACh1UU2zJw3qqe5F0I+iyeTBvVRVEV6Je+4aVj8DQK5VFTqAMO5+UNKHzOzPSvRxOC7i0H2S/k/Sde5eG1Nsa8zsJEnXSPq8pKMjDt2gxCqLW929Ls3p75bUQYkm2McpkfxoSb2kFyT9XNKf3f1wmtcBgJLWlKC48/nVkcdUVphuueI4vXdK1K/pwjBLrKb4+K9fDt3/k8eX6O6RpyqD9kQAUJLMbJSkkyQNVuIeeIekxZKeTz4PFCoukzRVifvxmuTwZiVeVJqbj7KqZjZA0omSRkjqrsQK6e2Slkt6w935RAxAyXP3yBdyiqUfRZPOHSo14egemvfWrmb75q7doQ+eVJg+dwBQrooySdHE3e+VdK+ZjVZiFcEgJR5gdkpaJOm5bB5g3L1Nn/wkkw43mtmPJE2TNEVvP8DUKrF6Ym5yVUgm8y5UosyTzKxS0lglVo8MltRDie99rxIPcKskveLuB9ryvQBAKXrg9fUtJiiqKkw//dDxunDSwPiCysAZY/rqpBF99PKq5m+SvbJ6h2Yv3aIZY2tCzgSA0pfsLfcNJRIBYfaa2Z1KvIi0Nca4qpV4CekaJZ47wrxlZrdI+mlbXxAyswpJH5H0r0o860RxM1so6W+Svu/uu9tyXQAolBVb9mnr3vB+FMWWpJASfSnCkhSvspICAHKuqJMUTdx9uRJvERWVZBLileRXrudukLQw+QUASNqy55Cueyj6V2NVhem/PzxV7zp2QIxRZcbM9MXzjtEVvwxvIfTjx5bqrGP6sZoCQFkxs46Sblfig/mWdJP0OUlXmNll7j4nhtiGSHpQidKyLRks6UdKrPq+2N3XZ3m9cZLuUuKFp1YPV6K07ERJd0giSQGgJEX1o6issKLqR9Fk6rDeoS9GrdiyTzv316lXlw7xBwUAZaroelIAANCSb/1lgXbuD395tbrS9POPTivqBEWTk0cepTPG9A3dN3/9Lj365uaYIwKA/EmuGviTmicoGpRYIfy6pODrqv0kzTSzU/McW42kp9U8QXFA0ptKrOAOrt6eJulpMwv/Rd7y9c6S9JKaJygaJK2T9Kqk15L/BoCyEZWkOLbI+lE0iWqeLUmvrdsZYyQAUP5IUgAASsbM+Rv18PxNofuqKky/+Og0nTehf8xRZe8/zh8bue8njy9RQ2POy54DQKF8SdLFgbFfSBrq7iPd/XhJfSS9X9LalGO6SLrbzHrmMbY7JY1K2T6oRMmnvu5+rLtPkNRX0hd0ZLJijKRfZ3IhM5si6SElSrk2WSTpY5Jq3H2ou5/g7lPdfaiknpIuVKIHHZ+IAShZ7h7ZNPuUkX1ijiY9g3p1Vk33jqH7aJ4NALlFkgIAUBJ27q/TNx58M3L//3vHGJ0zvnQSFJI0ZUivyKTK0s179dd5G2KOCAByz8yOkvT1wPBX3f0z7v6PX3Tu3uju90uaLml1yrGDlUgQ5CO28yVdkDJ0WNI73f1Wd9+fEts+d79Z0ruSxzR5r5mdnea1Okj6vRKNsZvcLGmKu9/l7s0+vXP33e4+090/K+loHflzAYCSsWLL3pLqRyElSrROGxZehmruWpIUAJBLJCkAACXhO39dGPlgM25Ad31mxqjQfcXuC+cdE7nvlieWqb6hMcZoACAvrtWRH8zPkXRD1MHJPg+fCgz/ezLZkWvfDWxf31IPDHefreaxfy/Na31V0oSU7dvc/QvpNuB29wPuHiw7BQAl4YWIVRSVFaYTIhIBxWDq0PDYXl+7k1XPAJBDJCkAAEXv6SW1um9ueG/SCpNuvGyyOlSV5p+08QN76D2TB4buW7V1X+T3DQClINmL4qrA8LfdvcVPdtz9SUnPpAx1l/SBHMc2SdJJKUP7JN2Uxqk3Jo9tMt3MxrdyrRolkhRN1ihRAgsA2oVS60fRZOqw8L4U++oatGTTnpijAYDyVZqf6AAA2o09Bw/r6/fNj9z/T2eO1OTB0U3tSsG/n3eMKix8361PLtOh+oZ4AwKA3JmuRAPsJislzUrz3NsD25fkIqAUwR4Zd7t7q584JY+5JzDcWmyfkJRa2Pym1HJSAFDO3F0vRSQpTi3SUk9NJh7dUx0qwz86o+QTAOQOSQoAQFG7fuZibdgVXt1iZN+u+vdzo8sllYpR/brp/VMHh+5bv/OA/vTKupgjAoCceXdg+/HWVlGkHhvYnmFmXXMQU5NgbI9lcG4wtve0cvzVKf+ul/SnDK4FACUt0Y+iLnRfsTbNbtKpulITB/UI3UeSAgByhyQFAKBovbBim3730trQfWbSDZdNVqfqypijyo/PnzNG1ZXhyyn+66nlOlDHagoAJem4wPbz6Z6YbKq9OmWog47s6ZA1MzNJkwPDaccm6bnA9pTknGHXGiVpbMrQfHffmsG1AKCkvbAifBVFZYXphOHFnaSQovtSzF1DkgIAcoUkBQCgKB2oa9BX7psXuf/jpwzTiSXwUJOuIX266IoTh4Tu27LnkO56cU3MEQFATgR7NSzM8Pzg8S32fsjAMEldUrb3uXt4VjyEu6+RlFquqauk8F/i0omB7Tea/mFmg8zsa2b2opltNLODZrY+uf0DM5uabkwAUKxejGiaPWlQT3XrWBVzNJmbFtHYe/W2/dq291DM0QBAeSJJAQAoSj95fInWbAsv1z2oV2dd+65xMUeUf587e0xkA/Cfz16hvYfqY44IALJnZp0lDQ0MZ1q/Lnj82NCjMhecJ5u6eunGFkxSrLSEz0taIen7kk6WNECJvhVHJ7e/KulVM7vHzAZkER8AFJy7RzbNPqXI+1E0iVpJIUmvrd0ZYyQAUL5IUgAAis5ra3fo9mdXRe6//tJJ6loCb11lakDPTvrYKcNC923fV6c7WviZAEAR6isptQTSYUm1Gc6xPrBd06aIoud5K4s50o1tdGB7t6T/kXSLjmymHeUySS+aWfll5wGUveW1e7VtX3g/ilNHlUaSYkDPTjq6Z6fQfa/SlwIAcqL8PuEBAJS0Q/UNuvbP89QY0Vb1AycM1hlj+sUbVIw+M2OU/vDyWu0P6UHxy2dW6uOnDlfPLtUFiAwAMtYtsL0/g6bZTfa1Mme2gvMEr5OOdGPrFdj+uKTUMk5zJf1R0rLk9jGSPijp+JRjhkl62MyOc/fdWcTajJnVSMr0D+qoXFwbQPsRtYqissJ0QkQZpWI0dVhvbZi3sdk4fSkAIDdYSQEAKCo/e2q5ltXuDd1X072jvv7unPRMLVp9u3XUVacND92352C9fvnMingDAoDsBT+0P5jFHAdamTNbccYWTFI0JSgaJX1O0gnufpO7P5D8ulHSNElfkJSa1BmhxOqLXPmspAUZfj2Yw+sDaAei+lFMHtyzpFZGR5V8mvfWLh1uaIw5GgAoPyQpAABFY+GG3bptVvSH8N9/3yT17Fz+qwj++YxR6t4p/KHtjudWa/fBwzFHBABZCdbGCK/30bJgR9LOWcYSFGdsUcmLL7v7z8JWl3jCzZK+Gdj1MTML9vkAgKJUDv0omkyNWPVx4HCDFm/cE3M0AFB+SFIAAIpCfUOjrr33DdVH1Hl675Sjdd6E/jFHVRg9u1Trn88YGbpvf12Dnly0OeaIACArwdUJHbKYI9izIZsVD2HijC1sfJGkn6RxjeslLU/ZrpL0yTTOA4CCW7o5uh9FqSUpJgzsoY5V4R+hzaUvBQC0GUkKAEBR+NWzq7RgfXiZ7T5dO+jb7y3vMk9BV50+Qn26hn9m9vD8TTFHAwBZCdbuC+862rLg6oTweoCZizO2sPHb3b3V+iDuXi/p14Hhs9KILR23STo2w6+Lc3RtAO3AnKVbQserSqwfhSR1qKrQ5ME9Q/eRpACAtiudAoAAgLJ1oK5Btz29PHL/ty+aqKO6BV9YLW/dOlbpkuMG6dfPrWq2b/bSLdp7qF7dSqiOL4B2KfjhfBczswybZ3dtZc5sBecJXicd6cYWNj47g+sEjz0hg3MjuXutpNpMzjGzXFwaQDsxZ1l4kuL4ob1Kqh9Fk6lDe+uV1c0TEiQpAKDtWEkBACi4Z5Zt0e6D9aH7zh3fX++dPDDmiIrDhZMGhI7X1TfqqcUZfa4EAIWwVUc2fq6WVJPhHIMC27n65RecZ3AWc6QbW1iNvqUZXGdJYLubmeWqNwcA5MWBuga9tCq8afaZY/rFHE1uRPWlWLf9gGr35KoaIQC0TyQpAAAF9/jC8B4L3TtW6fvvO7bdvrk5dWhv1XQPX0Eyc/7GmKMBgMy4+wFJawPDmTZ9Dh6/OPuIjhD84H9IFnMEz4mKbVFg2yVl0mU1rBZiadVJAdDuvLRqm+rqw6vanXlMiSYphkb/6p27ZmeMkQBA+SFJAQAoqPqGRj0R0Qj6gkkD1L9HNmXCy0NFhemCY8NXUzy9pFb768JXnwBAEQl+cJ9pg6HxrcyXrTWSDqRsdzWzYemenDy2S8rQPknrIg5fGDxdmTXqDvtDuD+D8wEgdrMj+lH07lKtYweF93Yodv26d9SQPuEL2V6j5BMAtAlJCgBAQb26Zod27D8cuu+8CeEf0LcnF0wKL3V18HCjZi0Jf/gDgCLyemB7eronmtlAScNThg6r+Qf+WUn2xZgXGE47NkmnBbbntdBrY27IWP8MrhUskdUgaVcG5wNA7KKaZp8+pp8qK0p3lfS0iNUUr64hSQEAbUGSAgBQUFGlnjpXV+qMMX1jjqb4nDi8j/p2C3/h9mFKPgEofn8NbJ9r6dfwOz+w/bS756pxttQ8tvMyODd47ENRB7r7KkkLAsPTMrhW8NilGTYfB4BYrd95QCu27Avdd1aJlnpqEtWXYt76XZHlrQAArSNJAQAoGHfXYxFJijOP6atO1ZUxR1R8KitM75wYvqLkqcW1Oni4IeaIACAjzyvRQLvJSEkz0jz36sD2g7kIKMVfAtuXm1m31k4ys+6SLg8Mtxbb/YHtS1u7Tmpcge1ZGZwLALGLWkUhSWeW+EtIUX0p6uobtXBjWAshAEA6SFIAAApmyeY9Wrs9vKw2pZ7edsGx4SWf9tc1RNb7BYBi4O6Nku4MDH+rtdUUZnaOpDNShvZIujvHsc2T9ErKUDdJ16Zx6rWSuqZsv+jurZWhukOJclVNLjezca1dyMwmS7okMJzTnwMA5FpUkmLcgO6qKfF+c+MGdFfniBepKPkEANkjSQEAKJjH3wxfRVFh0jnjgiW426+TR/ZR7y7VoftmUvIJQPG7QVJqmaazJH056mAzGyTpV4HhW919a9jxKed54GtGGrF9M7D9FTM7s4VrhMX+n61dJFny6ZcpQx0k/dHM+rRwrX6S/iCpKmX4RXef1dr1AKBQ6hsa9ezy8F/XpV7qSZKqKis0ZUh44++5NM8GgKyRpAAAFExUqaeTRvRR767hfRjao+rKCp0fsbLkiUW1OlRPyScAxSuZXPhBYPiHZnabmR3dNGBmFWZ2iRIlooanHLtB0o/zFNsjkh5LGaqW9KiZfd7MuqTE1tXMrpH0SPKYJg+7+5NpXu47klJfL54i6SUzu8jM/pGIMLPq5M/hJUkTUo4/KOlf0rwWABTE6+t2as/B+tB9Z5ZBkkKKLvn0GispACBrJCkAAAWxYecBzV+/K3QfpZ6au2BS+M9k76F6PbusxZeLAaAY3KDmjao/I2mtma0ws7mStinRu2FoyjEHJH3A3XfmMbaPS1qVst1J0i2StprZAjN7U4m+Gjcn9zVZIenKdC/i7rWSPqBEsqHJaCX6WWw1s9fN7PXkte6XNCLluAZJ/+zub6R7PQAohKhST52rK3XC8PAP90vNtIjm2Rt2HdTGXQdijgYAygNJCgBAQTyxKHwVhSSdP6F/jJGUhumj+qpHp6rQfQ/P3xRzNACQmWRvissl/TGwq1KJZtrHS+oV2LdN0oXu/lyeY9ss6WxJwQRAZ0kTlVjNECyi/rqks909o8ZAyVJN75RUG9jVU4mVFVMk9Qjs2y3pEnf/bSbXAoBCmB3x8swpI/uoY1V4L4dSc3zESgpJmrsmnzl1AChfJCkAAAXxWEQ/ivEDe2hIny6h+9qzDlUVkStMHl+4SXX1jTFHBACZcfeD7v4hSZcp8SF/lH2SbpM0Ia7+C+6+RtJJSvSb2NDCoRuUaJx9sruvy/JacySNl/QjSS19mrVdiRUdo909uAoFAIrOjn11mvdW+K+1cin1JEl9unbQiL5dQ/fRlwIAshP+SiYAAHm068BhvbhyW+i+81hFEenCSQN079y3mo3vPliv51ds1YyxNBsHUPzc/V5J95rZaEknSxqkRCPpnZIWSXrO3Q+2MEXUvNbGuOok3WhmP5I0TYlVDU2/WGuVSKzMTa4KaRN33y7pS2b2NSWSIxMl9ZV0WIm+FYslvZyLawFAXJ5dvlXu4fvKoWl2qqlDe2vV1n3Nxl+lLwUAZIUkBQAgdrOW1Kq+MfwJhlJP0U4f01fdOlZp76HmzQhnzt9EkgJASXH35ZKWFzqOoGRi4JXkV76vdVjSc8kvAChpUf0oBvfuHLnyoFRNHdYr9OWhNzfs0sHDDepUXR6lrQAgLpR7AgDELqrU06BenTXx6GApbjTpWFWpc8eHJyIeXbhJhxt44RYAAADxc3fNWRaepDjzmH4ya9Nit6IzNaIvxeEG15sbdsUcDQCUPpIUAIBYHapv0KwlwX6hCedN6F92DzC5dsGkgaHjO/cf1ksrt8ccDQAAACAt2bxHm3cfCt135pjyKvUkScf0765uHcOLk9A8GwAyR5ICABCr51ds0766htB9lHpq3VnH9FOXDuHLxx9esDHmaAAAAIDoUk+VFabpo4+KOZr8q6wwHTekV+g++lIAQOZIUgAAYhVV6qlHpyqdOKJPzNGUnk7VlXrHuIiSTws2qSGi1wcAAACQL3OWbg0dnzq0l3p0qo45mnhMHRqepJi7doc8qoM4ACAUSQoAQGwaG11PLApPUpwzvr+qK/mzlI4LI0o+bdtXp5dXUfIJAAAA8TlQ16CXV4ffg5ZjqacmU4eF96Wo3XNI63ceiDkaAChtfBoEAIjN62/t1JY94bVqKfWUvhlj+6lTdfif8JmUfAIAAECMXly1TXX1jaH7zjymfJMUxw8JT1JIlHwCgEyRpAAAxCaq1FOHqoqyfoDJtS4dqnT22PCSTzMXbFIjJZ8AAAAQk6h+FH26dtCkQT1jjiY+PbtUa3RNt9B9r62leTYAZIIkBQAgNo8v3BQ6fvrovurasSrmaErbBREln7bsOaRX1/LmFgAAAOIRlaQ4fXRfVVRYzNHEa9rQ8NUUc7kfB4CMkKQAAMRiee1erdiyL3TfeZR6ytg7xtWoQ1X4n/GH51PyCQAAAPn31o79kff47WGl9NRh4c2zF27YrQN1DTFHAwCliyQFACAWjy8ML/VkJp0zPrx0EaJ161ilsyIe/B6h5BMAAABiMGfp1sh9Z47pG2MkhTE1YiVFfaNr3luUfAKAdJGkAADEIqrU09ShvVXTvVPM0ZSHCycNCB3fuOugXuehCAAAAHkWVepp3IDuqulR/vf4o/p1U49O4WVr59KXAgDSRpICAJB3tbsP6rV14TfplHrK3jnj+6u6MrzO70xKPgEAACCP6hsa9dyK8JUUUSt+y01Fhen4iNUU89eTpACAdJGkAADk3ROLauUR1YfOJ0mRtR6dqnXGmPAHwIfnb5JH/dABAACANnp93U7tOVgfuq899KNoMmVIeF+KxZv2xBwJAJQukhQAgLyLKvU0uqabRvbrFnM05eWCY8NLPq3feUDz1++KORoAAAC0F1GlnjpXV+qE4eGrC8rRuAHdQ8dXb92ng4dpng0A6SBJAQDIq72H6vXc8m2h+yj11HbnTeivqorwkk8Pzw9PDgEAAABtNXtZeKmnU0cdpY5VlTFHUzhjI5IUjS4tr90bczQAUJpIUgAA8mr2ki2qa2gM3Uepp7br1aWDpo/uG7pv5oKNlHwCAABAzm3fV6d5b4X3XDhzTPi9abkaflRXdawK/3iNkk8AkB6SFACAvIoq9VTTvaOmDA6v34rMXBhR8mnNtv1auHF3zNEAAACg3D27fGtkz7n21I9CkiorTGP6h5ewXbqZJAUApIMkBQAgbw43NOrJxbWh+86d0F8VEWWKkJnzJw5QZWTJp40xRwMAAIByF9WPYnDvzhrRt2vM0RTe2P49QsdZSQEA6SFJAQDIm5dWbteeg/Wh+yj1lDt9unbQKSP7hO6bOX8TJZ8AAACQM+6uZ5aFJynOPKafzNrfi0hjB4SvpFiyiVXNAJAOkhQAgLyJKvXUrWOVTh11VMzRlLcLjh0YOr5y6z4t3UzDPgAAAOTGks17tHn3odB9Z45pX6WemowdEL6SYvPuQ9q5vy7maACg9JCkAADkhbvrsYWbQ/edNbafOlZVxhxReTt/Yn9FvbRGyScAAADkSlSpp8oK0/TR7fNFpHEDukfuo+QTALSOJAUAIC8WrN+tjbsOhu6j1FPu1XTvpBOHR5R8WkCSAgAAALkxOyJJMXVoL/XoVB1zNMWhpntH9eoS/r0vIUkBAK0iSQEAyIuoUk9VFaYZY2tijqZ9uPDYAaHjSzfv1fJaSj4BAACgbfbX1euVVTtC9511TPss9SRJZqax/cNXU7CSAgBaR5ICAJAXTy2pDR0/ddRR6tm5fb5hlW/viuhLIUmPsJoCAAAAbfTSyu2qa2gM3XdmO05SSNEln2ieDQCtI0kBAMi52j0HtWB9+M34ueMp9ZQvA3p20rRhvUP3PTw/fGULAAAAkK5ZES8i9enaQcce3TPmaIpLVPPspZv3yt1jjgYASgtJCgBAzs1eEl6nVpLOptRTXl0QUfJp4cbdWr11X8zRAAAAoFwcPNygB9/YELrv9NF9VVFhMUdUXMYO6BY6vvdQvdbvPBBzNABQWkhSAAByblZEkmJkv64aelSXmKNpXy6YFF3yaeYCVlPg/7N351FyneW9739Pz+pBakmt1iy1JNsajQbbsiUIyAGLQMiBywHOJQM3WZkOuTkHknMhyU3CsCABO+Qw3IRw74EsbsLJYA7HIeQCkTE2Bo+yJdnWaFvqVmuWWupuqefpCNoT8wAAIABJREFUvX9Ud1za/b7dVdVVu2v4ftbqtbzfd9eux0aoq/azn+cBAADIzHdeOK+uvmHv3p71pd3qSZJuC8ykkBieDQDTIUkBAMiqkdExPf6KP0lBFUXuLW+co60rG71732MuBQAAADL0t0+f9q43VFfoZwLVvKWkoaZSyxvnePcYng0AUyNJAQDIqgPtXboxMOLdI0kRj7cHviS+eLZbZ671xRwNAAAACt2hM1168Wy3d+/f37FCtVUVMUeUn8LDs0lSAMBUSFIAALIqNEyvtqpcd63xD3VGdr1tS7jl0/dp+QQAAIA0/e1T/ioKSfqlXatjjCS/rSdJAQAZIUkBAMiqRwPzKHava1J1RXnM0ZSmVQtrtXnZXO/ed2n5BAAAgDRc6x3Sd14MD8xet8g/MLoUhZIUJ6/0aGhkLOZoAKBwkKQAAGTNxe4BHbtw3bt37waG6cXp7YEB2gfbu3Shuz/maAAAAFCoHnzuTPAGO1UUN9uwxP+g0MiYU2tHb8zRAEDhIEkBAMiaH73sb/UkSXuYRxGrt00xvJCWTwAAAEjF6JjTNwIDs5fNq9GbN/AZP9mapjpVlJl37/hF/8NcAACSFACALHr0uL/V022L67W8cU7M0ZS2tYvqg4P7vvcSSQoAAABM70cvX9bZTn8V7s/fvUoV5dxWSlZVURZsf8VcCgAI47cJACArhkfH9JNXO7x7VFHMjtAA7f2nr+ny9YGYowEAAECh+ZvAwOzKctN/uGtVzNEUBoZnA0D6SFIAALLiubZO9QyOePf2rGcexWx4++3+lk/OSf96hGoKAAAAhJ2+2qsfveyvlH7blqVa1FAdc0SFIZSkOE6SAgCCSFIAALLisRP+eRT11RW6c/WCmKOBJN26uEG3NPvLzb9LyycAAABM4b8/0y7n/HsfYGB2UKjl6rmuft0YGI45GgAoDCQpAABZ8dgJ/1NWr79loaoq+HUzW0IDtJ9pvaqOnsGYowEAAEAhGBge1YPPnfHubVw6V3esnh9zRIUjVEkhSS9fopoCAHy4awQAmLFzXf06EfjAfS/zKGZVaC7FmJP2HbkUczQAAAAoBP/8wnl19fmf+v/ArtUys5gjKhzLG+eovrrCu0fLJwDwI0kBAJixUKsniaHZs23j0ga1LKz17n3v8IWYowEAAEC+c87pbwMDsxtqKvTObctijqiwmJluW+xvufoySQoA8CJJAQCYsVCrpw1LGrRkXk3M0SCZmeltt/urKZ48eVWdvUMxRwQAAIB89sLZbr10rtu79547Vqi2yl8lgNesXzLXu04lBQD4kaQAAMzI4Mionni1w7t37waqKPLB2wMtn0bHnB4+RssnAAAAvOZvnmoL7v3iPQzMTkVoePaJSzfkQtPIAaCEkaQAAMzIc22d6hsa9e7tuW1RzNHAZ8vyuVoxf45373sv0fIJAAAACdd6h/QvL/o/H77hliatW+RvY4SbhYZnd/UN6/KNwZijAYD8R5ICADAjjx73z6NoqKnQjtXzY44GPmamtwdaPv3k1Q519/uHIgIAAKC0PPjcGQ2NjHn3fmkXVRSpClVSSLR8AgAfkhQAgBl5NDA0+423LlJlOb9m8sXbtizxrg+POj1CyycAAICSNzrm9I2n/QOzl82r0Ztp5ZqyxtoqLZ5b7d07cfF6zNEAQP7j7hEAIGNnrvXp5JVe796b1tPqKZ9sXdGopYEh5t996WLM0QAAACDfPHbiss529nv3fv7uVargAaS0MDwbAFLHbxgAQMYeC1RRSMyjyDdlZaafCVRTPP7KFfUMjsQcEQAAAPLJ3zzlr6KoLDf9h7tWxRxN4Vu/2D+/4+VLJCkAIIokBQAgY4+duOJd37J8rprn+p/ax+wJzaUYGhnTDwOzRQAAAFD8Tl/t1Y9e9n+2f/vtS7Wowd+6CGGhSopXLvVodMzFHA0A5DeSFACAjAwMj+qJkx3evT230a82H92xar6aA18wv/fShZijAQAAQL4IzaKQpA8wMDsjoeHZgyNjarvqb5kLAKWKJAUAICPPtF7TwPCYd+/eDbR6ykdTtXx69MRl9Q3R8gkAAKDU9A+N6sHnznr3Ni6dqx2r5sccUXG4pbleZebfO8FcCgC4CUkKAEBGQvMoGmsrtW0lX2Ty1du2+Fs+DQyPBdt3AQAAoHh958Xz6u4f9u59YNdqmQXutGNKNZXlammq8+4xPBsAbkaSAgCQkdAN7Z+6dZHKQ48MYdbtXLNAC+uqvHvfpeUTAABASXHO6W8DA7Mbair0zm3LYo6ouIRaPp24eD3mSAAgv5GkAACkra2jV60d/j6q966n1VM+Ky8zvTXQ8umHxy9rYHg05ogAAAAwW05cuqGXznV7995zxwrVVlXEHFFxWb/YPzybdk8AcDOSFACAtIVaPUnSG28jSZHv3h5o+dQ3NKqnTl6NORoAAADMlp+80hHc+6V7GJg9U+uX1HvXT1/rYx4cACQhSQEASNujgVZPW1fMU1N9dczRIF13r12gxtpK795Tp0hSAAAAlIonAw+o3NUyX2sX+W+wI3Xrl/grKZyTXr3cE3M0AJC/SFIAANLSPzSqpwM3svesb445GmSisrxMb7ilybtHJQUAAEBpGB4d0zOBz/VvuIXq6GxYtaBWNZX+W28MzwaA15CkAACk5elTVzU4Mubd28M8ioKxa91C7/qR893q7h+OORoAAADE7aVz3eod8s8j232L/7Mi0lNeZrptcWh4NkkKAJhAkgIAkJZHA/MoFtRV6XUrGmOOBpnatdb/xXPMSc+2Xos5GgAAAMQtVEE7p7JcW/lcnzXrSVIAwLRIUgAAUuac02OBeRRvum2Rysss5oiQqTVNdVo81z8/hJZPAAAAxe/Jk/6h2XetWaCqCm4XZcv6Jf4kBe2eAOA1FbMdQCrMbJ2knZJWSKqS1CnpuKQnnXMDsxiXSdohaZukiUbslyS9IOmAc87NVmwAkAunOnrVfq3Pu0erp8JiZtq1dqH+6dD5SXsMzwYAAChuA8Ojeq6t07u3O9AWFJnZEBie3dEzqKs9g1pY739wCABKSV4nKczsXZL+WIlEgE+PmX1d0iedc/5HAHITV6WkD0n6sKTlgdPOmtkXJH3JOZf15t5m9puSvuLZWuOca8v2+wGApGAVhZn0xltJUhSaXev8SYpjF66rs3dI8+uqZiEqAAAA5NrB9q7gnDmSFNl125L64N6Jize0+xaSFACQl/V7ZlZtZt+Q9JDCCQpJqpf025KOmtkbY4ptpaRnJP2ZwgkKKVH18TlJT5nZVOdlEsMKSQ9k85oAkIrHAvMotq1s5IZ2Adq1tim490wr1RQAAADF6qlAq6eGmgptXjYv5miK26L6ai0IfFc6cYmWTwAg5WGSwszKJP2jpF+IbI1KapV0SFJ3ZG+RpO+Z2a4cx9Ys6VFJ2yNb/ZKOSDomKdp+6g5Jj5pZ+E5Q+v5Kkr9eEABypHdwRM+c8g9Uvnd9s3cd+W3lgjla3jjHu8dcCgAAgOL1ZOCz3j1rFzJnLsvMjOHZADCNvEtSSPqIpHdG1r4iaZVzbq1zbrukBZLeLak96ZxaSQ+aWS5T/l+XtC7peECJlk9NzrktzrlNkpok/a5uTlbcKumvsxGAmf28pHeMH/Zm45oAkIofv3JFQ6P+knCSFIXJzHTPWn85P3MpAAAAilPv4IgOneny7tHqKTcYng0AU8urmRRmtlDSH0aW/8A599nkBefcmKSHzOxZST+R1DK+tUKJBMHHcxDbXklvS1oalvRW59zjkdh6JX3ezA5IelhS5fjWz5nZvc65R2cQQ5OkLyYtfUzSn2d6PQBIx74jl7zrTfXV2ryM4q5CtWvdQn3rwNlJ6y9f6lFHz6CaGOQHIEfMbJ2knUp8hq+S1CnpuKQnnXPR6uQ44zIlWs5ukzSRhb8k6QVJB5xzbrZiA4Bs2N92TSNj/r/Kdq/LZhMITNgQSFK8fOmGxsacyqheAVDi8q2S4qOSkv/mflzS/aGTnXPnJP1aZPl3xpMd2fapyPFnowmKZM65H2ly7J+eYQxfUqJSQ5Ke180JCwDImeHRMT1y3D+P4r5NzXyoLmC7pnha7mmqKQDkgJm9y8yel/SqpL9TYtbapyX9paRHJF0xs/8ry+1SU4mr0sz+D0lnJD0n6auS/nT852vja+1m9l/MrDJ8Je+195iZm8FPW5b/dQGUsFBbz4V1VbptcXjIMzIXqqToGxrV2c7+mKMBgPyTN0mK8VkUvxJZ/sR0Tyo55x6R9OOkpQZJ78tybLcr8ZTXhF4lBmdP5wHd3JJpt5ltzDCGd0h6//jhqKRfd86NZnItAEjX/tZr6u4f9u7t3bQk5miQTcsb52jVglrvHnMpAGSTmVWb2TckPaREpUJIvaTflnTUzN4YU2wrJT2jxGf85VOcukLS5yQ9ZWZTnQcAeSs0j2LXuoVKFJMh224LzKSQpOMXr8cYCQDkp7xJUkjarcQA7AmnJD2W4mu/Fjl+VzYCShKdkfGgc27axoHj53wzspx2bGY2V4lh2RO+4Jw7mO51ACBT+476Wz3VVZVP+SQ+CsMu5lIAyLHxB5L+UdIvRLZGJbVKOiSpO7K3SNL3zGxXjmNrlvSopO2RrX5JRyQd083z5iTpDkmPxl3tAQAz1d03rMPno3/dJtDqKXfqqiu0csEc7x7DswEgv5IUPxs5fjiNfq8PR473mFldFmKaEI1tXxqvjcb2Du9ZU3tAiae2JKlNiVkUABAL55z2Hbno3duzvlk1leUxR4RsCyWaTl3p1aXrs9YWHkBx+YgmP/jzFUmrnHNrnXPbJS2Q9G5J7Unn1Ep60Mzm5TC2r0tal3Q8IOnDkpqcc1ucc5uUaLn6u7o5WXGrpL/O8D3/VtJ9afxEkzsAkJGnW68qdKeFodm5tX6xf47f8UskKQAgnwZnb4scP5nqC51z58f7tLaML1VJ2iRp/0yDGh+c97pMY5P0ROR4q5lZqgkYM3uTpN9IWvqgc64vjfcHgBk5cv66znf7b1Tv3bw45miQC9PNpXjnNjqaAMjc+Ly4P4ws/4Fz7rPJC865MUkPmdmzkn6i1z7br1AiQfDxHMS2V9LbkpaGJb01OnvOOdcr6fNmdkCJh5AmZlL8nJnd65x7NM23PuWc+0GmcQNApkLtPJfNq9Hqhf4WoMiODUsa9INjkyvUX6aSAgDyqpIiOqvhaJqvj56f0ewHj9VKPME1odc51x46Oco5d1pSclKhTtLKVF5rZnMk/TdJE00h/9459/1U3xsAsiFURVFRZtqzvjnmaJALi+fWaO0ifwEicykAZMFHlZgbN+FxSfeHTnbOnZP0a5Hl3xlPdmTbpyLHn40mKJI5536kybF/OutRAUCOPHmyw7u+a10T8yhyLDQ8+1RHrwZHGDkKoLTlRZJi/Gb8qsjymTQvEz1/feYRTXmddOPyvSbV2D6pRBm5JF1TouwcAGIVmkexa91CzZtT6d1D4WEuBYBcGJ9F8SuR5U9MV1XsnHtE0o+TlhokvS/Lsd0uaWfSUq8Sg7On88D4uRN2m1m2HpACgJy5cmNQL1/q8e7R6in3NgSSFKNjTicv93r3AKBU5EWSQoker8kp+2FJl9O8xrnIcbYe741e52wG10g7NjO7Q4my9gkfcc6l+98kK8ys2cw2p/Ojm/v6AihQp6/26nig/HjvJlo9FZNQy6fTV/t0rqs/5mgAFJHdSgzAnnBK0mMpvvZrkeN3ZSOgJNEZGQ8656btuTF+zjcjy9mODQCybqqHT6Zq/4nsaGmqU1W5/zbciUvXY44GAPJLvsykqI8c96UxNHtCNO0cvWamotfJJL2dVmxmVqnEl7KJabSPOecyHcqXDb+lHPQABpD/Hg5UUUjSW0hSFJV7ApUUUqLl03vuWBFjNACKyM9Gjh9O43P+w5HjPWZWNz4fIhuise1L47UPS/rlpON3SPrMTAMCgFx6KtDqaU1TnZY1zok5mtJTWV6mdc31OnZhckIi9GAYAJSKfKmkiN60909onVr0Mc9cJSniiO33JG0d/+dBSb+ZwXsCwIztO+JPUmxdMU9L5/FFppg01VfrtsX+X0/MpQAwA9six0+m+kLn3HlJbUlLVZI2ZSEmWaLx+usiyynHJumJyPFWo5k7gDz3ZOAzHVUU8Vkf+Lx9giQFgBKXL0mKmsjxUAbXGIwcZ+vuWayxjfez/aOkpU87517O4D0BYEY6egb13Olr3r29m5fEHA3iEJpL8fSpq0q/wBEAJEnRWQ1H03x99PxszX5YLak26bjXOdee6oudc6cl9SUt1UlamU4AlrDWzO4xs9eb2UYzm5/ONQAgVWc7+3T6ap93j3kU8Vm/ZK53nSQFgFKXL+2eotUJVRlco3qaa2YqttjGBwt+Len8I5Luz+D9su3Lmtx3dzrrJH07B7EAiMkPj13WWOC+NPMoitOudQv1/z51etL6ua5+nbnWr1ULaz2vAgA/M5sjaVVk+Uyal4mevz7ziKa8TrpxTbwm+TrrJaWa6PjfJP0nSQuiG2Z2XNIPJX3ZOXckg7gAYJKpKmOnavuJ7AoNz77QPaDuvmHNq62MOSIAyA/5kqToiRxHqxdSEa1OiF4zU3HG9p8k7Rr/ZyfpN5xzwxm8X1aND+xOa2g31e5A4dt39KJ3fU1TnW5pzlZHPeSTu9cslJnkK5p46lSHVi2M3msEgCk1SUr+UDisND9TSjoXOW6eUUTh65zN4BrndHOSIp3YWqbY2zD+80Ez+5ak33TO+UsbZ8DMmnXzUPNUrMt2HADiEUpSbFjSoKb66HOVyJX1gSSFJL10rltvuLUpxmgAIH/kS7un6E372gx6utZNc81MRa8TfZ9UTBubmbVI+pOkpa8459LpiwsAWdM3NKIfv+IfrLd302ISkUVqfl2VNgRK0JlLASAD0Yx2XxpDsydEh2Tnau5cJsO4cxXbBJP0HkkHzSwrszgifkvS4TR/qJQGCpBzjnkUeWLpvBotrPM36DjQ3hlzNACQP/IlSdGhROXAhEql/5TU8shxuk9phUSvsyKDa6QS2yf0WjLjvKTfz+B9ACArHn+5Q4MjY969vZtp9VTMQnMpnmIuBYD0RW/aZ9KOtX+aa2ZqtmJ7WdJ/lfROSbdImqvXvvu8QdInJUVLGVdJ+q6Z8QsYQEZaO3p18br/r7nd63hyP05mpu2rGr17JCkAlLK8SFI45/o1uX9ruj0loucfzzyim5yIHKc1EC/wGl9syb+llknqNjM33Y/nOq2Rcz6cQbwASlyo1VNTfbW2rWSmZzELPU136fqgWjsyedAYQAmLtkkdyuAag5HjaBvVTMUdW5uke51z651z/8U598/OuZPOuRvOuRHn3BXn3BPOuU9IWiPp/468frUSc+IAIG2hKooyk3aumTQaBzm2fZX/+9TB9i4eCgJQsvJlJoWUuHG/Oul4k6T9abx+o+d62XBaiaekJr501JnZaufc5MmiHma2WlLypNFeZTaYDwBiMTI6pkeO+YvR7tvUrPIyWj0Vs51rFqjM5B2a/tSpq1q7iHkkAFIWfWzX399iatFG6ZlUPPjEGptzrk2JRMW0nHMDkv6jmfVLSn7g6N1mdqdz7rk04wz5sqRvpvmadaLlE1Bwnjzpb+N6+/J5mjeHQc1xC1VSdPcP61RHr9bxeRtACcqLSopxhyLHu1N9oZkt1c3D54YlHc1CTBrvm/tiZDnl2CS9PnL8Yga9eAEgNs+2XVN3/7B3b++mJTFHg7jNm1OpzcvmefeYSwEgTdE5bNHqhVREqxNyNXcun2Kb8BFJr0bWfjFbF3fOXXbOHUnnR9LJbL0/gHiMjbngZ7hdtHqaFVtXNCr03NeB07R8AlCa8qmS4l8k/V7S8VvMzFK8ob83cvyocy6bXxL+RdLdScf3Sfr7FF97X+T4O4HzPibpL9KMS5Iejhz/oqRLScfRdlUAMKV9Ry551+uqyhmsVyJ2rVuol851T1p/+tQ1OecYnA4gVdHP47VpfL6fUBc5zlWSIvo+qchVbJIk59yImX1J0peSlqPfewBgSscv3lBnn/8BpN18tp8VddUV2rBkro5euD5p70B7l957ZyZdxgGgsOVTkuJJJQZoT6Ty10raI+nRFF77q5HjbJcg/7OkTyUdv9fM/vN0iRAza5D03lRic85FqzVS4rlR9MR4OTkApM05p4eP+pMUe9Y3q6ayPOaIMBt2rV2o/+fxU5PWO3oG9erlHt26uGEWogJQgDokOUkTH1gnBkT7f9H4LY8c+/sRpi96nRUZXCNXsSV7JHJ8awaJHgAlLNTqqbLcdGcLs+Zmy47Vjd4kxUGGZwMoUXnT7sk5Nybp65Hlj9s0j2ua2Zsl/VTS0g1JD2Y5thd183yMekkfTeGlH9XNT1g97ZzLShsqAMiFI+ev61xXv3dv7+bFMUeD2XLXmgXB2SNPnaLlE4DUOOf6JbVHlleleZno+dmaOxetNs7ksdXoa7IVW7LoLLsKSdxVBJCyUKun7Svnq7Yqn55bLS3bV/r/Kn/50g31DI7EHA0AzL68SVKMu183l0m/STe3gLqJmS2X9NXI8hedc/5HBV57nYv87Ekhto9Fjn/fzN44xXv4Yv+jFN4HAGbNvkAVRUWZac/65pijwWypr67Q7cuZSwEgK6I37jel+fqN01wvU6clJWfl68xsdaovHj+3NmmpV5MTCtng69HClFsAKRkZHdMzrde8e7RxnV07VvuTFGNOeuFMV8zRAMDsy6skxXhy4U8jy58xsy+b2bKJBTMrM7N3KdEiqiXp3POS/jxHsX1f0r6kpUpJ/2pmHzKzf/uCYmZ1ZvZhSd/XzV8gvuuci5ZrA0Be2Xfkonf9nrULNW8O90RKSeiL69OnrmpsjC4jAFJ2KHK8O9UXmtlS3fxZf1hSVqqSx9slRdutphybpNdHjl/MUQumJZFjJ4lsMYCUvHSuO/hUPvMoZlfLwlotqKvy7jE8G0Apyqskxbj7lRhUneyDktrN7KSZHVDig/lDurn8u1/S+5xzuUw5f0BSa9JxjaQvSOows8NmdkSJ3rufH9+bcFLSL+cwLgCYsfarfTp+8YZ3j1ZPpWfXWv8X186+YZ245P9zAgAe0c/1b5munWuS6JDoR6ebCZemaGz3pfHa6LnfmWEsIW+IHF9wztEHBEBKngxUwNZUlmnbqsaYo0EyM9P2lf7/DQ5SSQGgBOVdkmJ8NsV7Jf1DZKtciWHa2yVF/ya/KuntzrknchzbJUn3SnohsjVH0mYlytdrInuHJN3rnLuSy9gAYKb2HfVXUUjSWzaSpCg1d7bMV2V5YC4FLZ8ApO5JJR7imbBW0p4UX/urkeNvZyOgJP8cOX6vmdVP9yIza1Di+0qybMc2IfrfgMpsACkLfWa7q2WBqivKY44GUdsDiaKD7Z3KTXEeAOSvvEtSSJJzbsA5935J79HkEvFkvZK+LGmTc+6xmGI7LWmnEvMmzk9x6nklBmff7ZzLRX9aAMiq0DyK162Yp2WNc2KOBrOttqpCW1f4vzgxPBtAqsYfQPp6ZPnj01VTmNmbJf1U0tINSQ9mObYXJe1PWqpX4vP7dD4qqS7p+GnnXFbaUCUzs1/U5ITOP2X7fQAUp8GRUe1v88+j2L2uKeZo4LNjlX8uRWffsFo7emOOBgBmV8VsBzAV59y3JH3LzG6RdLek5ZKqJHVJOibpCefcQAbXTbXEPPT6IUkPmNnnJN0haaukiYmyl5VIrBwY/1KWUzP9dwEASbraM6jnAl9i9m6iiqJU7Vq3UM95euI+c+qqRsecysv4FQQgJfdL+o9KJAEk6U1KPPDzWd/JZrZc0lcjy18cn18XZGbRx07vTeFBpo9J+l7S8e+b2Q+cc48H3mMi9mR/NE1c/6ukIUkPpTq3wszer8n/DQ4p0fIWAKZ1sL1LgyP+WxLMo8gPW1c2qswSw7KjDrR3ae2iaYv7AKBo5GUlRZRz7lXn3H93zj3gnPu0c+4vnHOPZJKgyHJcY865/c65rzrn/nT856vOuefiSFAAQLY8cvyy98OxJO3dHJ3ZiVIRGp59fWBExy5cjzkaAIVqPLnwp5Hlz5jZl81s2cSCmZWZ2buUaBHVknTueUl/nqPYvi9pX9JSpaR/NbMPmVltUmx1ZvZhSd8fP2fCd51z07Vg2iDpW5JeNrM/MbNdZlYXPcnMqszsp83s25L+TlJ10vaApA/maDg3gCIUmkfRUFOhzcvmxhwNfOqqK7R+if9/i4PtDM8GUFoKIkkBAMitfUf8rZ5aFtbq1mae4ClVO1bNV1WF/6MCcykApOl+TR5U/UFJ7WZ20swOKDFn7iFJq5LO6Zf0PudcLqeIfkBSa9JxjaQvSOows8NmdkSJuRqf183z505K+uU03ucWSf+nEkmY62Z22sxeMLOnzeyYpG4lZk78u8jrhiX9gnPu6TTeC0CJe+qkv/js7jULVVHOraB8sSMwl+JAO8OzAZQWfjMBQInrGxrRj1+54t3bu3mJpmkbjiJWU1ke/OLEXAoA6RivMn6vpH+IbJUrMUx7u6ToXzhXJb3dOfdEjmO7JOleSS9EtuZI2ixpk25OTkiJ1kv3Ouf8v0CnV6ZEMuZ1SrS13eB5D0l6WdIu59z/zPB9AJSgvqERHQzc5KbVU37ZHphLceLidfUMjsQcDQDMHpIUAFDiHn+5I9ivlnkU2LXWP1jx2dZrGhmlsyGA1DnnBpxz75f0HiVu8of0SvqypE0pzJTIVmynJe1UYt7E+SlOPa/E4Oy7nXNnUrz8g5I+I+kpJSpDpjMi6ceSfl7SFufc8ym+DwBIkva3dWok0Mt19y0kKfJJ6IGgMSe9eIZqCgClI68HZwMAcm/f0Yve9ab6quCTPSgdu9Yt1Od/MHm9Z3BEh89f17aV/i9WABDinPuWpG+Z2S1KVBEsl1QlqUvSMUlPZDJ7zjk3o9I/59zm/AIzAAAgAElEQVSQpAfM7HOS7pC0VVLz+PZlJRIrB9KdPeecO6pEmyeZWbmk9UpUj6yQNFeJf/ceSZ1KtJ3a75xLJZkBAF5PBlo9Layr0m3NDTFHg6msaarT/NpKdfYNT9o7eKZLu2/xPzAEAMWGJAUAlLCR0TE9cuyyd+8tGxervIxWT6Vu68p5qqks08Dw5HtyT528SpICQMacc69KenW244gaT0LsH//J9rVHJR0d/wGAnNjfes27fs+6hSrj831eMTNtXzVfPzw++TvZgdMMzwZQOmj3BAAl7Nm2a+run/zUjiTt3UyrJ0jVFeW6c/UC7x5zKQAAAPKLc06vXu7x7t2zxv+ZDrNre+Chn4NnuuScv20XABQbkhQAUML2HbnkXa+tKtfudZQWI2FXYMDi823MpQAAAMgnnX3Duj7gH7h822JaPeWjHav9LXav9Q6p7WpfzNEAwOwgSQEAJco5p4eP+pMUe9YvUk1lecwRIV/ds9b/1F3v0KiOXbgRczQAAAAIae3oDe6taaqLMRKkauvKRoW6cB1sp+UTgNJAkgIAStTRC9d1rss/l/O+TbR6wmtuX96o6gr/R4Zn2/w9jwEAABC/UJKirqpcixqqY44GqaivrghWuRwgSQGgRJCkAIASFaqiKC8z/fR6khR4TVVFWXBAdmgwIwAAAOLXFkhSrF5YJzOGZuer7av8LZ8OnO6KORIAmB0kKQCgRIXmUdy9ZoHm1VbGHA3y3c7AoMX9bdcY6AcAAJAnWq/6kxRrFtHqKZ/tWOV/IOj4xevqHfTPGAGAYkKSAgBK0NnOPh29cN27t5dWT/C4q8WfpLjaO6RTU/Q+BgAAQHxClRRrFpKkyGeh4dljTnrxbHfM0QBA/EhSAEAJCrV6kqS3kKSAx47V84MD/Wj5BAAAMPucc8EkRQtDs/Pa2qY6zZvjr2ZnLgWAUkCSAgBKUChJsXnZXK2YXxtzNCgE9dUV2rxsnneP4dkAAACz70rPoHqHRr17a5r4jJ/PzEzbAy2fDpKkAFACSFIAQInp7hvWM4En3++jigJTCLV82k+SAgAAYNa1dfQF91po95T3doSGZ7d3MQMOQNEjSQEAJeaHJy5pdMz/IXfvpiUxR4NCsnON/4vTmWv9utg9EHM0AAAASBZq9dRQU6EFdVUxR4N0hZIU13qH1H4tnIACgGJAkgIASkyo1dPyxjnauLQh5mhQSO4MVFJItHwCAACYba1XA0Ozm+pkFhguhryxdeU8hf5nYi4FgGJHkgIASsjA8KgeO3HFu3ffpsV8ecGUmuqrtXaRv1XAs61XY44GAAAAyVqvBIZm0+qpIDTUVOq2Zv9DYwdOd8UcDQDEiyQFAJSQp05eVV9gmN7ezcyjwPR2huZStPJ0FwAAwGxqC1RStDSRpCgUO1b7h2dTSQGg2JGkAIASsu/oRe/6vDmVwZvPQLLQ8OwTl26oq28o5mgAAAAgSWNjLpikWEuSomBsD8ylOH7xhvqGRmKOBgDiQ5ICAErE2JjTD45d9u69eUOzKsr5lYDp7VwTTmY918YTXgAAALPh0o0BDQyPefeopCgcoeHZo2NOL57tjjkaAIgPd6QAoEQcOtulKzcGvXv3baLVE1KzYv4cLZlb493bz/BsAACAWdHa4a+ikKQ1zKQoGGub6jS3psK7R8snAMWMJAUAlIh9Ry5516sqyvTG2xbFHA0KlZnprkA1xbMkKQAAAGZFW0efd31+baXm1VbGHA0yVVZmwZZPDM8GUMxIUgBAiXg4MI/iDbc0qa7a/7QO4LOzxf/F6aWz3eoPDGYHAABA7jA0u3iEWj4dOtMp51zM0QBAPEhSAEAJOHmlRyev+L+40OoJ6QpVUoyMOR08Qxk6AABA3ELtnmj1VHh2rG70rnf0DOnMtf6YowGAeJCkAIAS8PBRf6snM+nNG5tjjgaF7rbmBs2b428bsL+VJAUAAEDc2gJJCiopCs/WlY0y8+8xlwJAsSJJAQAlYN8Rf6un7Ssb1dzgH4IMhJSVme5c7S9DZ3g2AABAvEbHnE5f88+kIElReObWVOrW5nrvHkkKAMWKJAUAFLkrNwZ18Ix/yNp9m5bEHA2KRajl04H2To2MjsUcDQAAQOk639WvoRH/5y/aPRWm0FyKg+0MzwZQnEhSAECRe+TYJYXmq+3dzDwKZGZnIEnRNzSqI+evxxwNAABA6QoNzZaklqbaGCNBtoSSFMcuXFf/0GjM0QBA7pGkAIAity8wj2LtojqtW+QvIwams2XZPNVU+j9G0PIJAAAgPqF5FE311Wqo8c8RQ37bvso/PHtkzOnFs1RTACg+JCkAoIj1Do7oJ692ePf20uoJM1BVUabtK/1PeD3bSpICAAAgLq0d/nkUa6iiKFjrFtVrbk2Fd+8ALZ8AFCGSFABQxB5/+UqwP+19m2j1hJkJzaV47nSnXKjHGAAAALIq1O6phXkUBauszLQtOJeC4dkAig9JCgAoYg8HWj011Vdr+0p/CTGQqp0t/iTFtd4hnbzSE3M0AAAApSnU7qmliSRFIdsRaPl0oL2LB4IAFB2SFABQpIZHx/TI8cvevfs2NauszGKOCMVm+6pGlQf+HD1DyycAAICcGxkdU/u1ULsnkhSFbHugkqKjZ1BnO/tjjgYAcoskBQAUqf1t19TdP+zdo9UTsqGuukJbls317u0nSQEAAJBz57r6NTLmf6qedk+FbdsUle8HaPkEoMiQpACAIhVq9VRbVa7d65pijgbF6q5Ay6f9bXxxAgAAyLXWQKsnSWphcHZBmzenUrc213v3DpzmszaA4kKSAgCKkHNO+474kxRvum2RairLY44IxSo0PPtcV7/OdVGGDgAAkEuheRRL5taotqoi5miQbTsCLZ8OtHfFHAkA5BZJCgAoQscu3AjeIKbVE7IpVEkh0fIJAAAg10KVFFRRFIcdq/0tn45euK6+oZGYowGA3CFJAQBFaN/Ri9718jLTT29ojjkaFLMFdVW6JVCG/mwbSQoAAIBcar3K0OxidsdqfyXF6JjTC2e6Y44GAHKHJAUAFKHQPIqdLQvUWFsVczQodsG5FFRSAAAA5FSo3RNDs4vD2qZ6zZtT6d1jeDaAYkKSAgCKzLmufh05f927t3czrZ6QfTvX+J/weuVyjzp7h2KOBgAAoDQMjYzpbKe/kqKFSoqiUFZm2rHK3/KJ4dkAiglJCgAoMg8f8bd6kphHgdyYci4FLZ8AAABy4kxnn8acf492T8Uj1PLp+fZOORf4AwAABYYkBQAUmYeP+Vs9bVw6VyvmM0AP2bdifq2Wzavx7pGkAAAAyI1QqyczadUCPvcXix2BJEVX37BOBf4MAEChIUkBAEWku39YT5/y3xTeSxUFcuiuNf5qimfbKEMHAADIhdbADepl8+aoprI85miQK1tXNKq8zLx7z9PyCUCRIEkBAEXk+dPXNBqo+abVE3Ip1PLpyLlu9Q2NxBwNAABA8Wu7Ghia3UQVRTGpq67QxqUN3j3mUgAoFiQpAKCIHD7nH5i9eG61Ni+bG3M0KCU7A5UUI2NOB9u7Yo4GAACg+LV1+IdmM4+i+NyxKjCXgiQFgCJBkgIAisjhc93e9detaJSZv0QYyIZbFtWrsbbSu/dsK3MpAAAAsi3U7qllIUmKYhOaS/HK5R519w3HHA0AZB9JCgAoIkfO+ysptiybF3MkKDVlZaY7V/urKRieDQAAkF0Dw6M6393v3aOSovjcEUhSSNKBM1RTACh8JCkAoEhc6x3SuS7/F5Uty2n1hNzbucb/5elAe6eGRsZijgYAAKB4tV/rk/OPolMLSYqis7xxjpobqr17zKUAUAxIUgBAkThy3t/qSZK2LKeSArkXGp49MDymw1P8+QQAAEB6Qq2eykxaOZ/B2cXGzILVFMylAFAMSFIAQJEIDc1uqq8OPnUDZNOW5fM0p7Lcu7efuRQAAABZ0xZIUqyYX6uqCm71FKNQkuLQmS6NjFK1DKCw8ZsLAIpE6En125fPZWg2YlFZXqYdqxu9e8ylAAAAyJ62q4Gh2bR6Klqh4dl9Q6M6fvFGzNEAQHaRpACAInHknD9JQasnxCnU8ml/W6fGxgKNkwEAAJCWULunNQtp9VSsNi+bG6ySOdBOyycAhY0kBQAUgesDw2q72ufd27yMJAXiszOQpOjuH9Yrl3tijgYAAKA4tXX4P/tTSVG8qivK9brAA2jMpQBQ6EhSAEAROHreP49CkrYsnxtjJCh121fNV0WZv73Ys7R8AgAAmLH+oVFdvD7g3VtDkqKoMTwbQLEiSQEAReBwoNVTY22lljfOiTkalLI5VeXBFmPPkaQAAACYsdA8CokkRbELzaU429mvS4HEFQAUApIUAFAEQkmKLcvmMTQbsdu5JjCXopUkBQAAwEy1BeZRVJQZDygVuR2r/EkKSTpANQWAAkaSAgCKwOFAu6fNtHrCLLgz8ITX+e4Bne30908GAABAak4FkhSrFtSqopzbPMVsUUO1VgeGozM8G0Ah47cXABS4vqERnbziH0i8haHZmAV3BYZnS9JzbXx5AgAAmIlQJQVDs0vDHYFqCuZSAChkJCkAoMAdu3Bdzvn3QrMBgFyaX1elW5vrvXsMzwYAAJiZ0EyKloUkKUpBaC7F4XPXNTA8GnM0AJAdJCkAoMAdPudv9VRfXaHVC/ylwECu3RmopmB4NgAAwMy0dvjbZ65p4rN/KbgjkKQYGh3TkfP+WYUAkO9IUgBAgQsNzd60bK7Kyhiajdmxc43/y9PLl3rU2TsUczQAAADF4cbAsDp6Br17tHsqDbctblB9dYV3j5ZPAAoVSQoAKHChodm30+oJs2jKuRR8eQIAAMjI6av+KgqJdk+lorzMtG1lo3ePJAWAQkWSAgAK2MDwqF65dMO7t2X53JijAV6zvHGOls6r8e7R8gkAACAzrYGh2VUVZVrWOCfmaDBbQnMpnj/dJRcaWAgAeYwkBQAUsJcv3dDImP9D6JZlVFJg9phZsJqC4dkAAACZaQskKVYvqFU5rV5LRmguRUfPoM5c6485GgCYOZIUAFDAQkOzayrLtHZRfczRADe7q8X/5emls93qHxqNORoAAIDC13rVn6RgHkVp2bayURbIST3fzgNBAAoPSQoAKGAvhYZmL53Lk1SYdXet8VdSjIw5HTrTFXM0AAAAhS9USbGGJEVJmTenUrc1N3j3mEsBoBCRpACAAnbkvD9JsYWh2cgDtzU3aG5NhXdvPy2fAAAA0haaScHQ7NIz1VwKACg0/jsHAIC8Nzw6puMXAkOzmUeBPFBWZrqzZYF+ePzypD2SFADMbJ2knZJWSKqS1CnpuKQnnXMDsxiXSdohaZuk5vHlS5JekHTAMZEUwCzp7htWZ9+wd6+lqTbmaDDb7lg9X3//bPuk9RMXr+vGwLAaaipnISoAyAxJCgAoUK9c6tHQ6Jh3b/PyuTFHA/jdFUhSHDjdqZHRMVWUU9QJlBoze5ekP1YiEeDTY2Zfl/RJ51xHjHFVSvqQpA9LWh447ayZfUHSl5xz/juFM4vhNyV9xbO1xjnXlu33A1BYQvMoJNo9laLQ8OwxJ71wpltvuLUp5ogAIHPcGQCAAnU40OqpqrxMtwb6kwJx27nG/+Wpd2hUxwKVQACKk5lVm9k3JD2kcIJCkuol/bako2b2xphiWynpGUl/pnCCQkpUfXxO0lNmNtV5mcSwQtID2bwmgOISmkdRU1mmxQ01MUeD2daysFYL6qq8e8ylAFBoSFIAQIE6EhiavX5Jg6oq+Osd+WHL8nnBP4/P0vIJKBlmVibpHyX9QmRrVFKrpEOSor/YFkn6npntynFszZIelbQ9stUv6YikY5Ki7afukPSomWXzMdW/kkQpJICgqeZRlJVZzNFgtpmZdqwKzKVoJ0kBoLBwFwsACtTh89e961to9YQ8Ul1Rrm0rG717z5GkAErJRyS9M7L2FUmrnHNrnXPbJS2Q9G5JyQ22ayU9aGa5HLb0dUnrko4HlGj51OSc2+Kc2ySpSdLv6uZkxa2S/jobAZjZz0t6x/hhuJ8LgJLWFmj3RKun0hVq+XTwdKfGxhihBKBwkKQAgAI0OuZ0NJikYGg28svOlgXe9f1t18T8WaD4mdlCSX8YWf4D59wHnXPnJxacc2POuYck7ZbUlnTuCiUSBLmIba+ktyUtDUt6q3Pui865vqTYep1zn5f0M+PnTPg5M7t3hjE0Sfpi0tLHZnI9AMUr1O6phSRFyQolKW4MjuiVyz0xRwMAmSNJAQAFqLWjR/3Do969LctIUiC/3Nni//LU0TMUbFsAoKh8VFLysKTHJd0fOtk5d07Sr0WWf2c82ZFtn4ocf9Y593joZOfcjzQ59k/PMIYvKVGpIUnP6+aEBQBIkpxzwc9NaxaSpChVr1sxTxWBVl/MpQBQSEhSAEABOnzOX0VRXmZav4Sh2cgvd6yer1Cb5Ofa+PIEFLPxWRS/Eln+hJumjMo594ikHyctNUh6X5Zju13SzqSlXiUGZ0/nAd3ckmm3mW3MMIZ3SHr/+OGopF93zvmfQgBQ0jr7hnV9YMS7RyVF6aqpLNfmQCX9AeZSACggJCkAoAAdDgzNvrW5XjWV5TFHA0ytoaZSG5f6Z6UwPBsoeruVGIA94ZSkx1J87dcix+/KRkBJojMyHnTO3ZjuRePnfDOynHZsZjZXiWHZE77gnDuY7nUAlIapqk9bmmpjjAT5Zscq//y3A1RSACggJCkAoAC9FEhSMI8C+equKeZSAChqPxs5fni6KorkcyPHe8wsm48LR2Pbl8Zro7G9w3vW1B5QYt6GlJjBwSwKAEGhJEVdVbkW1VfHHA3ySWguxamOXl3rHYo5GgDIDEkKACgwY1MNzV7mf1odmG2hJMXpq326fH0g5mgAxGhb5PjJVF84PlS7LWmpStKmLMQkMzNJr4sspxybpCcix1vHr5nq+79J0m8kLX0weVA3AERNNTQ7jb9+UIRCSQqJagoAhYMkBQAUmPZrfbox6O9HSyUF8tVdgeHZkrSfuRRAMYvOajia5uuj52c0+8FjtaTk/ii9zrn2VF/snDstKTmpUCdpZSqvNbM5kv6bpIm7in/vnPt+qu8NoDS1Xg0nKVDals6bo2Xzarx7zzOXAkCBIEkBAAXm8Hl/qyczBfv+A7OteW6NVi/090um5RNQnMZvxq+KLJ9J8zLR89dnHtGU10k3Lt9rUo3tk5JuHf/na5I+nMF7AygxoUqKtSQpIGlHoJrieSopABSIitkOAACQnsPn/K2e1jbVqa6av9aRv+5qWaDTVyd3MyFJARStJr1WLSBJw5Iup3mNc5Hj5hlFFL7O2QyucU43Jyamjc3M7pD0u0lLH3HOpfvfJCvMrFk3DzVPxbpcxAJgas65cLunhSQpkGj59C8vXpi0/sKZLg2PjqmynGeUAeQ37mYBQIE5EqikoNUT8t3OlgX6H89Pvg947MJ13RgYVkNN5SxEBSCH6iPHfWkMzZ4QvSsXvWamotfx3/2bWlqxmVmlpK9JKh9fesw599cZvG+2/Jakj8/i+wNI0ZWeQfUOjXr3aPcEKTyXYnBkTEfPX9fWlY0xRwQA6SGVCgAFxDmnw+f8SYrbSVIgz90ZmEsx5ihFB4pU9Kb9QAbX6J/mmpmajdh+T9LW8X8elPSbGbwngBLU1jG5EnXCGpIUUKLtb02l/xYfn7MBFAKSFABQQM53D6izb9i7t3kZSQrktzVNdWqqr/LuPcfwbKAYRad4DmVwjcHI8ZwMY4mKNTYz2yjpj5KWPu2cezmD9wRQgl693ONdn1tTofm1VKJCqiwv09YV/moJhmcDKAS0ewKAAhKqopCkTcsYmo38Zma6q2WBvnf44qS9Z5lLARSjaHWCP0s5tepprpmp2GIzszIl2jxNnH9E0v0ZvF+2fVnSN9N8zTpJ385BLACmcOyCfybdLc31MjPvHkrPHavn65nWyZ+pD1BJAaAAkKQAgAJyJJCkWL2wVvPm8BQV8t+dgSTFC2e6NDgyquqKcs+rABSo6KO/0eqFVESrE/yPE6cvztj+k6Rd4//sJP2Gc85fFhmj8YHdaQ3t5mYoMDuOX/QnKTYu5SElvCY0l+JC94DOdfVreWO2ihEBIPto9wQABeTwef8XlC20ekKB2NmywLs+ODI2ZaUQgIIUvWlfa+nf5Y42W89VkiKTpu7TxmZmLZL+JGnpK865JzN4LwAlyjmn4xduePc2kKRAku2r/EkKSTrU3hVjJACQPpIUAFBAXgrcxN28nC8oKAwblzaorspfLfFsK6XoQJHpUKJyYEKlpOY0r7E8cpzWk/9TiF5nRQbXSCW2T+i1ZMZ5Sb+fwfsAKGFnO/t1Y3DEu7dpaUPM0SCfLairCg5SP3SGz9kA8ltBtHsys3WSdirx5aFKUqek45KedM5lqy9tJnGZpB2Stum1L1yXJL0g6YBzzoVem8b11yrR+3WlpEYlysp7JXUp8d/gkHOufybvA6AwXL4+oCs3ojM6E6ikQKGoKC/TjtXz9eNXOibtPdd2TYlfeQCKgXOu38zaJa1OWl6lxOflVK2KHB+fcWAJJyLHKzO4RvQ1vtiSp5guk9SdYcuk1sjrfsc594VMLgSgsITmUUjS+iU8qISbbVvZqNaO3knrh85QSQEgv+V1ksLM3iXpj5VIBPj0mNnXJX3SOTf5bkfu4qqU9CFJH9bkJ6gmnDWzL0j6Ujo9Z83sHkn/i6Q3SNqq6UvPR8zs/5P0l865h1N9HwCF5/D5cCuczQzNRgG5q2WBP0lxulNjY05lZfQ8B4rIcd2cpNgkaX8ar9/ouV42nJbUr9fmStSZ2Wrn3OlUXmxmqyXVJi31SjqTpdgA4N8cC7R6WrWgVvXVeX1LB7Ng28pGPXTw3KT1l851a3h0TJXlNFQBkJ/y8m8nM6s2s29IekjhBIUk1Uv6bUlHzeyNMcW2UtIzkv5M4QSFlKj6+Jykp8xsqvOiflvSRyXtVmq9cSskvVPSPjP7ppk1TvcCAIXp8Dn/U1TL5tVoYX11zNEAmbsrMJeiu39YL1/2fxEHULAORY53p/pCM1sqqSVpaVjS0SzEpPGK5xcjyynHJun1keMXZ1pFDQA+4aHZtHrCZNtW+m8JDQyP6cRFPmcDyF95l3Y3szJJ/6jEjfdko5LaJXVLWiMpubfJIknfM7O3OOeeymFszZIe1eReFP2STimR9FkjqSZp7w5Jj5rZ7hlUe4wq8WRWl6QhJf7d1yrR1zfZeyStNbM3O+eo5QOKTGio8ObltHpCYdm2slGV5abh0cn38/a3dWoDrQuAYvIvkn4v6fgtZmYp3tDfGzl+1DmXrcHZUiK2u5OO75P09ym+9r7I8XcC531M0l+kGZckRSukf1E3t8mKtqsCUKRC7Z74vASfjUvnqqqiTEMjY5P2Dp3p0ha+OwLIU/lYSfERTU5QfEXSKufcWufcdkkLJL1biaTFhFpJD5pZLv/G/bpuTlAMKNHyqck5t8U5t0lSk6TfHd+bcKukv07jfXok/Q8lqiq2Sapzzq1xzm13zt3tnNsgqUHSv5P0dOS1OyR9MY33AlAgjpz3f0FhHgUKzZyq8uAXpP2t12KOBkCOPanEAO0JayXtSfG1vxo5/nY2Akryz5Hj95pZ/XQvMrMGSe+NLHtjc8696Jz7Qbo/nks9ETmH1lJACegdHNHpa33evY1LSVJgsqqKsmArYOZSAMhneZWkMLOFkv4wsvwHzrkPOufOTyw458accw8pUZLdlnTuCiUSBLmIba+ktyUtDUt6q3Pui865f/vU4Jzrdc59XtLPjJ8z4efM7N4U3upTSiQ93uuc+0vn3AvOuUmTcp1zg8657yhRav61yPYHzGxTiv9qAArAtd4hnevq9+7dvoIvKCg8OwMtn/a3XRMdU4Di4ZwbU+JBn2Qft2mmR5vZmyX9VNLSDUkPZjm2F3XzfIx6JdquTuejurkt69POuay0oQKAZCcu3VDoY9EmkhQICLV8IkkBIJ/lVZJCiQ/8yY0VH5d0f+hk59w5Sb8WWf6d8WRHtn0qcvxZ59zjoZOdcz/S5Ng/Pd2bOOdO+JISU5w/Jul/183JGkn696leA0D+OzLF0GwqKVCI7gwkKS50DwQTcgAK1v1KVApPeJNubgF1k/F5bl+NLH9xutapZuYiP3tSiO1jkePfn2rWnZn5Yv+jFN4HANIWavVUV1WuFfPnxBwNCkUoSXHySo+uDwx79wBgtuVNkmJ8FsWvRJY/MV2/WufcI5J+nLTUIOl9WY7tdkk7k5Z6lRicPZ0Hxs+dsNvMNmYzNilRVaFEe6hkWX8fALMnNDR7UUO1mufWePeAfHbn6vnBvf1ttHwCisl4cuFPI8ufMbMvm9myiQUzKzOzdynRIqol6dzzkv48R7F9X9K+pKVKSf9qZh8ys9qk2OrM7MOSvq+b58J9d/z7CABk3fEL/kHHG5bOVVnZlAVpKGHbV/o/ZzsnvXQ2/PAbAMymvElSKNG6aVHS8SlJj6X42mi7o3dlI6Ak0RkZDzrn/J8Wkoyf883IcrZjm3AyctyUo/cBMAsOByoptgT6jQL5bn5dlW5b7G/9/mxrZ8zRAIjB/UoMqk72QUntZnbSzA5IuirpIUmrks7pl/Q+51wue1R8QFJr0nGNpC9I6jCzw2Z2RIm5Gp8f35twUtIv5zAuACUuPDS7wbsOSNLKBXO0oK7Ku0fLJwD5Kp+SFD8bOX54uiqK5HMjx3vMrM57Zmaise3znuUXje0dM4wlJPooNb95gCJy5FwgSREYPgwUglDLp+eopACKzniL0vdK+ofIVrkSw7S3S4r2p7gq6e3OuSdyHNslSfdKeiGyNUfSZkmbNPmz9iFJ9zrnruQyNgCla2zM6fhF/7ORDM3GVMws2PLpYDu3igDkp3xKUmyLHD+Z6gvHh2q3JS1VKfFlYsbGh/q9LrKcclNFLxgAACAASURBVGySol+qtk43KDBDd0eOn8/BewCYBdcHhtV2tc+7t5l5FChgoeHZr1zuUWfvUMzRAMg159yAc+79kt6jxE3+kF5JX5a0yTn3WEyxnVaivevvKdFeKuS8EnP07nbOnYkjNgCl6VxXv3oGR7x7G5dSSYGpTTU8O/XngQEgPhWzHUCS6AyFo2m+/qhu7l27UdL+mQQ0brWk2qTjXudce6ovds6dNrO+pGvUSVopKeVrTGd8zsW7k5ZGJP1dtq4PYHYdCcyjkKQty3mKCoXrrjX+JIWUmEuxd/OSGKMBEBfn3LckfcvMblHiQZvlSjxk1CXpmKQnnHMDGVx3Rg8COeeGJD1gZp+TdIekrZKax7cvK5FYOTBeFZJTM/13AVD4jgZaPUnS+iV8B8DUQkmKjp5Bnevq14r5td59AJgteZGkMLM5urn3rCSl+2RS9Pz1mUc05XUyeWLqTOQ665WlJIWZ3a3E3IvkhoN/wpNdQPE4EphH0VhbqeWNc2KOBsie5Y1ztGxejc53T74X+dzpTpIUQJFzzr0q6dXZjiNqPAmxX9l54AkAMhIamr16Ya3qq/PiVg7y2NYV/iSFlKimIEkBIN/ky2+2JknJTwsNK/G0UjrORY6bvWelL3qdsxlc45xuTlKkHJuZLVLiKa4JZZIaJN0qaa+kPbr5v91XJH0ygxgB5KnDoXkUy+YpN93jgPjctWaBvn1ocmeVZ1uZSwEAAEoXQ7MxE/NqK7W2qU6nOnon7R1q79I7XrdsFqICgLB8SVLUR4770hiaPSH6N2/0mpmKXmfy3/DTm0lsr5f0UArnvSTp4865VM5Ni5k1S1qU5svWZTsOoFQdCAw320yrJxSBu1r8SYqXznWrZ3CEJwUBAEBJOnbRn6RgaDZStW1loz9JcYbh2QDyT75884/etE+7B62k/mmumal8jm3CAUmfkfSdLF93wm9J+niOrg1gCue7+tV+zT80e6oSXqBQ3B2YSzE65rS/7ZruXZ+twkgAAIDC0Ds4otNX/d8BNjCPAinatqpR//NgtOlI4mGg4dExVZaXzUJUAOCXL38j1USOhzK4xmDkOFuN2vM5tgk7lJhL8aqZvTXL1wYwi55pvRrc2znF0GGgUNzSXK+m+mrv3lMnw3/+AQAAitXxi/55FJK0iUoKpCg0PHtwZEwnpvgzBgCzIV+SFNHqhCrvWVOL3uHIpOLBZ1Zjc879k3POJn4kVSox02KPpE9Luph0+mpJ3zOzX88gRgB56JlT/r78U93YBQqJmemetf6EG0kKAABQio4HWj3VV1doxfxsP/OIYrVhyVxVVfhv+x2k5ROAPJMvSYqeyHG0eiEV0d/U0WtmKq9ic86NOOeuOOd+5Jz7Y0m3Sfq7pFNM0l+Z2T2ZvofHlyVtSfPnnVl8f6BkPRMYHhxqkQMUol3rFnrXj5zvVnf/cMzRAAAAzK7Q0Oz1SxpUVmYxR4NCVVVRpi3L/JU3hwJzDwFgtuTLTIroTftaM7M0h2fXTXPNTEWvE32fVOQqNjnnbpjZL0maJ+lnx5fLJf25EkO3s/EelyVdTuc1ZnxwAmbq8vUBtXoGnUnS3Wv9N3WBQrQr8Od5zEnPtl7TfZsWxxwRAADA7Dl2wd+KZ+PShpgjQaHbtnK+DngSEofOdM5CNAAQli+VFB2SkhMSEy2N0rE8cpzWTfUpRK+zIoNr5Co2SZJzbkzSf9bN/w13m9mt2XwfAPF6OlBFIUn3UEmBIrKmqU6L5zKXAgAAYGzMBecFMDQb6dq2yj+X4uSVXiqWAeSVvEhSOOf6JbVHlleleZno+cczj+gmJyLHKzO4RvQ12Yrt3zjnTkl6IbK8O9vvAyA+z5zy35xd01Sn5rmZdJ4D8pOZBaspngr8/wAAAKAYne3sV8/giHdvI0OzkabtgeHZkvTiWVo+AcgfeZGkGBe9cb8pzddvnOZ6mTotqT/puM7MVqf64vFza5OWeiWdyVJsUScjx0ty9D4AYsA8CpSS0FyKYxeuq7N3KOZoAAAAZsexwNBsSdqwhHZPSM+K+XO0sK7Ku8dcCgD5JJ+SFIcixylXAZjZUkktSUvDko5mISaNz8V4MbKcToVCdC7Ei2nO2pgJaveAAnXlxqBevewfX3MP8yjw/7N378F1nfW9/z+PrpZkWbIkX2L5Kttx4vieEDnOzYEkXFsop2GmwylDD5z20NMptDMNdNpCGfiVhtIB2oFD50d7MtMOPwpluLdAyD1ObEIc20l8SXyX75Zly7Yu1u37+2NLsL30PNLeW3uvvbX3+zXjYdbzrLX212kTaa/vep5PEdqyvCU4t+MIqykAAEBpCIVmL2muVV11ocSKYrpwzmlDYDXFrg6aFAAKRyE1KX4UOb7fpZ6+/GDk+Ekzy1o4tcbX9kAa10bP/eEUa5lIdIXH2Rx+FoAc+sUEeRTtbaykQPFZ1FSr1sYa7xy5FAAAoFSEmhQ3k0eBDE3UpIjvHVoAmFghNSmeVyJAe0ybpK0pXvuhyPH3s1FQkh9Ejh9yzs2c7CLnXL2khyLD2a5t7LMWSNoUGY6uAAEwTYTeHF/cVKsbGvwPcoHpLrTlE7kUAACgVOwPhWbfwFZPyEwoPPtCz4BOXOzzzgFA3AqmSWFmI5IejQx/arLVFM65t0i6O2noiqRvZbm2PZJeTBqaKenhFC59WFJd0vF2M8vKNlQef6vr/+95xMxeydFnAcixHYfJo0DpCYVnv372qs5fuRZzNQAAAPG6em1Ixy70eucIzUam1i0Mh2ez5ROAQlEwTYpRj0hK3qbpXkkfD53snGuV9PXI8JfNrNN3ftJ1FvmzNYXaPhk5/oRz7p4JPsNX+19OUtdHnHPvS2ObKznnKpxzn5f0u5GpL6R6DwCFpatnQAfO+t+gaiePAkUstJJCkrazmgIAABS5A4FVFJK0miYFMtRQU6nlc+q8czQpABSKgmpSjDYX/iYy/Dnn3FdHtzOSJDnnypxz71Fii6ilSeeekvT3OartJ5J+ljRUKemnzrmPOudqk2qrc859TNJPRs8Z859m9vgkH7NK0r9LOuSc+5xz7u7RLaPGcc7Nd879vqSXJf1ZZHqHpK+l9BcDUHAmzKNgJQWK2ILGGi1prvXOseUTAAAodqE8ipnVFcHsLiAVGxbN9o7TpABQKAqqSTHqEY0Pqv6IpOPOuUPOuZ2SLkj6rqTFSef0SXqfmeXyv7AfkHQk6XiGpC9J6nTOveqce02JXI0vjs6NOSTpg2l8zjJJn5D0jKRu51yHc26Pc+6F0f89K+m0pH+StCZy7S5J7xjdPgvANBTKo2htrNGiJv8DXKBYhLZ82k54NgAAKHKhJsVN8+tVVpbyhgvAOKFcildPdmtwmMdHAPKv4JoUow/XH5L0zchUuRJh2hslRf/rekGJB/PbclzbWUn3SdodmaqRdIuk1bq+OSElmgb3mdn5DD/WSVooaa2kzaP/O9dz3oikf5R0t5mFX8MGUPDIo0ApC235dLizR2cv98dcDQAAQHwIzUaubFzkb1JcGxrR/tPhbcYAIC4F16SQJDPrN7PfkfTbSjzkD+mR9FVJq83sqZhqOybpdiXyJk5NcOopJYKz282sI8Xbf07ShyX9xyT3TnZSiS2ubjGzPzazq5NdAKBwdfcOat8Z/xtU7W00KVD8QispJOkFVlMAAIAiNTJi2h9YSUFoNqZq1fx6VVf4HwHu6rgYczUAMF5FvguYiJl9R9J3nHMrJLVLapVUJemSpH2StplZ2q9VmtmU1kma2YCkzzvnviDpVknr9evVDeeUaKzsTHfLpdGVGv88+mcsGHyVEts/zVZixUaPpMtKbPf0spml2swAMA384miXzPxzmwnNRgmYO2uGls+p06HzPePmXjh0Qe/Z2JqHqgAAAHLrxMU+9QwMe+doUmCqKsvLtLa1Qb88Nr4h8XLHJf3uHXkoCgCSFHSTYoyZHZR0MN91RI02IV4c/ZOL+59UYqUEgBKxIxAOPH/WDC0mjwIl4o7lzf4mBeHZAACgSO0NrKJwTlo1j+2eMHUbFjV6mxSEZwMoBAW53RMAlKodRwJ5FG1Nco6wPJSGO9pavOPHu3p18lJfzNUAAADkXig0e0lTreqqp8X7pShwofDsw+d71N07GHM1AHA9mhQAUCAu9w/qtVPd3rn2ZWz1hNKxeYL8FXIpAABAMdofyKW7aT5bPSE7NgTCsyVp9wlWUwDIL5oUAFAgXjp6USOBPApCs1FKmmdWB7c1oEkBAACK0b7TV7zj5FEgW1oba9Qys9o7x5ZPAPKNJgUAFIjtR/wPX1tmVqutpS7maoD8umO5f/XQ9sMXZKF0eQAAgGno6rUhHe/q9c7dfAN5FMgO51xwNQVNCgD5RpMCAArEjsPkUQBjNrf5mxQnL/UFv8QDAABMRwcCWz1JrKRAdm0M5FLs6rjEi0AA8oomBQAUgJ5rQ3rlpD+PYvMytnpC6dnc1qRQb44tnwAAQDHZG9jqaWZ1hRbOrom5GhSz0EqKrp4BdXT1xVwNAPwaTQoAKAAvHbuo4UAgRXvgjXKgmDXWVunmQFDkC4dpUgAAgOKx/3QoNLueFdXIqnULG4IvAr3ccTHeYgAgCU0KACgA2wMPXZvqqrRy7syYqwEKQyiX4oVD5FIAAIDisS/QpGCrJ2Rb/YxKrZjj/35JLgWAfKJJAQAFYMeRQB7FMvIoULq2BJoU565c0+HOnpirAQAAyL6REdOBM/7tnmhSIBcIzwZQiGhSAECe9Q0Ma88J/y+E7eRRoIS9aVmTysilAAAARazjYq96Boa9czfdUB9zNSgFGwLh2a+duqyBoZGYqwGABJoUAJBnO49f1OAweRRA1KwZlVrb2uCdI5cCAAAUg9BWT85Jq+bRpED2hVZSDAyNaP8Z//8/AkCu0aQAgDzbEXjY2lhbyRcTlLzNgS2fdhwmlwIAAEx/+077t3pa0lSruuqKmKtBKVg1r141leXeObZ8ApAvNCkAIM+2B/Io3rS0SWWhvW6AEnFHYDVR59UBvXHuaszVAAAAZBeh2YhbRXlZcLXyruM0KQDkB00KAMij/sHh4Nsq5FEAiWZdRaBZRy4FAACY7vYTmo08WL8o0KRgJQWAPKFJAQB5tKvjUjCcbDN5FIDqqiu0bmEgl4ImBQAAmMau9A/qeFevd+6m+Wz7itzZsGi2d/xwZ4+6ewdjrgYAaFIAQF5tD+RR1M+o4O0pYNQdgVyK7UcuaGSEXAoAADA9HQisopBYSYHc2rDYH54tSbtOsJoCQPxoUgBAHu047M+juH1pk8rJowAkSXe0tXjHL/UOat8Z/z7OAAAAhW5foElRX12hhbNrYq4GpWRBwwzNqa/2zpFLASAfaFIAQJ5cGxrWzuMXvXPtbeRRAGNuXTJbVeX+X1nY8gkAAExXodDsm26ol3O8sITccc5pwyL/aopdHf7vqACQSzQpACBP9pzo1rVAHkX7MvIogDE1VeXBJemhLdMAAAAK3f5Ak4KtnhCHUJPilZOXZcaWqgDiRZMCAPJkR+Dh6szqCt2ygC8mQLI7AkHyO450aZhcCgAAMM2MjJj2B7Z7umk+3wWQe+sX+psUnVev6ezlazFXA6DU0aQAgDzZccSfR3HrktmqCGxtA5SqUHj2lf4hvXaqO+ZqAAAApuZ4V696B4a9czffUB9zNShFa1rDzbA9hGcDiBlPwQAgDwaHR/TSMfIogFRtXNyo6gpyKQAAQHHYf8a/1ZNz0qr5NCmQe421VVrU5A9of/UkLwEBiBdNCgDIg1dOdgffnCKPAhivuqJcty6Z7Z17gVwKAAAwzew97d/qaWlznWqrKmKuBqVqbWuDd/wVmhQAYkaTAgDyYMdh/1ZPNZXlWrfQ/4siUOpCuRQvHunS4LA/hB4AAKAQhUOzWUWB+KwJNikIzwYQL5oUAJAH2wNvft+2dLYqyaMAvEK5FD0Dw7ztBQAAppXXTvmbFIRmI07rWsPh2Wcu98dcDYBSxpMwAIjZ0PCIfnnUv5KifRl5FEDIuoWNqqks986RSwEAAKaLzqvXdPJSn3fu5htoUiA+E4Vnv3KCl4AAxIcmBQDE7LVTl9UTyqMIbGcDQKqqKNNtS/25FDuO+Bt/AAAAhWbPiUvBufVs/YoYEZ4NoFDQpACAmG071Okdr64oI48CmERoy6dfHiWXAgAATA+7OvwPfxc0zNDcWTNirgaljvBsAIWAJgUAxGzbQX+T4tYls1Vd4d/KBkBC+zJ/k6KXXAoAADBN7O7wr6RYv8ifDwDk0tpALsUrJ7sJzwYQG5oUABCj/sFhvXj0onfurpUtMVcDTD/rFjYEcylCgfQAAACFwsy0O7DdE00K5ENoJUXn1QHCswHEhiYFAMToxaNdGhjyb0lz94o5MVcDTD+V5RPkUhwmlwIAABS2Yxd6dal30Du3fiFNCsSP8GwAhYAmBQDE6LnAVk+NtZVavSD8yyGAX9scCJgnlwIAABS60CoK56S15NMhDwjPBlAIaFIAQIxCeRRbljervMzFXA0wPW1ua/KO9wwM80UKAAAUtF2BPIqVc2dqZnVFzNUACesCuRR7+N0aQExoUgBATLp6BvTaqcveuTtXkEcBpGpta+MEuRRs+QQAAApXMDSbrZ6QR2sCuRSvEp4NICY0KQAgJs8f6lTo9zvyKIDUVVVMkEtxhPBsAABQmAaHR/Rq4KWlDYtpUiB/CM8GkG80KQAgJqGtnhY11Whxc23M1QDTW/sy/5ZPLx7p0hC5FAAAoAAdOHNFA0P+31NYSYF8IjwbQL7RpACAGJiZnn3D36S4i62egLSFwrN7BoaDbygCAADkUyiPorqiTKvm18dcDfBrhGcDyDeaFAAQg+NdvTpxsc87Rx4FkL51Cxs1o9L/a8z2w2z5BAAACk8oj2JNa4Mqy3k8g/wiPBtAPvFTEABi8FxgqyfnpC3LaVIA6aqqKNOtSwK5FDQpAABAAdp9gtBsFC7CswHkE00KAIhBKI/ilgWz1FRXFXM1QHHYvMy/5dOLRy+SSwEAAArKlf5BvXHuqndu/SL/w2EgToRnA8gnmhQAkGPDI6ZtB/1vdrPVE5C5zcv9TYqr14b0GrkUAACggLxysluhl9E3LGIlBfKP8GwA+USTAgBy7LVT3eruG/TO3b1iTszVAMVj3cIGcikAAMC0sLvD/5C3sbZSi5tqY64GGK+xtir4/4uvkEsBIMcq8l0AABS7UB5FVUWZblvq31MfwOSqK8q1afFsPX9ofENix5Eu/cG9y/NQFYBUOeeWS7pd0kJJVZIuStov6Xkzy9u+Es45J2mTpA2S5o4On5W0W9JOm+LG3KP3b5O0XNIiSY2SaiT1SLqkxD+DXWbWN5XPAVBYQqHZ6xc2KvGfBSD/1rY26HhX77hxmhQAco0mBQDkWCiP4k1LZ2tGZXnM1QDFZXNbs7dJ8eKRLg0Nj6iinEWjQKFxzr1H0l8p0Qjwueqce1TSp83M/0M0N3VVSvqopI9Jag2cdsI59yVJ/2Bm/mWS/ntvlvRbku6StF5S3SSXDDnnfizpK2b2WKqfA6BwBUOz2eoJBWRNa4N+/MrpceNj4dk01ADkCt/cASCH+geH9eLRi9458iiAqdvc5s+luHJtSHtPk0sBFBLnXLVz7t8kfVfhBoUkzZT0R5L2Oufuiam2RZJ2SPo7hRsUUmLVxxckveCcm+i8qD+S9LCkLZq8QSElXiZ7t6SfOee+7ZzjKSYwjZ293K/T3f4FYhsIzUYBITwbQL7QpACAHHrxaJcGhka8c+RRAFO3flGDqivIpQAKnXOuTNK/S3p/ZGpY0hFJuyRF95KYI+m/nHN35Li2uZKelLQxMtUn6TVJ+yRFn8zcKulJ59xU3jgYlnRUib/7LyQdkORbnfHbkh6nUQFMX6GtnqTEdk9AoQg1KSRpD+HZAHKIJgUA5FAoj6KxtlKrF8yKuRqg+IzlUvjsONwVczUAJvBnSqwMSPY1SYvNrM3MNkpqkvReSceTzqmV9C3nXC5fNX5UiXyIMf1KbPnUYmZrzGy1pBZJf6rrmxUrJf1LGp9zVdJ/KLGqYoOkOjNbZmYbzazdzG6SVC/pNyVtj1y7SdKX0/gsAAUktNXToqYaNc+sjrkaIKxhgiD3V8mlAJBDNCkAIIdCeRRbljervIz9PIFsCG359IsjXRoemVK+LYAscM41S/qLyPCfm9lHzOzU2ICZjZjZd5XYEulo0rkLlWgQ5KK2ByW9PWloUNJbzezLZvar5FAz6zGzL0p6m65f7fAbzrn7UviozyjR9HjIzL5iZrvN7Fr0JDO7ZmY/lHSnpH+OTH/AObc6xb8agAKyu8P/cJdVFChEodUUhGcDyCWaFACQI109A3rtlH9PfPIogOzZ3NbkHb9ybUh7A/8OAojVw0qsEBjzjKRHQieb2UlJH44M/8losyPbPhM5/lszeyZ0spk9rfG1f3ayDzGzA76mxATnj0j637q+WSNJ/y3VewAoDCMjFlxJsYHQbBSgNYEmxVh4NgDkAk0KAMiR5w91KvQ7HHkUQPasX9SoKnIpgII0mkXxe5Hhv7ZJnnKY2eOSnk0aqpf0vizXtlbS7UlDPUoEZ0/m86PnjtninLs5m7VJiVUVSmwPlSzrnwMgtw539uhK/5B3bj1NChSgicKzQwHwADBVNCkAIEdCWz0taqrR4mb/Pp8A0jejslybFvu/5O84QpMCyLMtSgRgjzks6akUr41ud/SebBSUJJqR8S0zuzLZRaPnfDsynO3axhyKHLMUE5hmQqHZ5WVOt5BRhwI0UXg2Wz4ByBWaFACQA2amZ9/wNynuYqsnIOtCuRQ7yKUA8u2dkePHJltFkXxu5Hirc64uCzWNidb2szSujdb2rinWEjIjcux/2gmgYIW2erpxXr1qqypirgaYHOHZAPKBJgUA5MDxrl6duNjnnSOPAsi+9mX+JsWV/iHtO00uBZBHGyLHz6d64Wio9tGkoSpJWQmOds45SesiwynXJmlb5Hj96D2zrT1y/FIOPgNADoVWUmxYFH5bHcg3wrMBxI0mBQDkwHOBrZ6ck7Ysp0kBZNvGxeRSAAUqmqGwN83ro+dnK5NhiaTk10R7zOx4qheb2TFJvUlDdZIWZak2SdJozsV7k4aGJH0jm58BILeuDQ1rb+BlifULyaNA4QqFZ79ygvBsALlBkwIAciCUR3HLgllqqquKuRqg+M2oLNfGQPjk9sNdMVcDQJKcczWSFkeGO9K8TfT8VZlXNOF90q3Ld022apNzrl3ST5VYPTLm/zGzTOoEkCf7Tl/R4LD/gS6h2Shk6xb6mxQXegjPBpAbbIAIAFk2PGLadtD/5jZbPQG5s7mtWTuOjG9I/OLIBQ2PmMrLcrETC4AJtEhK/hdvUNK5NO9xMnI8d0oVhe9zIoN7nNT1jYmUa3POzZG0PmmoTFK9pJWSHpS0Vdf/s/uapE9nUCOAPApt9VRTWa6Vc2fGXA2QujULJg7PXtBYE2M1AEoBTQoAyLLXTnWru2/QO3f3ijkxVwOUjva2Junx8eOXR3MpQsvWAeRM9Alcbxqh2WN6JrlnpqL3iX5OKqZS252SvpvCea9I+pSZpXJuWpxzcyWl+4vJ8mzXARSzUJNibWuDKsrZ2AKFayw8+3hX77i5V0926623zM9DVQCKGU0KAMiyUB5FVUWZbls6O+ZqgNKxafFsVZWXaWB4ZNzc9sMXaFIA8Ys+tM9kf4i+Se6ZqUKubcxOSZ+T9MMs33fMH0r6VI7uDUDSrhOB0OzFbPWEwre2tcHbpNhzgvBsANlH6x4AsiyUR/GmpbM1o7I85mqA0jGjsjz4pd+3DRSAnJsROR7I4B7XIsfZ2l+ikGsbs0nStyUddM69Ncv3BpBj3b2DOnzev0iL0GxMB2sDuRSvniQ8G0D20aQAgCzqHxzWi0cveufIowByb/OyJu/4L450aWSEL1NAzKKrE6q8Z02sepJ7ZiqvtZnZ98zMjf2RVKlEpsVWSZ+VdCbp9CWS/ss59z8zqBFAnuw56V9FIUnrF7G6E4VvbWAVMuHZAHKBJgUAZNGLR7s0MDR+qxmJPAogDpvbmr3j3X2D2nfmcszVACXvauQ4unohFdHVCdF7ZqqgajOzITM7b2ZPm9lfSbpR0jeSTnGS/o9zbnOmn+HxVUlr0vzz7ix+PlDUQnkULTOr1EroMKaBycKzASCbyKQAgCwK5VE01lZq9YJZMVcDlJ6NE+ZSdOmWCb5sAci66EP7WuecSzM8u26Se2Yqep/o56QiV7XJzK44535XUoOkd44Ol0v6eyVCt7PxGecknUvnGudcNj4aKAm7OvwPcdcvbOTfJUwLE4Vnv3KC8GwA2cVKCgDIolAexZblzSov48sIkGs1VeXasCiQS3H4QszVACWvU1JyQ2JsS6N0tEaO03qoPoHofRZmcI9c1SZJMrMRSX+s6/8ZbnHOrczm5wDIPjPTrsBKivWB31OAQhTa8omVFACyjSYFAGRJV8+AXjvl306GPAogPu1t/lyKHeRSALEysz5JxyPDi9O8TfT8/ZlXdJ0DkeNFGdwjek22avsVMzssaXdkeEu2PwdAdp3u7lfn1WveOZoUmE4IzwYQF5oUAJAlzx/qVOj3NPIogPhMlEux/8yVmKsBSl70wf3qNK+/eZL7ZeqYpL6k4zrn3JJULx49tzZpqEdSR5ZqizoUOWZ/DaDAhfIoJGl94KEvUIgIzwYQF5oUAJAloa2eFjXVaHFzrXcOQPZtWjxbleX+7dW2s+UTELddkeOUVwE4526QtDRpaFDS3izUpNFcjD2R4XRWKERzIfakmbUxFYMxfQ6ADO064W9SLG2uVWNtVczVAJkjPBtAXGhSAEAWmJmefcPfpLiLrZ6AWNVUlWv9wkAuxRGaFEDMfhQ5vt+lnhj7YOT4STPLWji1xtf2QBrXRs/94RRrmUh0r+HHjgAAIABJREFUhcfZHH4WgCwIraRgqydMN2Ph2T6vnKBJASB7aFIAQBYc7+rViYt93jnyKID4hbZ8IpcCiN3zSgRoj2mTtDXFaz8UOf5+NgpK8oPI8UPOuZmTXeScq5f0UGQ427WNfdYCSZsiw9EVIAAKyPCIBR/ehl6iAApZKJeClRQAsokmBQBkwXOBrZ6ck7Ysp0kBxC3UpLjUO6gDZ8mlAOJiZiOSHo0Mf2qy1RTOubdIujtp6Iqkb2W5tj2SXkwaminp4RQufVhSXdLxdjPLyjZUHn+r67+zHTGzV3L0WQCy4OC5q+oZGPbObVhMkwLTTyiXgvBsANlEkwIAsuC5wFZPtyyYpaY69p0F4rZpSSO5FEDheERS8jZN90r6eOhk51yrpK9Hhr9sZv4ftr++ziJ/tqZQ2ycjx59wzt0zwWf4av/LSer6iHPufWlscyXnXIVz7vOSfjcy9YVU7wEgP0JbPVWUOa2+YVbM1QBTR3g2gDjQpACAKRoaHgmGZrPVE5AftVUVWhfKpTjcFXM1QGkbbS78TWT4c865r45uZyRJcs6VOefeo8QWUUuTzj0l6e9zVNtPJP0saahS0k+dcx91zv1qE27nXJ1z7mOSfjJ6zpj/NLPHJ/mYVZL+XdIh59znnHN3j24ZNY5zbr5z7vclvSzpzyLTOyR9LaW/GIC8CYVm33zDLM2oLI+5GmDqJgrP3kMuBYAsoUkBAFO0q+OSLvcPeefuXjEn5moAjNnc1uQd33HkArkUQPwe0fig6o9IOu6cO+Sc2ynpgqTvSlqcdE6fpPeZmf+pX3Z8QNKRpOMZkr4kqdM596pz7jUlcjW+ODo35pCkD6bxOcskfULSM5K6nXMdzrk9zrkXRv/3rKTTkv5J0prItbskvWN0+ywABSwcmh1+0AsUsobaSi1p9odnv0ouBYAsoUkBAFP09OvnveM1leW6bensmKsBMCaUS3Gxd1BvnLvqnQOQG6MP1x+S9M3IVLkSYdobJUWXP11Q4sH8thzXdlbSfZJ2R6ZqJN0iabWub05IiabBfWbm/yVgck7SQklrJW0e/d+5nvNGJP2jpLvNjGVgQIHrHxzW/jP+7CtCszGdrQls+UR4NoBsoUkBAFP01AH/84k7ljezpBvIo1uXzFZFmX8L+B1HyKUA4mZm/Wb2O5J+W4mH/CE9kr4qabWZPRVTbcck3a5E3sSpCU49pURwdruZdaR4+89J+rCk/5jk3slOKrHF1S1m9sdmRmcVmAZeO9Wt4cBqzQ2LaFJg+iI8G0CuVeS7AACYzjqvXgu+PbJ1FVs9AflUW1WhtQsb9PLx8dsu7DjcpQ/csTT+ogDIzL4j6TvOuRWS2iW1SqqSdEnSPknbzCztJE4zSzmYOnD9gKTPO+e+IOlWSev169UN55RorOxMd8ul0ZUa/zz6ZywYfJUS2z/NVmLFRo+ky0ps9/SymaXazABQQHZ1+L8XzKyuUNucmTFXA2TPROHZp7r71dpYE3NFAIoNTQoAmIJn3wjv8nDvjTQpgHxrX9bsb1IcuSAzk3NTeqYJYArM7KCkg/muI2q0CfHi6J9c3P+kEislABSZUB7F2tYGlQdWdwLTwUTh2bs7LtGkADBlbPcEAFMQ2uppWUudljTXxVwNgKj2QHh259UBHTrfE3M1AACgmO0+EQrNZqsnTG8NtZVaGgjP3nnsYszVAChGNCkAIEPDI6ZnAqHZrKIACsNtS2Yr9OIiuRQAACBbLvYM6NiFXu/chkXht9CB6WLTktne8ZeO06QAMHU0KQAgQ6+e7NbF3kHvHE0KoDDUz6jUmsAeujsOd8VcDQAAKFa7AqsoJFZSoDhsWuxvUrx6slv9g8MxVwOg2NCkAIAMhbZ6qqoo0+a25pirARDSvsy/5dNYLgUAAMBUvXjE//LD3PpqzZ81I+ZqgOy7NbCSYnDY9OpJf2g8AKSKJgUAZOjp1895x9uXNammqjzmagCEtC/zNw3PXr4W3JYBAAAgHdsP+7eR3LR4tpwjNBvT343z6jWzusI7t5MtnwBMEU0KAMjApd4B7erwL+neumpuzNUAmMibljUp9GyAXAoAADBVPdeGtOeE/03yO5azwhrFobzMaeNi/9ZlLxGeDWCKaFIAQAaefaNTI4FdYsijAApLQ02lbp4/yztHLgUAAJiql45d1FDgywHbwKKYbAzkUrx07BLbqAKYEpoUAJCBp1/351G0NtZo+Zy6mKsBMJn2Nn8uxfbD5FIAAICpCW311FRXpZVzZ8ZcDZA7oVyKzqvX1NHVF3M1AIoJTQoASNPIiAWbFFtXzWHPWaAAhXIpTnX368RFvlABAIDMhZoU7cuaVFbGdwMUjw2LGoPbqJJLAWAqaFIAQJr2nbms81eueefY6gkoTLcv86+kkMIPFgAAACbTOxDOo2CrJxSbhppK3Ti33jtHLgWAqaBJAQBpCq2iqCx32rKiJeZqAKSiqa5Kq+b5v1DtOEIuBQAAyAx5FCg1m5YQng0g+2hSAECanjrgb1LctqRJM6srYq4GQKpCuRQ7jrCSAgAAZIY8CpSaTYHw7P1nLqvn2lDM1QAoFjQpACANl/sHtTPwhsi9q9jqCShkoVyKjq4+nbpELgUAAEjf9sP+FZnkUaBYhcKzR0za3XEp5moAFAuaFACQhucPXggu595KkwIoaBPlUrCaAgAApKt3YCj4UJatnlCslrXUaXZtpXeOLZ8AZGpa7EvinFsu6XZJCyVVSbooab+k582sP491OUmbJG2QNHd0+Kyk3ZJ2mpn/SWZ6nzFH0lpJyyXNluSU+PufkLTdzNhIG4jR06+f847Pm1Ud3O8eQGGYU1+t5XPqdOh8z7i5HYe79FsbF+ahKgAAMF2RR4FS5JzTpsWz9fj+8d+NXzpOkwJAZgq6SeGce4+kv1KiEeBz1Tn3qKRPm1lnjHVVSvqopI9Jag2cdsI59yVJ/2Bmg2ncu0rSWyW9U9JbJK2Y4HRzzr0o6R8lfdPM2PwPyCEz09OBPIp7b5yjRN8SQCFrb2v2NykIzwYAAGkijwKlatMSf5Pi5eOXNDJibHUGIG0Fud2Tc67aOfdvkr6rcINCkmZK+iNJe51z98RU2yJJOyT9ncINCimx6uMLkl5wzk10XvK9PyjpjKQfSPoDTdygkBKrKm6X9K+SnnfOrUzlcwBk5o1zV3Wq2794a+uqud5xAIWlPbDl05HOHp27nLfFmQAAYBoijwKlKpRL0d03qMOdV2OuBkAxKLgmhXOuTNK/S3p/ZGpY0hFJuyR1R+bmSPov59wdOa5trqQnJW2MTPVJek3SPknRJxy3SnrSOdeSwkesUWJLJ59zkl6R9JKk0575NynRqLg5hc8BkIHQKoryMqc7V6TyrziAfJto64XtrKYAAAApIo8CpWz9wkaVBxpx5FIAyETBNSkk/Zmkd0fGviZpsZm1mdlGSU2S3ivpeNI5tZK+5ZxryGFtjyqRDTGmX4ktn1rMbI2ZrZbUIulPdX2zYqWkf0nzswaVWEnyfkmtZjbPzNaZ2W1mtkDSzZL+b+SaFkk/cc7VpvlZAFLw9Ov+JsWmxY1qqPEHhwEoLPNmzdDSZv+PyR2BLRsAAACiyKNAKaupKtfqG2Z552hSAMhEQTUpnHPNkv4iMvznZvYRMzs1NmBmI2b2XUlbJB1NOnehEg2CXNT2oKS3Jw0NSnqrmX3ZzHqTausxsy9KetvoOWN+wzl3XwofdUXSZyQtMrP3mtk3kv/uSZ+z38z+h6QPSEr+zWixpI+n/BcDkJKea0P6ReAt63tvnBNzNQCmon2Z/8EBuRQAACBV5FGg1IW2fNp53L/CCAAmUlBNCkkPS6pPOn5G0iOhk83spKQPR4b/ZLTZkW2fiRz/rZk9EzrZzJ7W+No/O8lnfE9Sm5l90szOplKUmf2rpC9Fhj+UyrUAUrf98AUNDI945+69kTwKYDppb/PnUhw8d1WdV6/FXA0AAJiOyKNAqdsUaFIcPHdVl3oHYq4GwHRXME2K0SyK34sM/7WZ+ddPjjKzxyU9mzRUL+l9Wa5trRIB1WN6lAjOnsznR88ds2WizAgze87MOjMo8RFdv5qi1Tm3JoP7AAgIbfXUMrNKtyzwL3MFUJjaJ9iCIbRiCgAAYAx5FEB4JYUkvcxqCgBpKpgmhRJbNyXvmXJY0lMpXvvPkeP3ZKOgJNGMjG+Z2ZXJLho959uR4WzXptFVF69Hhhdn+3OAUmVmeioQmn3Pyjm8KQVMM62NNVo4u8Y7Ry4FAACYDHkUgLSgYYbmzar2zpFLASBdhdSkeGfk+LHJVlEknxs53uqcq8tCTWOitf0sjWujtb1rirWERH8C5DJAHCgpRy/06nhXr3fu3lXkUQDTEbkUAAAgU+RRAJJzboJcCpoUANJTSE2KDZHj51O9cDRY+mjSUJWk1VmoSc45J2ldZDjl2iRtixyvH71ntrVGjnkVFMiSpw+c8447J929kiYFMB2Fcin2n7miiz3soQsAAMLIowASNi32Nyl2dVzSUCDTEQB8CqlJEc1q2Jvm9dHzg9kPaVoiqTbpuMfMjqd6sZkdk5T8CnadpEVZqk2S5JxbJmlhZPiNbH4GUMqeCuRRrFvYqKa6qpirAZANmwMrKSTpF0dZTQEAAPzIowB+LbSSondgWPvPTLpLOgD8SkE0KZxzNRqfodCR5m2i56/KvKIJ75NuXb5rslXbmA9KSn5dY5+ZHcnyZwAlqX9wOLice+uNrKIApqtFTTW6oWGGd25H4O1IAAAA8iiAX7tlQYOqKvyPFtnyCUA6KvJdwKgWXf+QfVCSf3+VsJOR47lTqih8nxMZ3OOkrm9MZKs2OedukPSxyPCj2br/6GfM1fWh5qlYns0agHzZcaRL/YP+ZarkUQDTl3NO7cua9L1dp8bN7TjCjokAAMAv9ALT7NpK8ihQcqoqyrSutUG/9ARl7zx2UR+4Y2n8RQGYlgqlSRH9Sd6bRmj2mJ5J7pmp6H2in5OKnNQ2mm3xdUmzkoZPSvpKNu6f5A8lfSrL9wSmhacP+Ld6aqyt1PqFjTFXAyCb2tuavU2Kvacvq7tvUA01lXmoCgAAFLJwHkUzeRQoSbcume1tUrzESgoAaSiI7Z40/qF9fwb36Jvknpkq5No+IekdkbE/NLNMGikAPJ5+3b+o6+6Vc1TOlxBgWmtf5g/PNpN+SS4FAACImDiPwv97BVDsNgVyKTq6+nTuciaP0ACUokJpUkQ3hR7I4B7XIsc1GdYSVZC1OefeLemzkeGvmdkPpnpvAAkdXb06dN7f87uXPApg2lvWUqc59dXeuR1HaFIAAIDrTZhHsZw8CpSmTYv9TQqJXAoAqSuU7Z6irdWqDO4RfcqQrXZtwdXmnNss6Ru6vsn0rMZnU2TLVyV9O81rlkv6fg5qAWLz9Ov+rZ4k6Z4bW2KsBEAujOVS/GjP6XFzOwL7TQMAgNI1UR7FjXPrY64GKAxz6qu1uKlWx7t6x83tPH5Jb1tzQx6qAjDdFEqT4mrkOLp6IRXR1QnRe2aqoGpzzt0i6ceSapOGd0v6DTOLrtjICjM7pzSDzBNxGcD0FmpS3LJglubWZ/KfAgCFpr2t2dukePXUZV29NqSZ1YXyqxIAAMg38igAv1uXzPY2KV7yZFUAgE+hbPcUfWhf69J/yl03yT0zFb1P9HNSkZXanHPLJP1MUvJml29IequZdWdyTwB+A0Mjev5gp3eOrZ6A4rE5kEsxPGLkUgAAgF8hjwIIC+VSvHKiW9eGhmOuBsB0VChNik5JyRs7Vkqam+Y9WiPHab35P4HofRZmcI8p1+acWyDp55IWJA13SLrfzM5mUBOACfzyWJd6Bvy/TG1dle5/ngAUqhVzZ6q5zr+TI7kUAABgDHkUQNimxY3e8YHhEb168nLM1QCYjgqiSWFmfZKOR4YXp3mb6Pn7M6/oOgcix4syuEf0mrRqc861KNGgaEsaPqdEgyL6zw1AFjy5399LrK+u0MbAL2AAph/nnNoDbz+SSwEAAMaQRwGErZpXr7qqcu/cy4RnA0hBQTQpRkUf3K9O8/qbJ7lfpo5J6ks6rnPOLUn14tFzk/MjepRYAZHq9Q2Sfqrr/36XJD1oZq+neh8A6Xki0KS4a2WLKssL6T+dAKaqfZn/7cc9J7rVOzAUczUAAKAQ7SCPAgiqKC/ThsDLfORSAEhFIT1p2xU53pLqhc65GyQtTRoalLQ3CzXJzEzSnshwyrVJujNyvGf0npNyztUpEZK9KWn4qqS3m9nuNGoAkIbjF3p16HyPd+6+m9jqCSg2oZUUQyOmncf8e08DAIDS0TswpN0nyKMAJnLrYn8uxS+PXVSKj8EAlLBCalL8KHJ8fxrh2Q9Gjp80s2wFZ0vja3sgjWuj5/4wlYucc9WSvqfrmxz9kt5tZtvT+HwAaXpifzjmZesqQrOBYnPj3Ho11lZ653YcYcsnAABK3c5jlzQ4TB4FMJGNgfDs81eu6cTFPu8cAIwppCbF80oEaI9pk7Q1xWs/FDn+fjYKSvKDyPFDzrmZk13knKuX9FBkeNLanHMVkr4l6f6k4UFJD5nZE5NdD2Bqnjhw3ju+bmGD5tbPiLkaALlWVuZ0+9JQLgXh2QAAlDryKIDJbVrkb1JI0k5yKQBMomCaFGY2IunRyPCnJltN4Zx7i6S7k4auKPGAP5u17ZH0YtLQTEkPp3Dpw5Lqko63m9mE21A558qU+Ofwm0nDI5L+u5lFV3QAyLLegaHgl5D7VrHVE1Cs2tv8b0Hu6rik/sHhmKsBAACFJPT9gDwK4Ncaaiu1cq7/fd6d5FIAmETBNClGPaJE5sKYeyV9PHSyc65V0tcjw182s07f+UnXWeTP1hRq+2Tk+BPOuXsm+Axf7X+Zwud8RdL7k45N0ofNLKuNFwB+2w5e0MDQiHeOPAqgeLUv86+kGBge0cvHyaUAAKBUkUcBpO7WwJZPL7GSAsAkCqpJMdpc+JvI8Oecc191zi0YG3DOlTnn3qPEFlFLk849Jenvc1TbTyT9LGmoUtJPnXMfdc7VJtVW55z7mKSfjJ4z5j/N7PGJPsM59ylJ/ysy/B1JHc65+9P80zalvzBQop7Yf8473jKzSutaG2KuBkBcbr5hlupnVHjnnn3DvwUcAAAofuRRAKnbFAjP3nf6inoHhmKuBsB04v82nl+PSNoi6V1JYx+R9PvOuWOSuiUtk9QYua5P0vvMLJevO35A0gujny9JMyR9SYlGymFJToksjeim9YckfTCF+9/nGfvt0T/p+rSkv87gOqBkmZmeOuBvUtx741yWcgNFrLzMqX1Zk36+b/x/Ax7be1YPv+2mPFQFAADyjTwKIHWbAisphkdMuzu6dQeNPQABBbWSQvpVNsVDkr4ZmSpXogGwUeMbFBckvcPMtuW4trNKNBJ2R6ZqJN0iabXGNyh2SbrPzHgNEyhw+05f0enufu/cm9nqCSh6WwO5M2+cu6qjnT0xVwMAAAoBeRRA6tpa6tRYW+mdIzwbwEQKrkkhSWbWb2a/o8QKgl0TnNoj6auSVpvZUzHVdkzS7UrkTZya4NRTSgRnt5tZRxy1AZiaJwOrKCrKnO6+sSXmagDE7YHV84Jzj+09G2MlAACgEJBHAaSnrMwFt3x6ifBsABMoxO2efsXMviPpO865FZLaJbVKqpJ0SdI+SdvMzP/a88T3ndLrDmY2IOnzzrkvSLpV0npJY69fnlOisbJzdFVIOvfdOpW6AExNKI/itqWzNWuG/20QAMVj3qwZWr+wQbtPdI+be2zvWf3Pe4h7AgCglJBHAaRv0+JG73frnccvyszkHCuQAIxX0E2KMWZ2UNLBfNcRNdqEeHH0D4Bp7GLPgF4OLD9lqyegdDywep63SfHLY13q6hlQU11VHqoCAAD5QB4FkL5QLsWl3kEd7uzR8jkzY64IwHRQkNs9AUDcnn79vEb8L0nRpABKyAOr53vHR0x6fB9bPgEAUErIowDSt35ho8oD/36w5ROAEJoUAKDwVk+Lmmp40wMoITfOm6nFTbXeuZ/TpAAAoGQMDo9oz8nxqysl8iiAidRVV+im+f6VRjtpUgAIoEkBoOQNj5iefv28d+7Nq+ayZyZQQpxzwQDtZ17vVP/gcMwVAQCAfHj97BUNDPljJm9bSpMCmMitgS2fdhzpirkSANMFTQoAJe/l4xfV3TfonbuPrZ6AkhNqUvQNDmvbwc6YqwEAAPnwamAVRVVFmVYF3hIHkBBqUhzp7NGpS30xVwNgOqBJAaDkhbZ6qqks1+a25pirAZBvty2ZrcbaSu/cY3vZ8gkAgFKw54S/SXHzDbNUWc6jFGAidywPf4/mpR8APvxkBVDyQk2KO1c0a0ZleczVAMi3ivIyvXmVfxXVz/ed08iIxVwRAACIW2glxdrWWTFXAkw/c+tnaNU8/4ojmhQAfGhSAChppy71af+ZK965rYGHlACKX2jLp86r1/Ryx6WYqwEAAHEaHB7RvsB3hLWtDTFXA0xPd65o8Y4/d/CCzHjpB8D1aFIAKGlPHvCvopDIowBK2T03zlFVhf/XJLZ8AgCguE0Umr2GJgWQkrtW+rd86rx6Ta+fvRpzNQAKHU0KACXtycBWTzfNr1drY03M1QAoFHXVFbozsJfuY3vPxFwNAACI00Sh2TcGtrABcL3blzWrosx5555jyycAETQpAJSs/sFhbTt4wTvHKgoAD6ye7x0/dL5Hh8/z9hcAAMWK0Gxg6mZWV2jDokbvHLkUAKL46QqgZG0/fEF9g8PeuTfTpABK3v03h/87wJZPAAAUL0KzgewI5VLsOHxBg8P+LdUAlCaaFABKVmirp4aaSm0MvPEBoHTMnTUj+PYXTQoAAIoTodlA9ty10t+k6BkY1u6OSzFXA6CQ0aQAUJLMTE8EQrPvvXGOKljGDUDSA6vnecdfOn5RnVevxVwNAADINUKzgezZsKhRdVXl3jlyKQAk4ykcgJJ06PxVdXT1eefY6gnAmAcDTQoz6Yl9/kYnAACYvgjNBrKnsrxM7W3N3jlyKQAko0kBoCQ9EdjqqcwlVlIAgCStmDtTS5trvXM/Y8snAACKTjA0e349odlABrYs9zcpXj5+ST3XhmKuBkCh4icsgJIUalJsXDxbs+uqYq4GQKFyzgW3fHru4Hn1DQzHXBEAAMilYGj2QrZ6AjIRyqUYGjH94khXzNUAKFQV+S4AAOJ2uX9Qvzx60TvHVk8Aoh5YPV//77NHxo33D47ouYOdwSYGgIk555ZLul3SQklVki5K2i/peTPrz2NdTtImSRskjf1icFbSbkk7zcyy8BlzJK2VtFzSbElOib//CUnbzYynNkAeEJoNZN+qefVqmVmlzqsD4+aeO9ip+/gODkA0KQCUoGdf79TQiP/5wn2r+AUJwPVuXTJbTXVV6uoZ/8Xqsb1naFIAaXLOvUfSXynRCPC56px7VNKnzSy2Daudc5WSPirpY5JaA6edcM59SdI/mNlgGveukvRWSe+U9BZJKyY43ZxzL0r6R0nfNDP2wgBiQmg2kH3OOd25okXf33Vq3By5FADGsN0TgJIT2upp/qwZuvkGwvAAXK+8zAVXWT2+75yGA01PANdzzlU75/5N0ncVblBI0kxJfyRpr3PunphqWyRph6S/U7hBISVWfXxB0gvOuYnOS773ByWdkfQDSX+giRsUUmJVxe2S/lXS8865lal8DoCpIzQbyI07V/i3fNp/5orOXcnb4kkABYQmBYCSMjJievp1f5PivpvmKLHDAwBcL7Ra4kLPgF4+7t8+DsCvOefKJP27pPdHpoYlHZG0S1L06eAcSf/lnLsjx7XNlfSkpI2RqT5Jr0naJyn6BOVWSU865/xPXa63RoktnXzOSXpF0kuSTnvm36REo+LmFD4HwBQRmg3kRqhJIUkvHLoQYyUAChU/ZQGUlD0nu717YUps9QQg7O6VLaqu8P/a9NjeszFXA0xLfybp3ZGxr0labGZtZrZRUpOk90o6nnROraRvOedyuc/Ko0pkQ4zpV2LLpxYzW2NmqyW1SPpTXd+sWCnpX9L8rEElVpK8X1Krmc0zs3VmdpuZLZB0s6T/G7mmRdJPnHO1aX4WgDQRmg3kRmtjjZa11HnnnnuDLZ8A0KQAUGKeDGz1VFVeNuHbHQBKW21Vhe5e6f9vBE0KYGLOuWZJfxEZ/nMz+4iZ/WqDajMbMbPvStoi6WjSuQuVaBDkorYHJb09aWhQ0lvN7Mtm1ptUW4+ZfVHS20bPGfMbzrn7UvioK5I+I2mRmb3XzL6R/HdP+pz9ZvY/JH1AUvJecoslfTzlvxiAtBGaDeTWnSuavePbDnbKjO1TgVJHkwJASXnygL9J0d7WpLrqipirATCdhLZ8OtzZo4PnrsZcDTCtPCwpeTP3ZyQ9EjrZzE5K+nBk+E9Gmx3Z9pnI8d+a2TOhk83saY2v/bOTfMb3JLWZ2SfNLKWuppn9q6QvRYY/lMq1ADJDaDaQW3cFXgo81d2voxd6vXMASgdNCgAl49yV/uA+s6FQXAAY8+ab5ikUW8NqCsBvNIvi9yLDf22TvDJpZo9LejZpqF7S+7Jc21olAqrH9CgRnD2Zz4+eO2bLRJkRZvacmWWyl8Ujun41Ratzbk0G9wGQAkKzgdy6o60l+Lv0cwfZ8gkodTQpAJSMpw6cD87RpAAwmTn11dq02J99+9jeMzFXA0wbW5QIwB5zWNJTKV77z5Hj92SjoCTRjIxvmZl/r5cko+d8OzKc7do0uuri9cjw4mx/DoAEQrOB3GqorQxunbaNXAqg5PGTFkDJCOVRtM2p05Jmf4gXACS7/2b/lk8vd1zS+SvXYq4GmBbeGTl+bLJVFMnnRo63Ouey+QM7WtvP0rg2Wtu7plhLyMXIMXvOADkSWknBVk9A9oRyIJ8/1KnhEXIpgFJGkwJASRgYGtGzgbcz3ryKVRQAUhMJgAC4AAAgAElEQVTKpTCTHt/Hlk+Ax4bI8fOpXjgaLH00aahK0uos1CTnnJO0LjKccm2StkWO14/eM9taI8cXcvAZQMmbKDR73UKaFEC2hHIpLvcPBRuFAEoDTQoAJeGXR7t09dqQd46tngCkasXcmWpr8b/ITS4F4BXNatib5vXR84PZD2laIqk26bjHzI6nerGZHZOUnPJZJ2lRlmqTJDnnlklaGBl+I5ufASCB0GwgHrcuma3qCv+jyG2H2PIJKGU0KQCUhCcCWz3NrK7QbUubYq4GwHQWWk3x3MFO9Q74m6FAKXLO1Wh8hkJHmreJnr8q84omvE+6dfmuyVZtYz4oKXl1xj4zO5LlzwAgQrOBuMyoLNebAt+/txGeDZS0inwXAABxeOKAv0lx14oWVQXe5AAAnwdWz9M/PXN43Pi1oRE983qn3rZmfh6qAgpSi65/yD4oyf8DOexk5Dhbyx+j9zmRwT1O6vrGRNaWZjrnbpD0scjwo9m6/+hnzNX1oeapWJ7NGoBCQWg2EJ8tK5r1nKch8eLRi+ofHNaMyvI8VAUg32hSACh6xy706PD5Hu8cWz0BSNfGxbPVXFelCz0D4+Ye23uWJgXwazMjx71phGaPif4Aj94zU9H7+H9RmFhOahvNtvi6pFlJwyclfSUb90/yh5I+leV7AtMSodlAfO5a0aLP68C48YGhEf3y6EXdtdKfWwGguPFKAICi92RgqydJ2npTui8QAih15WVOb7nZ3+B8Yv9ZDQ7797QGSlD0oX1/Bvfom+SemSrk2j4h6R2RsT80s0waKQAmQWg2EK9bFjSooabSO+dbYQGgNNCkAFD0njhw3ju+trVBc+tnxFwNgGLwwGr/aomLvYN64dCFmKsBClb0h+z45UeTuxY5rsmwlqiCrM05925Jn40Mf83MfjDVewPwIzQbiFd5mdOW5c3euecJzwZKFts9AShqvQND2n7Y/8DwPrZ6ApChu1e2qLaqXL0Dw+PmfrzntO65kVVagMavTqjK4B7Vk9wzUwVXm3Nus6Rv6PoXyZ7V+GyKbPmqpG+nec1ySd/PQS1A3hCaDcTvzhUt+q9Xz4wbf+Vkty71DqixNpMfywCmM5oUAIratoMXgm9GkUcBIFMzKsv1lpvn6Ye7T42b+8lrZ/TZ31pD0CYgXY0cZ7J8Mbo6IXrPTBVUbc65WyT9WFJt0vBuSb9hZtEVG1lhZueUZpB5Ii4DKC6EZgPxu2uFP3fCTHrh0AW9fe0NMVcEIN/4iQugqD15wP/du7muSutYvg1gCt61zv/lqbtvUNvYTxeQxj+0r3XpP+Wum+SemYreJ/o5qchKbc65ZZJ+JqkpafgNSW81M//TUwBZQ2g2EL8lzbVqbfTvkkguBVCaaFIAKFpmFgzNvnfVHJWV8TYggMzde+Mc1VWVe+d+vOd0zNUABalTkiUdV0pKdxlja+Q4rTf/JxC9z8IM7jHl2pxzCyT9XNKCpOEOSfeb2dkMagKQholCs9fSpAByxjmnO1f4cyl42QcoTTQpABSt/Weu6HS3f3totnoCMFUzKsv1wOp53rmfvnYmuNUcUCrMrE/S8cjw4jRvEz1/f+YVXedA5HhRBveIXpNWbc65FiUaFG1Jw+eUaFBE/7kByIGJQrPXLqRJAeTSnYEtn45e6NWJi70xVwMg32hSAChaTwRWUZSXOd29klBbAFP3znULvOOX+4d4CwxIiD64X53m9TdPcr9MHZPUl3Rc55xbkurFo+cm50f0KLECItXrGyT9VNf//S5JetDMXk/1PgCmhtBsIH+2LPc3KSTp+YMXYqwEQCGgSQGgaIW2erptyWw11FTGXA2AYnT3yhbVV1d45364Z3yoNlCCdkWOt6R6oXPuBklLk4YGJe3NQk0yM5O0JzKccm2S7owc7xm956Scc3VKhGRvShq+KuntZrY7jRoATBGh2UD+zKmv1k3z/c1AcimA0sNPXQBF6WLPgHYev+idu4+tngBkyURbPj322lldGxqOuSKg4Pwocnx/GuHZD0aOnzSzbAVnS+NreyCNa6Pn/jCVi5xz1ZK+p+ubHP2S3m1m29P4fABZQGg2kF+hLZ+2HezUyEhKvX8ARYImBYCi9Mwb5xX6nYY8CgDZ9K71N3jHr1wb0rOv8xYYSt7zSgRoj2mTtDXFaz8UOf5+NgpK8oPI8UPOuZmTXeScq5f0UGR40tqccxWSviXp/qThQUkPmdkTk10PILsIzQby765Ak+JCz4AOnPX/+wmgONGkAFCUQls9tTbWaOXcSZ8/AEDK7loxR/Uz/Fs+/fiV0zFXAxQWMxuR9Ghk+FOTraZwzr1F0t1JQ1eUeMCfzdr2SHoxaWimpIdTuPRhSXVJx9vNbMJtqJxzZUr8c/jNpOERSf/dzKIrOgDEgNBsIP9uX9akijL/rwTkuwGlhSYFgKIzPGJ6+vXz3rk33zRXqe8yAQCTq6oo01tvme+de2zvWfUPsuUTSt4jSmQujLlX0sdDJzvnWiV9PTL8ZTOb8GmFc84if7amUNsnI8efcM7dM8Fn+Gr/yxQ+5yuS3p90bJI+bGZZbbwASB2h2UD+1VVXaNPi2d45cimA0kKTAkDR2dVxURd7B71zbPUEIBfeuc6/5dPVa0N6JtA0BUrFaHPhbyLDn3POfdU5t2BswDlX5px7jxJbRC1NOveUpL/PUW0/kfSzpKFKST91zn3UOVebVFudc+5jkn4yes6Y/zSzxyf6DOfcpyT9r8jwdyR1OOfuT/NP25T+wgB+hdBsoDCEcil+caQruNoJQPHx700AANPYE4GtnqorynTH8uaYqwFQCu5c3qKGmkp1941vkP5oz2k9GFhpAZSQRyRtkfSupLGPSPp959wxSd2SlklqjFzXJ+l9ZnYph7V9QNILo58vSTMkfUmJRsphSU6JLI0ZkesOSfpgCve/zzP226N/0vVpSX+dwXUAIgjNBgrDnSua9cWfjx/vHRjWy8cvqr2N7/BAKeD1AABF54n9/reWtyxv1ozK8pirAVAKEls+zfPO/XwfWz4Bo9kUD0n6ZmSqXIkGwEaNb1BckPQOM9uW49rOKtFI2B2ZqpF0i6TVGt+g2CXpPjNjqRQwDRGaDRSO9YsaVVfl/56+7dCFmKsBkC80KQAUlTPd/dp3+rJ3jq2eAOTSu9Yt8I73DgzrqQP+FV5AKTGzfjP7HSVWEOya4NQeSV+VtNrMnoqptmOSblcib+LUBKeeUiI4u93MOuKoDUD2EZoNFI7K8jJtDqyWIDwbKB1s9wSgqDw5wYPA+2hSAMihO5Y3a3ZtpTcT50d7Tutta/y5FUCpMbPvSPqOc26FpHZJrZKqJF2StE/SNjPrz+C+bop1DUj6vHPuC5JulbRe0tgvD+eUaKzsHF0Vks59t06lLgDZR2g2UFjuXNGixz3bNu/quKQr/YOqn1HpuQpAMaFJAaCohPIobpw3Uwtn13rnACAbKsvL9LY18/X//WL8y9WP7zunvoFh1QSWsgOlyMwOSjqY7zqiRpsQL47+AVCECM0GCstdK/3h2cMjph2Hu3T/av+2qgCKBz99ARSNa0PDweWgrKIAEId3rvVv+dQ3ODzhSi8AABAfQrOBwrJy7kzNqa/2zj3Hlk9ASaBJAaBo7Djcpd4BfzjtfatoUgDIvc1tTWqqq/LO/XjP6ZirAQAAUYRmA4XHOae7VvhXU5BLAZQGmhQAikboLeX6GRW6dcnsmKsBUIoqRrd88nl8/1n1XBuKuSIAAJBsotBsVlIA+XNnoEnxxrmrOns57agqANMMTQoARePJQB7FPTfOYW9ZALF511p/QHb/4EgwNwcAAMSD0GygMN25ojk4x2oKoPjx1A5AUTh8/qqOXuj1zr2ZrZ4AxOj/Z+++o+OqzjUOv3tUrW5JbpIty71bLuAGBoOpxpTQAoEkJEASSCMhIZTk3uSGEAIhlDSSUBNKApjesTEY3HCvcq9ykWXLliWra/b9QzYR43PULB1N+T1raWXN+c6MXtaKRzNnn/194/tmKDOJlk8AAASjxoZmx0ZziQToKD1SO6lfl0THGnMpgPDHX2AAYcHt7mRjpCmDunicBkAki/IZnT/ceTfF7PX7VEbLJwAAOgxDs4Hg1dhcCmutx2kAeIlFCgBhwW0eRV7PNGUkxXmcBkCku2Ck8yJFVa1fs/ILPU4DAAAkhmYDwc5tLkXh4SptLirzOA0AL7FIASDklVXV6rOtxY61M2j1BKADnJybri7JzguktHwCAKBjMDQbCG4T+mXIZ5xrn26k5RMQzlikABDyPt24XzV1zls/zxzMIgUA70X5jKYN7+5Y+2hDkUorazxOBAAAVuxkaDYQzFLiY5TXK82x9ummAx6nAeAlFikAhLzZLvMouiTHaVhWisdpAKDeBSOzHI9X1/o1k5ZPAAB47iOXFrEMzQaCh9tcigVbDqi2znknFIDQx19hACHNWus6j+KMQV3kc9srCgDt7KTendUthZZPAAAEg8qaOn26ybldzPi+GR6nAeDGbS5FWVWtVhQ474YCEPpYpAAQ0tbsPqx9pVWONVo9AehIPp/RtBHOA7TnbNivkgpaPgEA4JWFW4tVXl3nWJvK9wYgaIzOSVOnmCjH2lyXhUYAoY9FCgAh7UOXVk8xUcb1DgwA8Mr0kc6LFNV1fs1cS8snAAC8Msul1WJKfLTG9u7scRoAbuKiozSuT7pjzW03FIDQxyIFgJDmtkhxcm66kuNjPE4DAF80uldn9UiNd6y9tYqWTwAAeMFaq1n5zt8bpgzqqugoLo0AwcRtLsWyHQd1pKrW4zQAvMBfYgAh60BZlVYUHHKs0eoJQDBorOXTJxuLVFJOyycAANrbhsIy7TpU4VibOoTvDUCwceuKUFNn9dm2Yo/TAPACixQAQtbM/EJZ61w7g0UKAEHiApeWTzV1Vu+t2etxGgAAIs+sdc6tnqJ8RqcP7OJxGgBNGdw9WRmJsY61uRtp+QSEIxYpAISsFxcXOB7vnZGgvpmJHqcBAGeje6UpO62TY23GUuf3MQAA0HY+dGn1NLZ3Z6UlOF8IBdBxfD6jSS67KZhLAYQnFikAhKQtRWVavP2gY+3cYd1ljPE4EQA4M8a47qZYuLVYO4vLPU4EAEDkKD5SraU7nL83TGX3NRC0Tu2f4Xh83d5SFZVWeZwGQHtjkQJASHppifvdx5eP7elhEgBo2pdGZ7vWXlm2y8MkAABElo/W75PfpUUs8yiA4OU2l0KS5m1mNwUQblikABBy6vxWLy91vqiX1zNVA7sle5wIABo3pEeKhvZIcay9vLRA1m3ADgAAOCGz1jm3espJT1C/LkkepwHQXD07Jyg3I8GxNpeWT0DYYZECQMj5ZGOR9h6udKxdflIvj9MAQPNc5rLLa9uBci1xaV8HAABar6bOrznrixxrU4d0pUUsEOTcdlN8unE/N/kAYYZFCgAhx63VU2y0TxeNzPI4DQA0z8WjshTlc74YMsNldxgAAGi9RVuLVVpV61ibOribx2kAtNSpLosUu0sqte0Ac92AcMIiBYCQUlJeo/fXFjrWzh3WXakJMR4nAoDmyUyK05SBXRxrb67crcqaOo8TAQAQ3txaPSXGRmlcn3SP0wBoqYn9MuS24elTWj4BYYVFCgAh5fUVu1Rd63esXcHAbABBzq3lU2llrT5wWYAFAACt86HLIsVpA7soNprLIUCwS0uI1YjsVMfa3I0sUgDhhL/KAELKiy6tnnqkxrv2qwSAYDF1SFeldnLe8TVjqfP7GwAAaLktRWXauv+IY23qEFo9AaHC7Xv+vM37VednLgUQLlikABAy1u8t1cqCEsfaZWN6uvZ6B4BgERcdpQvzejjW5mwo0r7SSo8TAQAQntx2URgjTRnk3H4RQPBxm0txuLJWq3c5Xx8AEHpYpAAQMl5cvNO1djmtngCEiEvHOL9f+a302rLdHqcBACA8zcx3bqM4qleaMpPiPE4DoLXG9u6sOJf2bMylAMIHixQAQkJNnV+vLt/lWBuXm67czESPEwFA64zulaa+Lu9ZM5YWyFq2rQMAcCJKKmq0aNtBx9rUwV09TgPgRMTHROnkXOdB93NZpADCBosUAELC7HX7tL+s2rF2+UnsogAQOowxrgO01+0t1Zrdhz1OBABAeJmzoci1Vz3zKIDQ4zaXYvG2g6qorvM4DYD2wCIFgJDgNjA7ITZKF4xw7u8OAMHqktHZMi5jdF5e6rxrDAAANI/bPIqs1HgN7p7scRoAJ2ryAOdFiuo6vxZvL/Y4DYD2wCIFgKC3v6xKs12+aEwb0UOJcdEeJwKAE5Od1kkT+2Y41l5bvks1dX6PEwEAEB5q6/yavd75u8OZQ7rKuN0lACBoDe2RorSEGMcacymA8MAiBYCg9+qyXap12a59BQOzAYSoy1wGaB84Uq2P1xd5nAYAgPCwbOchHSqvcaxNHUyrJyAU+XxGp/Rz3k3BXAogPLBIASCoWWv14mLnVk+9MxI0ro/zAC0ACHbnDe+uhNgox9qMpc7vewAAoHGz8p13UXSKidLEfs67GAEEP7e5FGt2H1bxEef5lQBCB4sUAILa6l2Htb6w1LF2+ZiebNcGELIS46J1/nDnmTqz8vfpUDlftgAAaKkP1xU6Hj+lf6biY5xvDgAQ/E51WaSwVpq/+YDHaQC0NRYpAAS1F5fsdDxujHQZrZ4AhLjLxmQ7Hq+u8+uNlXs8TgMAQGjbWVyuDYVljrWpQ7p6nAZAW8rJSFCv9E6ONeZSAKGPRQoAQauypk6vLd/tWDu1f6ay0pw/oABAqJjQN0PZLu9lM5bQ8gkAgJaYle+8i0KSzhjEIgUQ6tx2UzCXAgh9LFIACFoz8wtVUuE89O5ydlEACAM+n9GXRjvvpli+85A2FznfDQoAAI43a53zPIrh2SnqnhrvcRoAbc1tLsWO4nLtOFDucRoAbYlFCgBBy21gdnJ8tM4d1t3jNADQPi51afkkSS8zQBsAgGYpq6rVwi3FjrWpg7t5nAZAe5jUz3mRQpLmbmY3BRDKWKQAEJT2llTqk41FjrUL87IYegcgbPTtkqTROWmOtVeW7pLfbz1OBABA6Pl0435V1/kda8yjAMJDemKshmWlONY+3cgiBRDKojs6QHMYY/pJGiepp6RYSQclrZM0z1pb2YG5jKQxkkZJOvapp1DSCklLrbVcVQBaacbSArldl7uCVk8AwsxlY3pq2Y5Dxx3fXVKp+VsOuG5tBwAA9dzmUXRJjtPwrFSP0wBoL6f2z9Sa3YePO/7R+n2qqK5Tp1huaARCUVDvpDDGXGKMWSJpk6TnJN0n6W5Jf5Y0S1KRMeaPxhhPv7kbY2KMMT+RtFPSYkmPSbrn6M/jR4/tMMbcaoyJOYHfk2qMOcsYc6cx5lVjzB5jjA34yT3x/yIguFhr9ZLLwNj+XZM0qpfzHccAEKouHJml2Gjnj2UM0AYAoHF+v9Xs9c7zKM4c1FU+n/E4EYD2cuoA50uAR6rrNNNlsRJA8AvKRQpjTJwx5hlJr6h+p4KbJEnfk7TWGHOaR9l6SVoo6X5J7k2k63d9/F7SfGNMY+cFvn53Y8zTxph81e8Y+UDSbyRdLIkm/IgIS7Yf1Nb9RxxrV4ztqfpNTAAQPlITYnT2EOd+2e+s3qsjVbUeJwIAIHSs3FWi/WXVjjVaPQHhZULfDKUnxjrWXlu+2+M0ANpK0C1SGGN8kv4j6ZqAUp2krZKWSyoJqHWR9I4xZmI7Z+sqabak0QGlCklrJOVLCmw/NVbS7Bbs9ugu6WuSBkviSiwiktvA7Cif0ZcaGTALAKHMbYB2RU2d3lm91+M0AACEjg9d7p6OjfbRMhEIMzFRPl0woodj7eMN+3So3HnBEkBwC7pFCkk/Vf2ugYYelZRjre1rrR0tKV3SpZJ2NDgnQdILxpj2bDb5lKR+DR5XSrpFUqa1dri1dqikTEk/1hcXKwZIeqINfn9ZG7wGENTKq2v15krnux+mDOyirsnxHicCAG+cNrCLMpOc7wqj5RMAAO5mrXNu9TSxb4YS40JiFCeAFrh4VJbj8Zo6q7dXcXMPEIqCapHCGJMh6a6Aw3dYa2+y1n5+1dJa67fWviJpkqRtDc7tqfoFgvbIdo6k8xscqpF0rrX2YWtteYNsR6y1D0o67+g5x1xojDmjBb+yRtIS1S/QXC9phCSmfSHsvbNqr45U1znWrjiJgdkAwldMlE8Xj3LeTTF/ywEVHCx3rAEAEMn2lFQ4DtGVaPUEhKsxOZ2VndbJsfba8l0epwHQFoJqkULSbZKSGzyeI+l3bidba3dJuiHg8I+OLna0tV8HPL7XWjvH7WRr7cc6Pvvdzfg92yRNkJRsrT3p6ALNE9ba1dZaf4sSAyHGWqvnP9vhWEtPjNWZg537tQNAuLhsjPti7KvL+MIFAECgD112UUjSmYNZpADCkc9nXHdTLNxarN2HKjxOBOBEBc0ixdFZFN8IOPxLa61t7HnW2lmSPmlwKFnSlW2cbYSkcQ0OHVH94Oym3Hf03GMmGWOGNPYEa+0ha+1Ca21Vy5MCoW3+5gNavP2gY+3iUVmKjQ6atywAaBdDs1I0uHuyY+2lJQXy+xv9WAQAQMT5MN95kWJw92T17JzgcRoAXnHbgSxJb6xggDYQaoLpit8k1Q/APmaLpI+a+dzHAx5f0haBGgickfGCtba0qScdPefFgMNtnQ0IC9ZaPThzg2v9irG9PEwDAB3n8rHOuym2HSjX7PXud4sCABBpSitr9Mmm/Y41dlEA4W1Q92TXm3teW84iBRBqgmmR4oKAxx80tYui4bkBj6cYYxLbINMxgdneb8FzA7NNP8EsQFiat/mAFm1z3kUxqV+GhmaleJwIADrGxaOyFeUzjrV/fLLF4zQAAASvmfmFqq517orMPAog/Lntpli757A2FjZ5bzGAIBJMixSjAh7Pa+4Tjw7V3tbgUKykoW2QScYYI2lkwOFmZ5M0N+Bx3tHXBHCUtVYPfuC+i+JHZw/0MA0AdKwuyXE6f3h3x9qCLcVavavE40QAAASnN1bscTzeLSVOo3p19jgNAK9dmNfDtcZuCiC0BNMiReCshrUtfH7g+Y3OfmiB3pIaNrI8Yq11nuzrwFq7XVJ5g0OJkuhbAzTw6ab9rrMoTu2fqZNz0z1OBAAd64bJfV1rj7GbAgAAlZTX6JONRY61aSN6uO5KBBA+enZO0Mm5zguSr63YpeY3aAHQ0YJikcIY00lSTsDhnS18mcDzB7U+UaOv09JcTs9pq2xAyGt6F8UAD9MAQHAY1SvN9QvXmyv3aE9JhceJAAAILu+t2auaOucLkBfmZXmcBkBHcWv5tLO4Qkt3HPI4DYDWCopFCkmZkhre5lAjqaWTIXcFPG6rBpSBr1PQitdor2xAyJuzcb/rB4fJAzI1tje7KABEJrfdFLV+q6fmbfM2DAAAQeaNlc6tXLLTOml0rzSP0wDoKNNG9FC0y86p15cHXo4DEKyCZZEiKeBxeQuGZh9zpInXbK3A1wn8Pc3RXtk8YYzpaowZ1pIfSf06OjeCn7VWD81030Vxy1nMogAQuc4a0k29MxIca88t3KGyqlqPEwEAEBz2l1Vp3uYDjrXpI3uIMZBA5EhPjNVpA7s41t5cuUe1dX6PEwFojWBdpKhsxWsE9j1or0WKYMrmlZslrW7hz2sdkhQh5eMNRVrmsovitIFdNLY3w+4ARK4on9H1p/ZxrJVW1urFxa3pQAkAQOh7Z/Ve1flp9QSg3sWjnP/dHzhSrU837fc4DYDWCJZFiviAx9WteI2qgMedWpklUDBnA0KWtVYPztzoWv/RWcyiAIDLx/ZUaqcYx9oTc7e6XqABACCcvbnCudVTbkaChmWleJwGQEc7e2g3dYqJcqy9vtz5/QJAcAmWRYrA3QmxrXiNuCZes7WCORsQsj5aX6QVO513UUwZ1EWjc9hFAQAJsdG6ZnyOY21ncYXeW7PX40QAAHSswsOV+mxbsWPtwrwsWj0BESghNlrnDOvmWHtvzV5VVNd5nAhASwXLIkVZwOPA3QvNEbg7IfA1WyuYs3nlL5KGt/Dn4g5JipBQv4uCWRQA0Bxfn5SrmCjnCy6PfbLF4zQAAHSst1bukdsEy+kjafUERKpLRmU7Hj9SXaeZ+YUepwHQUtEdHeCowIv2CcYY08Lh2YlNvGZrBb5O4O9pjvbK5glr7T5J+1ryHO5eQWNmr9+nlQUljrUzB3fVqF5pHicCgODVLSVeF+Vla8bSguNqS3cc0pLtB5nhAwCIGG+udG7dMrBbkgZ1T/Y4DYBgceqATHVOiNHB8prjaq8t3828GiDIBctOiv2SGi5IxEjq2sLXCFwybdFF9UYEvk7PVrxGe2UDQo61Vg81Movih1OZRQEAgdwGaEvspgAARI6Cg+VausO5ZSy7KIDIFhPl0wUjezjWPt6wT4fKWzNiFoBXgmKRwlpbIWlHwGHnBszuAs9f1/pEX7A+4HGvVrxG4HPaKhsQcmblu++imDq4q/LYRQEAxxmalaJT+2c61t5bs1c7DpR7nAgAAO+9tXKPa226y8VJAJHDreVTTZ3V26uY5QYEs6BYpDgq8ML90BY+f0gTr9da2yVVNHicaIzp3dwnHz03ocGhI5J2tlE2IKRYa/XQLGZRAEBrXD/ZeTeF30pPzN3qcRoAALz3hkurp2FZKerbJcnjNACCzZiczspOCxwLW++15bs8TgOgJYJpkWJ5wONJzX2iMaaHpNwGh2okrW2DTDo6F2NlwOFmZ5N0SsDjlS2ctQGEjQ/WFmr1rsOOtbOGdNOInqkeJwKA0DFlYBcN6Op8AeaFxTtVUnF8/10AAMLFtv1HXL9L0OoJgCT5fEYXjXJ+P/hsW7F2H6pwrAHoeMG0SPFmwOOzTPOnL58T8Hi2tbYth1MHZju7Bc8NPPeNE8wChKSmZlHcchazKACgMTuD2gEAACAASURBVMYY3eCym6K8uk7PfxbYORMIbsaYfsaYq40xPzXG3GWMudkYc6YxJr6DcxljzFhjzPXGmDuO/nzz6LHmfj8B0MbcBmZLtHoC8F9uLZ+sld5Y4f4+AqBjBdMixTzVD9A+pq+kKc187vUBj19ri0ANvB7w+ApjTJN7SY0xyZKuCDjc1tmAkPD+2kKt3eN859PZQ7tpeDa7KACgKRePylZmUqxj7am521Rd6/c4EdByxphLjDFLJG2S9Jyk+yTdLenPkmZJKjLG/NEY4zyIpf1yxRhjfqL61qyLJT0m6Z6jP48fPbbDGHOrMSbmBH5PqjHmLGPMncaYV40xe4wxNuAn98T/i4Dw8sYK53kUo3qlqVd6gmMNQOQZ1D1Zg7snO9ZeW84iBRCsgmaRwlrrl/RUwOH/bepuJWPMVEmTGxwqlfRCG2dbKWlRg0NJkm5rxlNvk5TY4PECa22btKECQonfzy4KAGgL8TFR+uqEXMfa3sOVenuV+0BRoKMZY+KMMc9IekXSmEZOTZL0PUlrjTGneZStl6SFku6X5HwLZr2ekn4vab4xprHzAl+/uzHmaWNMvqSDkj6Q9BtJF0vq3urgQITYUFiq9YWljjV2UQAI5Nbyae2ew9ro8l4CoGMFzSLFUb+T1LBN0+mSfuZ28tEvBo8FHH7YWrvf6fwGzwu8U2lKM7L9T8Dj2xv70mSMccr+82b8HiDsvL92r/JddlGcO6ybhmWxiwIAmuvaCTmKi3b+CPePT7aI0VcIRsYYn6T/SLomoFQnaavq59OVBNS6SHrHGDOxnbN1lTRb0uiAUoWkNZLyJVUG1MZKmt2C3R7dJX1N0mBJtIwCWuhNlxYtxjCPAsDxLspzf19gNwUQnIJqkeLo4sI9AYd/a4z5izHm83cYY4zPGHOJ6ltE5TY4d7ekB9op27uS3m9wKEbSe8aYHxpjPt9baoxJNMbcIundo+cc87a1dlZzfpcxZujRLeDH/TicforLuYEDu4EO0fQuioEepgGA0JeRFKdLx/R0rK3ZfVgLthR7nAholp+qftdAQ49KyrHW9rXWjpaULulSSQ0HrCRIesEY0553NDwlqV+Dx5WSbpGUaa0dbq0dKilT0o/1xcWKAZKeaIPf35az9ICwY63Vmyuddwqe3Dtd3VM7dIwNgCDUs3OCTs7t7Fh7bcUubuoBglB0Rwdw8DtJkyRNb3DsJknfMsZsV/0dVn0kpQU8r0LSldbaQ+2Y7WuS5h/9/ZIUL+kh1S+kbFH9XVF9jx5vaLOk61rwe26T9PVmnvuMy/Ht+uICDtAhZq3bp3V7nbdTnj+8u4b0SPE4EQCEvutP7eM6KPuxT7ZoYr8MjxMB7owxGZLuCjh8h7X23oYHjrZ/fcUY85mkT/Xfz7I9Vb9A8L/tkO0cSec3OFQj6Vxr7ZyAbEckPWiMWar6Vk3Hbka60BhzhrV2djN/ZY2kY61kF0n6TNJa1e8oAeBgze7D2rL/iGPtwjxaPQFwdtGobC3advC44zuLK7Rs5yGNyXFexADQMYJqJ4X0+ZeTKyT9O6AUpfoFgNE6foHigKRp1tq57ZytUNIZklYElDpJGiZpqI5foFgu6QxrbVF7ZgOC1b8WbHet/WAqsygAoDX6d03SmYO7OtZmrdunTfu4MRtB5TZJDSdYzlH9jUmOrLW7JN0QcPhHRxc72tqvAx7fG7hA0ZC19mMdn/3uZvyebZImSEq21p5krb3JWvuEtXb10e8/AFy47aLwGem84SxSAHB2wYgeivY5d1h8bdkuj9MAaErQLVJIkrW20lp7taTLVX+R380RSX+RNNRa+5FH2bZLGqf6eRONNbLbrfovZOOttTu9yAYEm237j2jOBuf1uWkj2EUBACfihsl9XGtPzN3qYRLA3dFZFN8IOPxL20SfhaNtUj9pcChZ0pVtnG2E6j/XH3NE9YOzm3Lf0XOPmWSMGdLYE6y1h6y1C621VS1PCkSu+lZPzl+7J/XLVJfkOI8TAQgV6YmxOm1gF8famyv3qLaOewSAYBKM7Z4+Z62dIWmGMaa/pPGSsiXFSjqk+gF2c621gUPsmvO6JzSszlpbLek+Y8zvVT80L0/SsdsZ96l+YWVpa++KstZep5a1hwKC0nMurUgk6YbJfT1MAgDhZ2LfDA3LStGa3YePq81YUqBbzx6ojCQu3qDDTVL9AOxjtkj6qJnPfVzS5AaPL5H017aJJen4GRkvWGude1Q2YK0tNca8qC9+Xr9E9d9PALSh5TsPqeBghWNt+kh2UQBo3MWjsvThun3HHT9wpFofrS/SWUO7dUAqAE6CepHiGGvtJkmbOjpHoKOLEMf6yQJooLKmTi8sdt5ENCwrRaN7BXZtAwC0hDFGN0zuox/9J7ALpVRV69c/Ptmq288f3AHJgC+4IODxB03tomh4bsDjKcaYxKPzIdpCYLb3W/DcD/TFRYrpkn57ooEAfJFbq6don9F5w7t7nAZAqDl7aDd1iolSRc3xo5+enr+NRQogiARluycAoe+tlXt0qLzGsXbthN4y5oQ2NAEAJE0fmaXuKYHjsOo9OXer9pa0eMMp0NZGBTye19wnWmt3q36WwzGxqp8Bd8JM/QeRkQGHm51NUuAsvDzDhxugTfn9Vm+5LFJMHpCptIRYjxMBCDUJsdGuC5qfbNyvjYVNbqAE4BEWKQC0i2cWOg/MTo6L1sWjsjxOAwDhKSbKp+tOyXWsVdX69dDMDd4GAo4XOKthbQufH3h+o7MfWqC3pIQGj49Ya937VAY4OqeuvMGhREm92igbAEmLtx/U3sPOi+3TR/J9AkDzfHVib9fa0/O3eZYDQONYpADQ5lbvKtGyHYcca5eN7amE2JDoNAcAIeGrE3orM8n5btIXFu/Upn3cIYaOYYzpJCkn4LBzL0h3gecPan2iRl+npbmcntNW2QBIrgOzY6N9OnsYLVoANM+YnM7Kc2k3PWPJLpW4dIAA4C0WKQC0uWdddlFI0rUTAq9VAABORGJctH44dYBjzW+l+95d73Ei4HOZkhq2QKqRdPz0ysbtCnjc9YQSub9OQSteo72yecIY09UYM6wlP5L6dXRuRIbaOr/eXuXc6mnKwC5KiY/xOBGAUPaNSbmOxysamaUJwFssUgBoU4cra/TqMue7nib0TVf/rskeJwKA8HfVuBzlZiQ41t5fW6gl24s9TgRIkpICHpe3YGj2MYFDsgNfs7UCX6c1w7jbK5tXbpa0uoU/r3VIUkSchVuLtb+s2rF2YR6tngC0zLQRPdQlOc6x9vT8barzt/TjCYC2xiIFgDb18pICVdTUOda+OiHX2zAAECFionz6ybnunWbufWedWn5tGDhhgRftWzPJvaKJ12ytYM4GRLw3Vjjf9NQpJkpTh4TUpiUAQSA22qdrxjt3dSg4WKFZ+YUeJwIQiEUKAG3GWqtnFjrPnOySHKdz6B0LAO1m2vAeGtkz1bG2aNtBzcpvaZcd4ITFBzx2vi26cVUBjzu1MkugYM4GRLSaOr/eXbPXsXbmkK7MtwPQKl8Zn6OYKONYe2reNm/DADgOixQA2syCLcXatK/MsXb1yb0UE8VbDgC0F5/P6PbzBrvWf/fuOrayw2uBuxOcJ7w3LrA3Q2t2PDgJ5mxe+Yuk4S38ubhDkiKizMrfp0Mug2wvHEmrJwCt0zU5XtNd3kPmbT6g9XtLPU4EoCGuGAJoM88scB6Y7TP1/dIBAO1rUv9MnTawi2Nt474yzVjamtnAQKsF3rkQuHuhOQJ3JzjfDdFywZzNE9bafdbaNS35kbS5o3MjvNXW+fXA++sda0lx0ZoyyPlvHAA0x3UuA7Ql6al5W70LAuA4LFIAaBP7DlfqPZdt2WcN6aasNDogAIAXfnae+2yKBz/YoEqXuUFAOwi8aJ9gjHHus+AusYnXbK3A1wn8Pc3RXtmAiPXikgJtdNmZfc7QboqPifI4EYBwktcrTWNy0hxrryzbpYNHWtP9EUBbYJECQJv496KdqnVpI3LthN4epwGAyDUsK1WXjHLeyr6npFJP03MX3tkvqeGHgxhJLZ14mx3wuK2GqwS+Ts9WvEZ7ZQMiUnl1rf7wwQbX+jdP7eNhGgDh6rpTnN9LKmv8+s/inR6nAXAMixQATlhtnV/Pf+Y8MDs3I0Gn9s/0OBEARLZbzxnkOhjwz7M3qcSl1zfQlqy1FZICPyC0tP9j4PnrWp/oCwL7yfRqxWsEPqetsgER6R9ztqqoNHAefb1LRmVpeHaqx4kAhKPzh3dXt5TAsVL1/jV/u2rr/B4nAiCxSAGgDcxat097SpxnRV4zvrd8vpZ2dgAAnIhe6Qm6ZrzzLrbDlbX6y8ebPE6ECBZ44X5oC58/pInXa63tkioaPE40xjR76+fRcxMaHDoiidsvgVbaV1qpv81xHnkSG+XTree4tzIEgJaIifLpWpfPybsOVWhmfqHHiQBILFIAaANuA7Pjon26fGxruicAAE7U98/sr6S4aMfak3O3afehCsca0MaWBzye1NwnGmN6SMptcKhG0to2yCRrrZW0MuBws7NJOiXg8cqjrwmgFR6auVHl1c4zk647JVe90hMcawDQGlePz1FslPMl0SfnbvM2DABJLFIAOEFb9x/RJxv3O9amj8xS58RYjxMBACQpIylO3zqtr2Otutavh2a69/0G2tCbAY/PasHw7HMCHs+21rblcOrAbGe34LmB575xglmAiLVpX6n+s8h5I1Jqpxh9d0p/jxMBCHeZSXG6yGWG28KtxVqzu8TjRABYpABwQp5b6LyLQpK+OpGB2QDQka4/tY8yk5x77r60pEAbCks9ToQINE/1A7SP6StpSjOfe33A49faIlADrwc8vsIYk9TUk4wxyZKuCDjc1tmAiHHvO+tU53feiPT9M/srNSHG40QAIsF1k3Jda0/P2+ZZDgD1WKQA0GqVNXV6YXGBY214doryejLcDgA6UmJctH541gDHmt9K970bODsYaFvWWr+kpwIO/29TuymMMVMlTW5wqFTSC22cbaWkRQ0OJUm6rRlPvU1SYoPHC6y1bdKGCog0C7Yc0Mz8fY61XumduOkJQLsZnp2qk3M7O9ZeXb5bxUeqPU4ERDYWKQC02psr96ikosax9tUJvdX8bg4AgPZy1cm9lJvh3Mt7Zn6hFm8r9jgRItDvJDVs03S6pJ+5nWyMyZb0WMDhh621zv0l//s8G/AzpRnZ/ifg8e3GmNMa+R1O2X/ejN8DIIDfb3XP2/mu9dvOHay46CgPEwGINNdN6uN4vLrWr+c/2+FxGiCysUgBoNXcBmYnx0frwjzn/o4AAG/FRPn003MHu9bvfWedmPeL9nR0ceGegMO/Ncb8xRjz+QcGY4zPGHOJ6ltE5TY4d7ekB9op27uS3m9wKEbSe8aYHxpjPl/dM8YkGmNukfTu0XOOedtaO6s5v8sYM9QYc5bTj8Ppp7icGziwGwhZb6zcrZUFzn3f83qlafrIHh4nAhBpzhnWTT1S4x1rzyzYrpo6v8eJgMgV3dEBAISm1btKtHznIcfa5WN7KiGWtxcACBbTRnRXXs9UrXC4GLR4+0HNzN+ns4d264BkiCC/kzRJ0vQGx26S9C1jzHZJJZL6SEoLeF6FpCuttc4fOtrG1yTNP/r7JSle0kOqX0jZIsmofpZG4FWMzZKua8HvuU3S15t57jMux7friws4QEiqqq3T/e+5txy88/zB7MoG0O5iony6dkJvx/ejPSWVen9NoS5gwRTwBDspALSK2y4KSbpmPL1jASCYGGP0s/Pdd1P85q21qqiu8zARIs3R2RRXSPp3QClK9QsAo3X8AsUBSdOstXPbOVuhpDMkrQgodZI0TNJQHb9AsVzSGdbaovbMBoSrf87broKDFY61s4Z00/i+GR4nAhCprh6Xo7ho58ujT87d6nEaIHKxSAGgxUoqavTq8l2OtUn9MtS/a5LHiQAATZnUL1OnD+ziWNt2oFz3vbfO40SINNbaSmvt1ZIuV/1FfjdHJP1F0lBr7UceZdsuaZzq503sbuTU3arfETHeWrvTi2xAuDlUXq0/frjRsRblM7q9kUV1AGhr6YmxumRUtmNt8faDWuXSlg5A26IfC4AWe3lpgSprnHszXjuBXRQAEKx+dt5gzdlYJKcRFE/O3aazh3bTpH6Z3gdDRLHWzpA0wxjTX9J4SdmSYiUdkpQvaa61trIVr3tCvWGstdWS7jPG/F7SWEl5kroeLe9T/cLK0qO7Qlrz+tepZe2hgLD059mbdLiy1rF21cm9uOEJgOe+PilX/1nsfO/BU/O26YEr8zxOBEQeFikAtEhJeY0e/XizY61rchw9zQEgiA3NStHlY3rqxSUFjvXbXlqpd285TUlxfERE+7PWbpK0qaNzBDq6CLHo6A+ANrSzuFxPz3NuG5sYG6VbzhrocSIAqP+MPL5PuhZuLT6u9saK3bpj2mBlJsV1QDIgctDuCUCL/O/rq1V4uMqxdtW4HMVE8bYCAMHs5xcMVbcU5y9ZBQcr9Ju38j1OBACIFPe/t17Vdc6bkb59ej91SeYiIICO8Y1Tch2PV9f59dzCHd6GASIQVxMBNNs7q/bo1eXObZqjfEZXj+vlcSIAQEulJsTod5eNdK0//9kOfbR+n4eJAACRYGXBIb2+wvm7RNfkON0wuY/HiQDgv84a0k3ZaZ0ca499skUHj1R7nAiILCxSAGiWotIq3fnKKtf6dZNy1SPV+Q86ACC4TBnUVVePy3Gt/2zGSpWU13iYCAAQzqy1je7Uu/WcgUqIpdUggI4THeXT1yY6z9g8XFmrh2dt9DgREFlYpADQJGut7nh5lQ66XLDq2yVRPz13kMepAAAn4q4LhqhnZ+fF5cLDVfrlG2s8TgQACFez8vc59nqXpIHdknT5WHZkA+h4Xz65lxJjoxxrzyzYrs1FZR4nAiIHixQAmjRj6S7NzC90rPmM9MAVeYqPcf5DDgAITklx0br/8jzX+ivLdund1Xs9TAQACEc1dX799h33XRR3nD9EUT7jYSIAcJaWEKubpvRzrNX6rX779jqPEwGRg0UKAI3adahCv3rd/W7am6f01+iczh4mAgC0lYn9MlyHBErSXa+s0oGyKu8CAQDCztPztmlz0RHH2qR+GZoyqIvHiQDA3Q2T+yorNd6xNjO/UPM27/c4ERAZWKQA4Mrvt7rtpRUqrap1rA/tkaIfTB3gcSoAQFu67dzB6puZ6Fg7cKRaP391tay1HqcCAISD/WVVenimex/3O6cNkTHsogAQPOJjonTbeYNd63e/ma86P5+NgbbGIgUAV/9asF1zNx1wrMVG+fSHL+cpNpq3EQAIZZ1io/T7K/Pk1mnjndV79fqK3d6GAgCEhQfeX+96w9OlY7I1PDvV40QA0LSL8rKU19P5/WntnsN6eWmBx4mA8MfVRQCOthSVNdo79kdnD9Tg7ikeJgIAtJcxOZ317dOd++9K0v+8tkaFhys9TAQACHWrd5Xo34t2OtYSY6N0eyN3KgNAR/L5jH4+fahr/f731qu82nkBFkDrsEgB4Di1dX7d+uIKVdb4HetjctL0rdP6epwKANCebjlrgAZ1S3aslVTU6PYZK2n7BABoFmutfvXGGrn92fjumf3VNcW55zsABIOTc9N1/vDujrV9pVX6+5wtHicCwhuLFACO87c5W7RsxyHHWqeYKD1w5ShFufUFAQCEpLjoKD1wZZ6iXd7fZ68v0guLne+IBQCgoTdW7tGibQcda70zEnT9qX08TgQALXf7+YMVE+X82fhvH2/R3hJ2GgNthUUKAF+Qv+ewHpq5wbV+x7TB6uMyYBUAENqGZ6fq+2cOcK3/+s18FRws9zARACDUlFfX6rdvu7eN/fkFQxUXHeVhIgBond4Zifr6xFzHWkVNnX7//npvAwFhjEUKAJ+rrvXrxy+sUE2d877sU/tn6trxvT1OBQDw0s1n9NMIl0GmZVW1+umLK+X30/YJAODs0Y+3aI/L3cWTB2TqrCFdPU4EAK33/TMHKC0hxrE2Y2mBVu8q8TgREJ5YpADwuYdnbVD+nsOOteT4aN13+Uj5aPMEAGEtJsqnB67MU2y088fE+VsO6K8fb/Y4FQAgFBQcLNffXP5GRPmM/mf6UBnD9wkAoSM1IUa3THXeaWyt9Ju38pnbBrQBFikASJKW7jiov37kftHplxcOU1ZaJw8TAQA6ysBuybr17IGu9QfeX69PNhZ5mAgAEAp++/Y6VdX6HWtfm9hbA7ole5wIAE7cNRN6q69L2+v5Ww5oZv4+jxMB4YdFCgCqqK7TT15YIbfuHecM7aZLx2R7GwoA0KFumNxXY3t3dqz5rfSD55dp16EKj1MBAILV/M0H9NaqPY61zgkxumWq++I3AASzmCif7pg2xLV+z9v5qnZZoAXQPCxSANAfP9yoLfuPONbSE2N1z6Uj2JYNABEmymf0wBV5Soh1Hm56sLxGNz+zRFW1dR4nAwAEm9o6v371xhrX+k/OHaRUl57uABAKzhrSVRP7ZjjWtu4/omcXbvc4ERBeWKQAItz6vaX6+5wtrvV7vjRcmUlxHiYCAASL3MxE/e6yka71FQUl+tUbaz1MBAAIRv9etFPr9pY61ob0SNFVJ+d4nAgA2pYxRnddMERu928+PGujSsprvA0FhBEWKYAI5vdb3fnKKtW69Hn60uhsnTe8h8epAADB5MK8LF1/ah/X+nMLd+iFxTs9TAQACCYl5TV64P31rvVfXjhUUT52ZQMIfcOzU3XZmJ6OtUPlNfrjhxs9TgSEDxYpgAj270U7tWT7QcdaZlKsfnnhMI8TAQCC0e3nD9a43HTX+s9fXa3Vu0o8TAQACBYPztyggy53D18wsofGu7RHAYBQ9JNzBqlTjHM71Kfnb9M2l1baABrHIgUQofaVVured/Jd67+YPpS+sQAASfXDAv90zWh1TXZu/1dd69d3nlmiQ+XVHicDAHSkDYWl+tcC5z7scdE+3XH+YI8TAUD76p4ar2+f3texVlNnde876zxOBIQHFimACHX3m/k6XFnrWJs8IFMX5WV5nAgAEMy6Jsfrz9eMUbRLy46CgxW65T/L5XdpIQgACC/WWv3fG2tV5/K+/53T+6ln5wSPUwFA+/vWaX3VLcX55p131+zVmyt3e5wICH0sUgAR6OMNRXp9hfMfzbhon+6+ZLiM2zQoAEDEOjk3XXddMMS1/tH6Ij1CL14AiAgfrC3Up5v2O9ayUuP1ndP7eZwIALyREButn5wzyLV++4xV2krbJ6BFWKQAIkxFdZ1+/uoq1/oPpg5Q74xEDxMBAELJdZNyG91t9/CsjZq9bp+HiQAAXqusqdPdb7m3jr1j2hB1inXu2Q4A4eCyMT01LCvFsVZWVaubn12qypo6j1MBoYtFCiDC/PHDjdpZXOFYG9gtSTdOdu6tCACAJBljdO9lIzSwW5Jj3Vrph/9eph0Hyj1OBgDwytPztmlHsfP7/Lg+6Zo+sofHiQDAWz6f0a8uGiaXTqjK33NYv3pjrbehgBDGIgUQQdbvLdXf52xxrd/zpRGKjeZtAQDQuITYaD167VglxUU71g9X1uo7zyzh7jEACEPFR6r1p9mbHGvGSP974VBaxwKICCflputHZw10rT//2Q69sqzAw0RA6OJqJBAh/H6rO19ZpVqXwXZXj8vRSbnpHqcCAISqvl2S9Psr8lzra/cc1l2vrJa1DNIGgHDyyKyNKq2sdaxddXKOhmWlepwIADrOd8/or9MGdnGt3/nyam3aV+phIiA0sUgBRIh/L9qpJdsPOtYyk2J1+3mDPU4EAAh15w3vrpumuA9GnbG0QI99stXDRACA9rSlqEzPLNjuWEuKi9at57jfUQwA4cjnM3rwyjx1T4l3rFfU1OnmZ5eqvNp5cRdAPRYpgAiwr7RS977jPtjuF9OHKjUhxsNEAIBwcevZA3VK/wzX+m/eztfv31vPjgoACAP3vrPOdWf2zWf0U2ZSnMeJAKDjZSTF6U9fGa0olwEVGwrL9PNX2WEMNIZFCiAC3P1mvg67bMk+bWAXXZSX5XEiAEC4iI7y6ZGrRqtHqvPdY5L0p9mbdPuMVaqt83uYDADQlhZuOaD31xY61rLTOumbp/TxOBEABI+TctN127mDXOsvL92lFxcznwJwwyIFEOY+3lCk11fsdqzFRft098XDGWwHADghGUlx+uu1YxUb5f7R8j+Ld+o7zyxlmDYAhCC/3+qet913Zv/03EGKj4nyMBEABJ8bJ/fVWUO6utZ/8dpq5e857GEiIHSwSAGEsYrqOv381VWu9R+eNUA5GQkeJgIAhKtRvdL060uGNXrOzPxCffXxhSopr/EoFQCgLbyxcrdWFJQ41kZkp7IzGwBUP5/i91fkKTutk2O9qtav7z67VGVVzKcAArFIAYSxP364UTuLKxxrg7ol68bJfT1OBAAIZ18+OUf3XjpCLu14JUmLth3UlX+br70lld4FAwC0WmVNne57d71r/a4LhsjX2Bs/AESQtIRY/ekroxUT5fy+uGX/Ed3x8irmUwABWKQAwtT6vaX6+5wtrvV7Lh2umEbacgAA0BpXjcvRo9eOVVy0+9+Y9YWluuyv87RpX5mHyQAArfHk3G3adcj5xqezh3bThL4ZHicCgOA2Oqez7pw2xLX+xordenbhDg8TAcGPK5RAGNpxoFzffGqRav3OK/NfGZ+jsb3TPU4FAIgU5wzrrn9dP14p8dGu5+w6VKErHp2nZTsOepgMANASB8qq9JfZmxxr0T6j288f7HEiAAgN103K1fnDu7vW/++NtVq9y7mNHhCJWKQAwsyWojJd+bf5rnc7ZSbF6Wfn8mUCANC+xvVJ1wvfmahuKXGu5xwsr9FX/rFQs9fv8zAZAKC5Hpm1UaUuvdOvGZ+jfl2SPE4EAKHBGKPfXT5SOenOc0Cr6/y6+dmlifMioAAAIABJREFUzGoDjmKRAggjGwtLdeXfFmjvYfc+3/9z4VClJsR4mAoAEKkGd0/RjJsmqW+XRNdzKmrqdOPTizVjSYGHyQAATdlcVObajiQ5Llo/mDrA40QAEFpS4mP0l2vGKNalDeqO4nJd9Y8F2tfINRwgUrBIAYSJ/D2HddXfF2h/WZXrOWcO7qoLR/bwMBUAINL17Jygl74zSXm90lzPqfVb3friCv3lo00MEQSAIHHvO+tc28fefEZ/ZSS575QDANQbnp2q/5k+1LWev+ewLv3rPG0uYlYbIhuLFEAYWL2rRFf/Y4EOHKl2PWdUrzQ9+OVRMsZ4mAwAACk9MVbP3zhepw/s0uh59727Xjf+c4kONvL3DADQ/hZsOaAP1hY61rLTOukbp+R6GwgAQtg143N0UV6Wa73gYIUu/yuz2hDZWKQAQtyyHQd19T8W6FAjfQxPzu2sf10/TqmdaPMEAOgYCbHReuzrJ+lLo7MbPW9mfqGmPfKJFm8r9igZAKAhv9/qN2/lu9ZvO2+Q4mOiPEwEAKHNGKN7Lh3RaAvUz2e1rWNWGyITixRACFu0rVjXPrZQpZXOw+wkaWLfDD31jXFKjmeBAgDQsWKifHrgijzdOLlPo+ftKanUl/++QH+evUl+l1YjAID28fqK3Vq1q8SxNrJnqi4c6X43MADAWVJctJ7+xjjlZjgP0pbqZ7Xd8M/FenHxTg+TAcGBRQogRM3bvF9fe/wzHamucz1n8oBMPXHdyUqMi/YwGQAA7nw+o7suGKo7pw1u9Lw6v9X9763X15/8TEWl7vOWAABtp7KmTve9u861fte0IfL5aB8LAK3RKz1BL900SSOyU13PqfNb/fSllfrzbGa1IbKwSAGEoI83FOkbTy5SRY37AsWZg7vqH187SZ1i2YoNAAg+3zqtnx6+apQ6NdEy5JON+zXtkU80d9N+j5IBQOR6Yu5W7S6pdKydM7SbxvfN8DgRAISXzKQ4/ftbEzR5QGaj593/3nr98vU1qmNXMSIEixRAiJmVX6gbn16sqlq/6znnDuumR68dS69YAEBQu3hUtl7/3ika1C250fOKSqt07eML9Yf316u2zv3vHwCg9Q6UVekvszc71qJ9Rref3/gOOABA8yTGRevxr5/c5Ky2p+dv1w+eX6bKRm5QBcIFixRACHln1R5955klqm7kAs30kT30p6+MUWw0/7wBAMFvQLdkvfrdU3T1uF6Nnmet9MiHm/SVxxZqr8tdvgCA1vvDBxtUVuU86+7aCb3Vt0uSx4kAIHzFRtfPavv2aX0bPe+tVXt03ZOf6XBljUfJgI7BVUwgBFhr9efZm3Tzc0tVU+e+1e/S0dl66MujFBPFP20AQOjoFBul3146Uo9cPVpJTcxR+mxrsc5/eI5mr9vnUToACH/zNu3Xswt3ONaS46P1g6kDPE4EAOHP5zO6Y9oQ/fyCIY2et2BLsa58dD436iCscSUTCHLl1bX63vPLdP9769XYzKQvn9RL91+Rp2gWKAAAIeqivCy9+f1TNTw7pdHzDpbX6BtPLdJ3n1uqncXlHqUDgPB0uLJGP3lxhWv9e2f0V3pirIeJACCy3DC5rx6+apRioozrOev2lur8h+fozZW7PUwGeIermUAQKzhYrsv/Ol9vrdzT6HnXTsjRby8doSif+x80AABCQW5mombcNEnfOCW3yXPfWrlHU//wsX737jqVsgUeAFrlV6+vdR2WnZ3WSV+flOttIACIQBePytaT141TYqz7bNGD5TX63nPL9N3nlqr4SLWH6YD2xyIFEKQWbjmgi/40V2v3HG70vG+e0ke/vni4fCxQAADCRFx0lP73wmH621fHKiW+8fZP1bV+/fWjzTrj9x/p+c92qM7fyLZDAMAXvLt6r2YsLXCt33PpCMXHuF8wAwC0nVMHZOo/356ozKS4Rs97a+UenfPgHH2wttCjZED7Y5ECCEL/WrBd1zy2sMmV8R9MHaBfTB8iY1igAACEn3OHddfbP5ysMTlpTZ67v6xad7y8Shc88ok+3bjfg3QAENr2l1XprldWudavnZCj0wd28TARAGB4dqpevmmScjMSGj1vf1mVbvznYt36wgqVVLCjGKGPRQogiFTX+nXnK6v0i1dXq7aRO0HjY3z601dG68dnD2SBAgAQ1np2TtB/vj1RN03p16zz1+0t1bWPL9T1Ty3S5qKydk4HAKHJWqs7Xl6lAy43RfXOSNCd0xof5AoAaB85GQl66aZJGtWr6Rt1Ziwt0HkPzdGcDUUeJAPaD4sUQJDYX1alax5boOcW7mj0vOy0Tppx0yRNH5nlUTIAADpWTJRPPztvsJ6/cYIGd09u1nNmrduncx+co1++vkaHyunZCwANvbSkwLVNiM9If7gyTwmxjbfbAwC0n8ykOL3w7Yn6wZn9m5w/uqekUl974jPd+coqlVXVepQQaFssUgBBYPWuEl30x0+1aNvBRs8b1yddr3/vFA3LSvUoGQAAwWNivwy99YPJ+u2lI5SZFNvk+bV+q6fmbdPp93+kR2Zt1IGyKg9SAkBw21lcrl+9sda1/p3T+2ls73QPEwEAnMRG+/TjcwbplZsnqX/XpCbPf27hDp3/8Bwt2HLAg3RA22KRAuhgr6/YrcsfnafdJZWNnnfthBw9e8N4ZTQxQAkAgHAW5TO6elyOZv9kim6a0k+x0U1/nC2pqNEfPtigifd+qNtnrNSGwlIPkgJA8PH7rX7y4grXO22H9EjRLWcN9DgVAKAxI3um6c3vn6pvn9ZXTXX83llcoav+vkD/98ZaVdXWeRMQaAMsUgAd6IlPt+oHzy9TZY3f9Zxon9FvvjRcd18yQjFR/JMFAECSkuNj9LPzBmvWj0/X9JE9mvWc6lq//r1op855cI6++vhCfbR+n/yNzIACgHDzxNytWri12LEWG+XTg1/Oa9biLwDAW/ExUbpj2hC9+O2JTQ7Vlurf7698dL4KDpZ7kA44cXz6ADrIjCUF+r833bdZS1JGYqyeu3GCrhnf26NUAACEll7pCfrTV8Zoxk0TldeM4YLHfLJxv657cpHOeWiOnl24XRXV3GkGILxtLCzVfe+td63fes5ADe6e4mEiAEBLnZSbrrd/OFnXTcpt8twVBSWa/sdP9dH6fe0fDDhBLFIAHeCDtYW6bcbKRs8ZlpWi179/qsb1oR8sAABNGds7Xa/cNEkPfXmUeqTGN/t5m/aV6a5XVmvSvbN0/3vrVHi48faLABCKaur8+tELy1Vd67yD++Tczrphcl+PUwEAWiMhNlq/vGiYnrthvLLTOjV67qHyGn3jqUV6aOYGdhAjqLFIAXhs/uYD+u5zS1XXyB+HC/Oy9NJ3JjX5xwYAAPyXz2d0yehsfXjrFN169kAlxEY1+7kHy2v059mbdervPtR3n12qt1ftYXcFgLDxxw83afWuw461hNgoPXDFKEX5mmh0DgAIKpP6Z+rdWybr6nG9Gj3PWumhmRt13VOLVHyk2qN0QMuwSAF4aFVBiW7852LXO5gk6afnDtIjV41SpxZcWAEAAP/VKTZK3586QPNuP1M/O2+wuqc0f2dFTZ3VW6v26OZnl2rMrz/Qzc8u0Zsrd+uIy5BZAAh2y3ce0p9nb3Kt/2L6UOU0o785ACD4JMfH6LeXjtST3zhZXZLjGj13zoYiXfjHT7V85yGP0gHNxyIF4JHNRWX6+pOfqayRixx3Thus757RX8ZwFxMAACcqLSFWN03pp09+doYevmqU8nqmtuj5FTV1envVXn3vuWUa8+sP9O1/LdZry3c1+rccAIJJRXWdfvzCctdd3GcM6qKrTm78DlwAQPA7Y1BXvdWMluG7DlXoikfn6V/zt8la2j8heER3dAAgEuw+VKGvPraw0W11N03pp2+d1s/DVAAARIaYKJ8uHpWti/KytGT7QT3+6Va9t2avWtKWt6rWr/fWFOq9NYWKjfbp9IFdNG1Ed00d0k0p8THtFx4AWqmotEp3v7VWW4qOONbTEmL0u8tGcoMUAISJrinxeu6G8br/vfX625wtrufV1Fn94rU1WrL9oO65dIQSYrk8jI7H/wuBdlZ8pFpffXyhdpe4D+K8elyObjt3kIepAACIPMYYnZSbrpNy07WzuFxPz9um/yzaqdIW7oyorvXrg7WF+mBtoWKjfJo+soe+PilXeb3S2ik5ADTf2t2H9cTcrXp9+W5V17m3mf3NJSPUtQXt8AAAwS86yqc7pg3R6JzO+umLKxr9nPvq8t1au+ew/nrtWPXrkuRhSuB4LFIA7aisqlbXPfmZNrvcvSRJF4zoobsvGc4dTAAAeKhXeoJ+Pn2ofnjWAL24uEBPztuqncUVLX6d6jq/Xl62Sy8v26XROWm6blKuzh/eQ7HRdFUF4B2/3+rDdfv0+KdbNX/LgSbPv3hUli4Y2cODZACAjnDe8O4a1D1ZNz2zROv2lrqet6GwTNMf+VTj+qRrVK80jeqVprxeaUpPjPUwLcAiBdBuKmvqdOPTi7WyoMT1nMkDMvWHL+cpyscCBQAAHSE5PkbfPLWPvj4pV/M279fbq/bqvTV7G23R6GbZjkNatmO57k7O17Xje+sr43OaHGAYyYwx/SSNk9RTUqykg5LWSZpnrXXfgtr+uYykMZJGSep69HChpBWSlto2bOBsjMmQdIqkfpISJR2RtFnSXGtt01eaEfGOVNXqpSUFenLuVm07UN6s53RLidP/XTS8nZMBADpan8xEvXLzKbrr1VV6eeku1/Mqaur08YYifbyh6PNjOekJny9YjOqVpmFZKYqPifIiNiIUixRAO6it8+sHzy9r9C6m0TlpevTasYqL5k0eAICOFuUzmjygiyYP6KJfXzxMn20t1tur9+jd1YXaX1bVotcqKq3SgzM36E+zN2r6yCxdRyuoLzDGXCLpF6pfCHBSZox5StKvrLX7PcwVI+mHkm6RlO1yWoEx5iFJj1hra07gd+VJ+j9J0yU5bbupM8a8JekX1tqVrf09CF+7DlXon/O26fnPduhwZfNb1vmMdP/leUpNYJYOAESCTrFReuCKPJ3UO12/fH1No20AG9pRXK4dxeV6fcVuSVK0z2hIjxQN6JakuGifonxG0b5j/2sa/K9P0VH/fZyRFKseqZ3UIzVe3VLiWeiAK8Mkd7QHY8wwSauPPV69erWGDRvWLr+rrKpWO4ubd9eQVx7/dKteWlLgWh/YLUkvfHui0hLYPgcAQDCr81st2last1ft0Tur96qotGULFsccawU1sFtyGyc8XreU+Hbdor9mzRoNH/6Fu7CHW2vXNPU8Y0ycpMclXdPMX1Uk6XJr7ZyWp2wZY0wv/X979x1vWVkeevz3zDADMwMOTaQJA6hEwIqKYhssYEEwUWyxoBiTGG8sUaM3XsVoVFQikiuaTxSHRP0oxN5Q5IINGyAiTSNKHYogQ5nCtOf+sfaBddacsvtae5/f9/M5H1jvXuWZPXve86z9vs+74KvAI9o85ALg6Mycflri9Nd6PfBh2pswthF4U2b+W6fXGYRh5vi3r9nAyts7X4JtlG3clKxau55Vazawau0Gbl+zntvWbGDVmg3cvnbi/9dz+9oN3Lp6PZ3eym+3zVac/KJHcNif7TL7zpKksXPxdav4289cyPWr6vv9uuOShex6n23Ybek27Lp04r/FIMYOixfiSujT2337RSxdNLhJBt3m+P0yEpUUloJbCj6TX127ir/85M/qDqNte+6wiP981SEOUEiSNALmzwseu+9OPHbfnTj+OQdywTW3FQMWv76RG+9oPw2dWApqGN555AG86gn7DOVa7YqIecAXgKMrL20CrgFuB/YBlpZeuy/w7Yh4Wmb+ZICx7QKcQ5Fnl60Ffk9R6bAPUH7C8MHAORFxaCfVHhHxJuDEKV66AVgJ7A6UHxSwFXByRERmntzudcbBub+9mdd/fjj/ZsbdHtsv4thDl/GCR99/oF9uSJKa7aF7bs83//4JvOELF3Hub/44+wED8KfV6/nT6vVcdsMdtVx/lJ384kdw1MN2rzuMgWn0IIWl4JaCj5udt13Ifx13CLsu3Wb2nSVJUqPMmxc8etmOPHrZjrzj2Qfwvctv4rTzruK8K+f0fJF2vYUtByg+AbwnM1fCPQMZRwMnAXu19lkMnB4RB2Xm9A/66s0KJg9QrAPeBvxHZq5pxbYEeA3wPu4drHggcCpwVDsXiYhDgQ9Wms8F/iEzLyzt9yiKSosnl/Y7MSJ+mpk/b++PJMHBe+/Aqx6/D0cceD+2mj/VraQkaa7ZfvFCTn3Fo/nkj37PKedeyao1XX9lKfVVIwcpOigF3xZ4HfDCiGhaKfieFDcXL46IQZaCz6e4MXpWRDSmFFxb2m7rrTjtVY9hn52X1B2KJEnq0fx5wREH7soRB+7Kb268kxXnXcWXf3kd6za0t87vXNKqCP6nSvPbM/MD5YbM3Ax8OSJ+DvwIWNZ6aU/gTcC7BhDb4cAzS00bgCOq9xWZuRr4SERcCJwFTExHf05EHJaZ57RxuQ9R5O4Tvk6xnNWkp7Rn5vmtuL4EPLvVvFXr+PLAhbSF+fOCZz1kN457wj483GfhSJKmMG9e8Jon7cfLHruMi65dxUXXruJXrf92Uiks9VPjBiksBb/nWpaCj5Gtt5rHp459NAfuvnT2nSVJ0kjZf9fteP9fPIR/fMb+nH7+tZx23tW1rvXbQG8Fyg/j+AFwwnQ7Z+b1EfFq4Hul5jdGxMkDWOb0PZXtD8w08Skzvx8RJwDvKDW/l2JZ1mlFxDOBQ0tNtwLHVQcoStdZHxGvAi4Ddmo1Pykinp6ZZ810Lc1NSxct4CWH7MXLH7c3uy1dVHc4kqQRsGjhfB633048br+d7mm78fZ1xaDFdau46JpVXHzdKlav31RjlJorGjdIgaXgloKPma3mBR9/6SN5zD471h2KJEkaoO0XL+Q1T9qP456wL2dffhMrXApqIm9/ZaX5+Nme3ZaZZ0fED4Entpq2A14AfLyPsT2E4rl3E1ZTVCvM5oPAGymeEwdwaEQ8ODMvn+GYV1e2P5aZMy4GnZk3R8QpFMvfls/jIIXuse99l/DKx+/D8x65B4sXNvH2XpI0SnZdug3PWLorzzhoVwA2bU6u/ONdXHTtKi69/nbuWLeRjZuTTZs3s3FTsmlzsnFzsjmztL2ZTZuTNes3cdMd67hj3caa/1QaBY3KYiwFv4el4GPiQffbluOPOpBD99u57lAkSdKQzJ8XHH7grhzeWgrqtJ9cxZmX3MifVk85aX7cHUpR9Tzh9xSTb9rxKe4dpAB4Ln0cpGDLiVGnZ+adsx2UmXdGxBnAsZXYphykaC1le0Sl+dQ2YzyVyYMUz4yIhdNVYGj0RcB9tlnADosXsHTxQrZfVPz/9osXsnTRArZfvIAdFi9k6eIF3H+Hxex33yVERN1hS5LG1Px5wYPutx0Put928Kj7d3WO1Xdv5MY71nHj7eu44fZ13Hj7Wm5o/f/E9m0+G2POi1kmMQ1Vq3T6raWmHwDLZ5ppFRFPZXIp+J3APv0uBY+InzF5ptV7MvOdsxzzHiaXgp+Xme2Ugn+r1HQr8OCZZlq1lqEql4IDHF5nKXhEHAhcMrF9ySWXcOCBBw7kWhs2bWb13c0blV0wfx5Ltm7UOKAkSapJZnLH2o0kg8+9t1kwn20WzJ99xy5deumlHHTQQeWmgzLz0qn2jYj3U1QeT/j3zPybdq4TEbsD5ee6rQd2bE0K6llE/AR4bKnpxZn5+TaPfQnw2VLTtHl+RBwBnFlq+k1m/lkHcf6WojJ7Qm15/jBz/Ls3bmLtHFteIiLYduutmD/PQQdJ0tyybsMm1m2YW7/3O7Vo4Xy23qoZOf4gNOYbVEvB72EpeIcWzJ/H9osX1h2GJEnStCKCpYsXzL7j+Hl4Zfu8dg/MzJURcRX3Vk0vBA4AftFrUFFMPX9ot7EBP65sP6z1bLip7l26fg9K1yoPUjycOZDnb73VYG/EJUlScwx6ko2ab17dAZT0Wgpe9tx+BFTSdSk4cEaledrY+lAKXvbMiPCbe0mSJNXpwZXtyzo8vrp/9Xzd2pvimXYTVmfmNe0enJlXA2tKTUuA6dZAaOp7IEmSJDVCkwYpnl3ZPmu2KoryvpXt5a0HWPdLNbbvdnBsNbYjZ9h3OfdWXUBRCn51OxfJzKuA/yk1bYfPpZAkSVJNImIRsFel+doOT1Pdf//uI5rxPJ3GNdUx08XW67UG9R5IkiRJjdCkQYqeSsGBq0pNE6XgPRtUKfg0+/ajFHym80mSJEnDsjNQzns3ADd3eI7rK9u79BTR9Oe5rotztBtbr9ca1HsgSZIkNUJjnklBf8qgl1XO1/N6tfShFDwi1pTOMVEKPtU5LAWXJEnSuNi2sr2mg0rpCdWHZFfP2a3qebp5GHe7sfV6rYG8BxGxC5OX223Hfv24tiRJklTWiEGKOVIKXj7P/kw9SGEpuCRJksZF9cv0dV2cY+0s5+zWMGPr9VqDeg9eC7yrT+eSJEmSutaIQQrmRil4ecBgpErBnWUlSZKkLmxT2V7fxTnurmwv6jKWqmHG1uu1BvUeSJIkSY3QlEEKS8H7c61BvQfOspIkSVKnqhUDC7s4x9aznLNbw4xtHZOXj+30WoN6DyRJkqRGaOoghaXg3V1rUO+BJEmS1Km7KtvVioJ2VKsGqufs1jBju4vJgxSdXmtQ78EpwBkdHrMf8NU+XV+SJEkCmjNIYSl4f65lKbgkSZKaovpl+uKIiA4rppfMcs5uVc9TvU472o3tLiYvw9rptQbyHmTmzXS4xG5EzL6TJEmS1KGmDFJYCn5vexNLwZ1lJUmSpE7dAiT3PntuAcWX9Td1cI49KtudPrduOtXz7NnFOdqN7WZg3x6uNaj3QJIkSWqEpgxSWAp+b3vjSsGdZSVJkqROZebaiLgG2LvUvBedDVLsVdm+oufACr+pbN+/i3NUj5kutt8Ajy1tV/9MsxnUeyBJkiQ1wry6A2iZshS8w3OMSyl4L9ca1HsgSZIkdaP6hfoBHR7/4FnO162rmfw8tyURsfd0O1e19i1PLloNXDvN7k19DyRJkqRGaMogxUQp+ISJUvBOjEspeC/XshRckiRJTXJRZfvQdg+MiN2AZaWmDcBlfYiJ1nMxLq40tx0b8PjK9sUzPGuj6/dgmmtVzydJkiSNtEYMUmTmWuCaSnNTyqCHXQpe1pT3QJIkSerGNyrbT+ugYvrwyvY5mdnPSuFqbE/v4Njqvl+fYd9zKSotJjyo3aqNiFgGPLDUdGfrfJIkSdLYaMQgRUtTy6AtBZckSZK6cx5F1fSEfYHlbR57XGX7q/0IqORrle1jImLb2Q6KiO2AYyrN08aWmeuA71aaX9VWhFvud2Zmrm/zWEmSJGkkNGmQwlJwS8ElSZI0RjJzM7Ci0vyu2aopIuKpwBNLTXcCp/c5touBX5SatgXe2sahb2Xys+B+mpmz3Xt8qrL9dxFx35kOiIhdgNfOch5JkiRp5DVpkMJScEvBJUmSNH5OAMq5+ZOBf5xu54jYA/hkpfmjmXnLVPuXjsvKz/I2YntnZfttEfGkGa4xVezvmO0imflN4Kelpp2AT0XEgmmus5BiQGKnUvMPM/M7s11LkiRJGjVNGqSwFNxScEmSJI2Z1uDC+yrN74+IUyJi94mGiJgXEc+luC9YVtp3JXDigGI7k8n59wLgOxHx+oi4Z8nWiFgSEW8AzmztM+FbmXl2m5d7C7C5tP0c4LsR8cjyThFxcCumI0vNm2ivykOSJEkaOY0ZpLAU/B6WgkuSJGncnMCW1cl/C1wTEVdGxIXArcCXgb1K+6wFXpCZqwYY28uBP5S2twFOAm6JiEsi4lKKyVQfab024Urg2HYvkpk/At5eaV4OXBAR10fE+RGxEjifotqk7K2Z+VMkSZKkMdSYQYoWS8EtBZckSdKYaU1IOgb4fOWl+RQV1I8Atq+8divwrMz88YBjuwk4DPhV5aVFwIHAAUwenIDi2W+HZeYfO7zWB4E3U1RGlO0OHAzsVmnfBLwxM/+1k+tIkiRJo6RRgxSWgt/DUnBJkiSNlcxcl5kvBp5P8SX/dFYDpwAHZOa5Q4rtauAxFJOMVs6w60qKXPuQzLy2y2udCDwK+CaTc/6yzRSVJwdn5kndXEeSJEkaFVvVHcAUTgAOZfIX738LvCYirgZuB/Zhy5lWwyoF/0nr+nBvKfj7I+L3QFDMBKvOtOq4FDwi3k7xXkxYTlEKvhK4gWK2VXWmFVgKLkmSpAbLzC8CX4yIBwCHAHsAC4FVwOXAj1vPauv0vDMuE9vG8euBD0bEhymqGh4G7NJ6+WaKgZULW1UhPcnMi4AjI2Jn4AkU9xBLKAZorqR4D2asDpckSZLGReMGKTJzc0QcA3waeFHppYlS8KncCjx/GKXgEXEYxcOvH1Z6aaIUfCoXAUd1UwoeEZsoBirml17avfVTtQl4szOtJEmSNAoy83fA7+qOo6o1CPELJj+TblDXugX4yqCvI0mSJDVZo5Z7mmAp+D3XshRckiRJkiRJkjS2GldJUWYp+EiXgi8sb/zud42bJCdJkqQuTJHXLZxqP40lc3xJkqQxVHeOH5k5zOtpjoiIoyiWxZIkSdJ4Ozozv1Z3EBo8c3xJkqQ5Y6g5fiOXe9JYWFp3AJIkSZL6yhxfkiRJfecghQblPnUHIEmSJKmvzPElSZLUd41+JoVG2vmV7ecDV9QRiEbSfkxeSuBoimewSO3w86Ne+PlRL+bK52chcP/S9vfrCkRDZ46vXsyVPlKD4edHvfDzo17Mlc9PrTm+gxQalLsq21dk5qW1RKKRE7HFs+2v9POjdvn5US/8/KgXc+zz88u6A1AtzPHVtTnWR6rP/PyoF35+1Is59vmpLcd3uSdJkiRJkiRJklQLBykkSZIkSZIkSVItHKSQJEmSJEmSJEkXMxFfAAAXz0lEQVS1cJBCkiRJkiRJkiTVwkEKSZIkSZIkSZJUCwcpJEmSJEmSJElSLRykkCRJkiRJkiRJtXCQQpIkSZIkSZIk1cJBCkmSJEmSJEmSVAsHKSRJkiRJkiRJUi0cpJAkSZIkSZIkSbXYqu4ANLb+CLy7si21y8+PeuHnR73w86Ne+PnRuPMzrl74+VEv/PyoF35+1As/P0MQmVl3DJIkSZIkSZIkaQ5yuSdJkiRJkiRJklQLBykkSZIkSZIkSVItHKSQJEmSJEmSJEm1cJBCkiRJkiRJkiTVwkEKSZIkSZIkSZJUCwcpJEmSJEmSJElSLRykkCRJkiRJkiRJtXCQQpIkSZIkSZIk1cJBCkmSJEmSJEmSVAsHKSRJkiRJkiRJUi0cpJAkSZIkSZIkSbVwkEKSJEmSJEmSJNXCQQpJkiRJkiRJklSLreoOQKMrIuYBDwAeAuwG3AdYC/wJuBz4ZWZuqC9CDUtE7Ac8BtgTWAjcBlwBnJeZ62qMK4BHAg8Hdmk13wT8CrgwM7Ou2Oay1t/LMoq+Y09ge+Buis/N/wC/qPNzI/WD/Y+kUWWOrwnm+OqEOb7GnX2PNFjhvyF1IiJ2BZ4HHA4sp7hpmc5a4L+Bj2TmLwcfHURErx/ofTLzqn7EMhdExHOB/0Pxi3oqdwErgHdn5i1DjGsB8HrgDcAe0+x2HXAScLI32oMXETsAzwWeATwF2HmG3TcA3wROyszvDyE8IuIqYO8eTnFYZp7bn2g0lYg4HnhXD6c4LTOP7U8007P/kTSKzPFVZo6vdpnjqxfm95LKXO5JbYuIrwLXA/8XOIqZb14AFgEvA86PiA9FxMIBh6ghiYitI+IzwJeZ/uYFYFvgdcBlEfGkIcV2f+BnwIeYPoGAYnbPh4GfRMRM+6lHEfEx4EbgVOAFzHzzArCA4mbn3Ig4LSJm62ukRrD/aaaIWBER2aefqwYQ31U9xrS83zFpbjHH1wRzfHXCHF9zgX1Pc5njjx8HKdSJxzP1Z2YDcDVwPnAJsKby+jzgzcAZEeESYyOutQTAF4C/rLy0CfgDcBFwe+W1+wLfjojHDTi2XYBzgEdUXloLXEqxREG1xPhg4JyImC2pVvcOoVgioGoTxYyTC4CL2fJzA/By4KyI2HZw4Um9s/+ZM9bXHYA0AOb4MsdXN8zxNdbse+YUc/wGMJlUt24C/hM4k8qapK1SuGcA/0KxHuWEo4APUNzMDMPFwD90eMyNgwhkzLwFOLrS9gngPZm5Eu65yTmaotxxr9Y+i4HTI+KgzJwqUe2HFcB+pe11wNuA/8jMNa3YlgCvAd4HbNPa74EUM4COGlBcutcq4HMUpd4/zMw7J16IiPnAE4F/bv13wmMo/m6fP6QYbwJe2uExvxpEIJrRm+nsfV85qEBaVmD/Mxd8o+4ApAEzx5+7zPHVC3N89YP5vepijt8APpNCbYuIW4AbgHcDX8nMjbPsvw1wBnBkqXkDcFBm/nZAMZY/0N/PzOWDuM5cFRE7Ucyk2q7U/PbM/MA0++8B/IjiAWoT/jkze1l3crrYDge+U2raADwtM38wzf5PBs6iKDue8JTMPKffsc11EXE+sBPwXuBzmbl2lv3nA6dQJHtlA/v7icnr1V6dmcsGcR11L7Zcs7YxawTb/zRbRBwA7N7FoQ+k6IvKHpaZF/ce1b0q/U83X6BckJm39TMmzS3m+DLHVzfM8dUr83v1whx//FhJoU4cB3w9Mze3s3NmrouIFwFXUKzPB0WH/QrgnwYTogbsrUy+efkBcMJ0O2fm9RHxauB7peY3RsTJmXlrn2N7T2X7A9MlEK3Yvh8RJwDvKDW/l2LJA/XXu4CzMrOtEsrM3BQRr6VYC/lRpZdeTVFuKzWN/U+DZeZlwGWdHhcRT6k0/bLfNy9TWJeZ35t9N6mvzPFljq9umONrnNn3NJw5/vjxmRRqW2Z+td2bl9Ixq4GTK81H9C8qDUurvPuVlebjc5ZyrMw8G/hhqWk7iger9TO2h1CUC09YTfFgq9l8sLXvhEMj4sH9jE2Qmd9s9+aldMwmir+fMvsONY79z3hq/c57WaV5RQ2hSANnjj+3meOrW+b4Glf2PePLHL/ZHKTQMPywsr3XlHup6Q6leDjehN8D57Z57Kcq28/tR0Al1fVzTy+vgzqd1j5nVJr7HZu6V+07doqIxbVEIk3P/mc8PY17Z4hDUeL/uZpikZrKHH88mONr2Mzx1XT2PePLHL/BHKTQMFTXUFtaSxTq1bMr22fNNsOqvG9le3nrAVP9Uo3tux0cW43tyCn3Uh2mWn/R/kNNY/8znl5R2f5GZt5SSyRSc5njjwdzfA2bOb6azr5nfJnjN5iDFBqGPSrb/V6nVMPx8Mr2ee0emJkrgatKTQuBA/oQExERwEMrzW3HBvy4sv2w1jlVv2rfAfYfahD7n/EUEfcB/rzSvKKGUKSmM8cfD+b4GjZzfDWWfc/4MsdvPh+crWF4YmX7t8O8eETsBuwOLKGYtXFLZt4wzBjGRHUtxU4fUHQZsKxyvl/0ElDL3kC5PHh1Zl7T7sGZeXVErCmdYwlwf6Dtc2hgqn3H1Z2ue9uLiNiZohT0PsAdFDdP13Uwu1ADFhFbA/sCO1GU6t4KrMzMNUMKwf5nPL0AWFTavhn4Vk2xSE1mjj8ezPE1bOb4mpb5vQbIHL/hHKTQQEXEfODlleZhdQIPiYjfA/tUX4iIG4HvAysy88whxTOyImIRW64zfG2Hp6nuv3/3Ec14nk7jmjimfJ79MYlogldVtofVd+wSEZex5U07wJ8i4ocU61Z+sfXwP9XjYxQ3MNtU2jdGxAXAt4FTMvOPA4zB/mc8HVvZ/mxmbhxmAH6BoqYzxx8P5viqiTm+pmN+r0E6trJtjt8wLvekQftril8yE4b5UJodmeLmpWVX4IXAtyPiwoh4yJBiGlU7A+USxQ0Uo86duL6yvUtPEU1/nuu6OMegYlOXIuJZwJMqzSuGdPlFTH3zAkW/cjTwBeA3EfHkIcWkLR3AljcwUEzAOAQ4Hrg6Iv659WXaINj/jJmIeADw+Erzp4cYwsQXKH8EfknxZesvKW5sb4mIr0TECwb4mZbaZY4/HszxNVTm+JqF+b0Gwhx/NDhIoYGJiP2AD1SaP5aZ3XTyg/QI4GcRcUzdgTTYtpXtNV2M9K6e5Zzdqp6nep12DCo2dSEidgT+vdL8lcz8eR3xzGA/4OyIeH3dgWhai4D/A3wvIgbx79r+Z/xUH6Z3YWb+eojX9wsUNZ45/lgxx9fQmOOrT8zv1Q1z/BHgIIUGIiIWA/8NbFdqvhp45xAufwvFbIyXUjzwaEdgAbAD8DDgdcCvKscsAj4TEdVZHSpUf6mu6+Ica2c5Z7eaHJs6FBHzgM9QlEBOuB34+yFc/g7gdOA44FEU66AuAJZSJBTHAT+qHDMf+EhEvGgI8QmS4sF1/wQ8neJzsphixtUewHMobn6r/cBy4PMDmJli/zNGWg81rC5fs6KGUNrhFyiqhTn+2Gny77Emx6YOmeNrBub3Gihz/NHhMynUd60O4DTg4aXmjcBfZuadA778S4Ezpnnw1qrWz8XAxyLir4GPAlu3Xl8IfC4iHpCZ3fwiGmfVkstuHmx2d2V70ZR7da7JsalzHwKeWWn768zsZi3QTrwF+HZm3jXFa3e0fq4ATo2IPwdOBbZvvR7ApyLi3My8ccBxzmXfBT6XmdM9mHVl6+cbEfFe4PNMLul9NvBa4N/6GJP9z3h5CpPXZl/P8JavuQM4E/gOxZesf2i1LaZ4MPChwCuBJ5SOmfgC5abM/PyQ4tQcZo4/lpr8e6zJsalz5viaivm9hsEcf0RYSTEiIuKkiMgh/Bzfh3BPBJ5faXt9Zv64D+eeUWZ+dpqbl6n2/XfgJcDmUvMewN8NIrYRV72hW9jFObaubPfrJrHJsakDEfH3wJsqzR/MzC8M+tqZecY0Ny9T7ftlipus8gyZxRSzfzQgmXneDDcw1X2vA54G/KTy0jtas4D7xf5nvFTLwL+RmbcO4bpvAfbIzBdm5qmZeUFm/ikzN2bmHZl5Rav9icBfUHwZO2HiC5RdhxCnBsAcvz3m+APT5N9jTY5NHTDH13TM7zUk5vgjwkEK9VVEvA14Y6X53Zl5Sh3xzCYzvwT8V6X5ZXXE0nDVxG6qh1nNpjp7oK1ksQ1Njk1tioiXACdVmlcAbxt+NLPLzJ8CH6w0v6RVyq4GaM2WfTnFLN8JuwCH9/Ey9j9jorWm8V9UmlcM49p+gaJRYI4/tpr8e6zJsalN5vjqJ/N7dcocf7TY0apvWqXV7680n5yZx9cQTidOrGw/NCLuV0skzVXtWBe3Sv47sWSWc3arep7qddoxqNjUhog4kmL5iPJn6kvAq7t4eOMwfRTYVNrekWKdWzVEZv4O+FqleZA3MfY/o+sYJv9d3AR8u6ZYZuQXKBo2c/yxZo6vgTHH1yCY36tD5vgjxGdSjI5vUjwsbtB+0M1BrRkS1ZlUpwFv6DmiAcvMX0fEzRQj8FAkUQ+i6LxUuIXigVYTCeYCiverk/doj8r2zX2Ia6rz7DnlXjMbVGyaRUQcBpzB5N9HZwEvzsxNUx/VDJl5W0RcCDy61Lw/8POaQtLUzmby7Jn9+3hu+5/xcWxl+7OZuXGqHRvio8A7KNashXu/QLH/GT3m+ANijt8Wc3wNhDm+Bsz8Xu06trJtjt9gDlKMiMw8i+KXeuNExNEUNyvl0b0vAsc1fIZE2XXcewMDcN+6AmmizFwbEdcAe5ea96KzG5i9KttX9BxY4TeV7ft3cY7qMf2KTTOIiEMoZsGUS2jPA/683XWnG+BaJt/A2Hc0T/WBjP38O7L/GQMRsS/wxErzihpCaZtfoIwPc/yBM8efgTm+BsEcX0Ngfq9ZmeOPnjlTMqLBiIinAV9g8oDXd4CXNH2GRMWGyvaCWqJotuov1gM6PP7Bs5yvW1czed2+JRGx93Q7V7X2LT9oazVbJj3qs4h4KEWZ5bal5l8Cz8rM1fVE1RX7juYb5N+R/c94eDmTl6K4MDN/XVcwHRjkDbrmOHP8OcUcX31jjq8hMb9XO8zxR4yDFOpaRDwe+Cqwdan5h4zWDIkJu1a2/1hLFM12UWX70HYPjIjdgGWlpg3AZX2IidZMvosrzW3HBjy+sn3xCM0OHEkRsT/FrNEdSs2XA0dk5u31RNU1+47mG9jfkf3P6Gutvf7ySvOn64ilC36BooEwx59zzPHVF+b4GiLze83IHH80OUihrkTEIynW0C2PEJ8PHJmZa6c+qpkiYk8mlziDI91T+UZl+2kdPFiv+iCrczKznw+Pqsb29A6Ore779R5j0QxaM0u+x+SlF/4APD0zRyr5j4itmVyGCfYdTfSEyna//47sf0bbk4F9Stvrgc/VFEun/AJFfWeOPyeZ46tn5vgaMvN7zcYcfwQ5SKGORcQBFOXeS0vNlwDPyMw76omqJ8dVtq/NzP+pJZJmO4/JD3bcF1je5rHV9/ir/Qio5GuV7WMiYtsp9yyJiO2AYyrN/Y5NLa3Zdmcz+eFj1wNPzczr64mqJy9i8pc4dwM/rikWTSEitgeeV2k+u8+Xsf8Zba+obH89M/9USyQd8AsUDYI5/pxljq+emONrmMzv1SZz/BHkIIU6EhH7UJRw7lxq/h3FDIlb64mqexHxYOAfKs1fqSOWpsvMzWz5kKF3zTbTKiKeyuSHFd0JnN7n2C4GflFq2hZ4axuHvhVYUtr+aWb2pURdk0XEjhR9x36l5j9S9B1/qCeq7kXErsC/VJq/m5lr6ohH0/owsH1pez3FOsl9Y/8zuiJiCfD8SvOKGkLphl+gqK/M8ecuc3z1whxfNTC/14zM8UeXgxRqW0TsTlHCuXup+RqKGRI39vlayyMiyz+z7P/wiHhjRCyeab/qMcCZwHal5rXAB7qLek44ASiXcD8Z+Mfpdo6IPYBPVpo/mpm3TLV/6bis/CxvI7Z3VrbfFhFPmuEaU8X+jjauow61ZpScCRxYal4FHJ6Zl/f5Wsum+Pwsm2H/3SLi3RGxw3T7THUNij/PHqXmBI7vKmjNKiLeFhEHd7D/VhFxIlvO8PxEZt4wy7H2P3PH85j8YM8bKf5tN5pfoKjfzPGFOb66YI6vXpjfa4DM8UdU+PwWtaN1Y/BzJicgm4D/BXRTNv2jzFw3w/WWA+eU2zJz2tk8pf1vBb4EfBn4RTVRbs0IOgj4K+A1TH4gIMAbMvOjbf8p5qCIeDvwvkrzx4H3ZubK1j7zgKOAjwJ7lfZbCRyYmatmuUa1YzosM89tI7bvMHlt3HXA24D/mOjYW6PqfwW8H9imtO+3MvPZs11DnYuIc9hy2YB3Aj/p4nQXZOZtM1xrGcX6t2X7ZOZVs+x/F0VZ7xcpZrysnGLfBwDHAq9j8lIYACdl5hvbiF9diIhzKb4wOY9ilubZwBWZubGy31LgWRSzmB5eOc2VwCGzzQi2/5k7IuL/AYeVmk7MzDf3cL5ldNb/7Ab8DUX/MW2/NsU1vgI8rNScwKMy88KOApYwx9e9zPHVKXN89cL8XoNijj+6HKRQW6b5R9mLaf9Bt663nO5uYKpuolhj9U6KkdQ9gOlmU/TUcc0VrZuTrwJHVl7aBFwN3E7xgKLtK6+vpSj7nbVUrYck4n4USfE+lZfWAr8HgmKd3W0qr18JPG7UHuo2KmabJdmhGT8LPdzAVN0K3AzcASwCdgPuO81lzwBe1FouQQNQuokpuxu4jqLP2QTsBCxj6irRG4EntbMWuf3P3BDFAz7/QPH3MuEhmXlJD+dchl+gaMSY42uCOb46ZY6vXpjfaxDM8UfbVnUHIA3Y/Vo/M7kDeG1mfnYI8Yy8zNwcEccAn6ZYL2/CfIpf0FO5FXh+OzcvPcZ2U0QcRnGDVR6BXsTkGYJlFwFHmUCoYqfWz0zuBv438JF0xL8OWzN5/ePpfAt4ZWbePMhg7H9GzsuZfPNyQS83Lz3aFnhJ64eI6PQLlOq6+9JcYI7fZ+b4miPM8ZvN/F69MscfYT6TQuPi1xTr/50J/KnNY66gKBlc5s1LZzJzXWa+mOJhRBfNsOtq4BTggHZmKvQptquBx1B8HrYYqS5ZSfH3f0hmXjuM2NRINwGvpyitvKnNY64G3gvsm5n/6s3LUPwL8AngUopZVbO5iyKxe3JmPnvQNzAT7H9Gyisq2yvqCGIaOwEPBg4BHsrUNy93U9y4vNAZnhpz5vhDZI6vMWKO33zm9xoEc/wR5nJPGkutEq8HUqyVugPFKOU64DbgBuBns61bqPa1StQOoSi1X0jxwLTLgR/PtC7xEOKaBxxMMethl1bzzRQ3XRfOxU5fM2utH7k/Rd+xM7AYWE/Rd9xMsQ72TMmpBqy1fvoBFKXfu1HMUJlH0e/cBlwG/Doz27nZGRj7n+aKiCcAPyw1rQd2y8x2vwCd7rzL6KwUfBHFGsaHAY9j9lnhUHyB8l/Ax+2LNBeZ4w+XOb7GhTl+s5nfqx/M8UefgxSSJElSzfwCRZIkSRov5vjtc5BCkiRJkiRJkiTVwmdSSJIkSZIkSZKkWjhIIUmSJEmSJEmSauEghSRJkiRJkiRJqoWDFJIkSZIkSZIkqRYOUkiSJEmSJEmSpFo4SCFJkiRJkiRJkmrhIIUkSZIkSZIkSaqFgxSSJEmSJEmSJKkWDlJIkiRJkiRJkqRaOEghSZIkSZIkSZJq4SCFJEmSJEmSJEmqhYMUkiRJkiRJkiSpFg5SSJIkSZIkSZKkWjhIIUmSJEmSJEmSauEghSRJkiRJkiRJqoWDFJIkSZIkSZIkqRYOUkiSJEmSJEmSpFo4SCFJkiRJkiRJkmrhIIUkSZIkSZIkSaqFgxSSJEmSJEmSJKkWDlJIkiRJkiRJkqRaOEghSZIkSZIkSZJq4SCFJEmSJEmSJEmqhYMUkiRJkiRJkiSpFg5SSJIkSZIkSZKkWjhIIUmSJEmSJEmSauEghSRJkiRJkiRJqoWDFJIkSZIkSZIkqRYOUkiSJEmSJEmSpFo4SCFJkiRJkiRJkmrhIIUkSZIkSZIkSaqFgxSSJEmSJEmSJKkWDlJIkiRJkiRJkqRaOEghSZIkSZIkSZJq4SCFJEmSJEmSJEmqhYMUkiRJkiRJkiSpFv8fQNwigdaNfOMAAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1.2340649 1.3271438]\n"
+ ]
+ }
+ ],
+ "source": [
+ "plt.figure(figsize=[6,3],dpi=300)\n",
+ "#plt.suptitle('State $[1,2]$ with $\\gamma=0.9$')\n",
+ "pos=torch.tensor([0])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('std dev:',np.sqrt(variance))\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(2):\n",
+ "\n",
+ " plt.subplot(1,2,i+1)\n",
+ " text='Action '+str(i)\n",
+ " plt.title(text)\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "plt.show()\n",
+ "#print('Argmax:',pol[tuple(pos)],'for pos:',str(pos.numpy()))\n",
+ "print(m-np.sqrt(variance))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 1000
+ },
+ "id": "uZL_GVlhqEq-",
+ "outputId": "85aa02a6-7a5a-42af-84ad-128f89aeff6e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [2.8445053 2.6744578]\n",
+ "std dev: [1.6104404 1.347314 ]\n",
+ "Entropy: [3.515364 3.3332715]\n",
+ "Mean Q: [2.0731869 1.8455882]\n",
+ "std dev: [1.4629111 1.1183099]\n",
+ "Entropy: [3.4160085 3.074432 ]\n",
+ "Mean Q: [1.1683384 0.94158494]\n",
+ "std dev: [1.2219588 0.83429414]\n",
+ "Entropy: [3.1453402 1.2751778]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<Figure size 576x864 with 0 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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+ "text/plain": [
+ "<Figure size 2400x3600 with 6 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light",
+ "tags": []
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig=plt.figure(figsize=(8, 12))\n",
+ "\n",
+ "_, ax = plt.subplots(3, 2,\n",
+ " sharex=False, \n",
+ " sharey=False,figsize=(8,12),dpi=300)\n",
+ "for i in range(3):\n",
+ " pos=torch.tensor([i])\n",
+ " p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ " m_u=(p*u(agent.support.cpu().numpy())).sum(1)\n",
+ " y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ " ent=-(p*np.log(p)).sum(axis=1)\n",
+ " variance=np.sum(p*y.T,axis=1)\n",
+ " ra=m-np.sqrt(variance)\n",
+ " print('Mean Q:',m)\n",
+ " print('std dev:',np.sqrt(variance))\n",
+ " print('Entropy:', ent)\n",
+ " for j in range(2):\n",
+ "\n",
+ " \n",
+ " title='State $s_'+str(i)+'$: Action '+str(j)\n",
+ " ax[i,j].set_title(title)\n",
+ " ax[i,j].plot(agent.support.cpu().numpy(),p[j])\n",
+ " #ax.annotate(str(i)+str(j), xy=(0.75, 0.75), textcoords='axes fraction', size=12)\n",
+ " text='$Q$: '+str(np.round(m[j],2))+'\\n$Q-\\sigma$: '+str(np.round(ra[j],2))+'\\n$Q_U$: '+str(np.round(m_u[j],2))\n",
+ " ax[i,j].text(0.8,0.8,text, bbox = dict(facecolor = 'None', alpha = 1),size=12,horizontalalignment='center',verticalalignment='center',transform=ax[i,j].transAxes) \n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "eqNJxk5Pur24"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "collapsed_sections": [],
+ "name": "mdp.ipynb",
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/code/polgrad_pacman_combined.ipynb b/code/polgrad_pacman_combined.ipynb
new file mode 100644
index 0000000..f315e84
--- /dev/null
+++ b/code/polgrad_pacman_combined.ipynb
@@ -0,0 +1,852 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "polgrad_combined.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "3P1g559FrwfN"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import sys\n",
+ "from google.colab.patches import cv2_imshow\n",
+ "import torch\n",
+ "import cv2\n",
+ "from torch import nn\n",
+ "from torch import optim"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LoesPrPVr4rC"
+ },
+ "source": [
+ "class policy_estimator(nn.Module):\n",
+ " def __init__(self, env,net):\n",
+ " super(policy_estimator, self).__init__()\n",
+ " self.n_outputs=2\n",
+ " self.conv = nn.Sequential(\n",
+ " nn.Conv2d(1,128, kernel_size=3, stride=1),\n",
+ " nn.ReLU(),\n",
+ " nn.Conv2d(128,128, kernel_size=3, stride=1),\n",
+ " nn.ReLU()\n",
+ " ).to('cuda') \n",
+ " self.network = nn.Sequential(\n",
+ " nn.Linear(self.get_conv_size()+4*51, 1024), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(1024, self.n_outputs)).to('cuda') \n",
+ " self.softmax=nn.Softmax(dim=-1)\n",
+ " self.net=net\n",
+ "\n",
+ " def get_conv_size(self,flatten=True):\n",
+ " test=torch.ones([1,1,7,7]).float()\n",
+ " if next(self.parameters()).is_cuda:\n",
+ " test=test.to('cuda')\n",
+ " out=self.conv(test).shape\n",
+ "\n",
+ " if flatten:\n",
+ " out=torch.prod(torch.tensor(out)).item()\n",
+ " self.conv_size=out \n",
+ " return out\n",
+ " else:\n",
+ " return out \n",
+ " def forward(self, state):\n",
+ " dist=self.net.dist(state.float()).reshape(-1,4*51)\n",
+ " conv=self.conv(state).reshape(-1,self.conv_size)\n",
+ " action_probs = self.network(torch.cat([dist,conv],dim=1))\n",
+ " return self.softmax(action_probs)\n",
+ " def predict(self, state,dist):\n",
+ " dist=dist.reshape(-1,4*51)\n",
+ " conv=self.conv(state).reshape(-1,self.conv_size)\n",
+ " action_probs = self.network(torch.cat([dist,conv],dim=1))\n",
+ " return self.softmax(action_probs) \n",
+ "\n",
+ "class super_env():\n",
+ " def __init__(self,env,agent,balance=None):\n",
+ " self.env=env\n",
+ " self.agent=agent\n",
+ " self.state=self.env.reset()\n",
+ " self.balance=balance\n",
+ " def reset(self):\n",
+ " state=self.env.reset()\n",
+ " self.state=state\n",
+ " return state\n",
+ " def step(self,a):\n",
+ " if a==0: #risk neutral\n",
+ " action=self.agent.greedy_action(self.state)\n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ "\n",
+ " else: #risk averse\n",
+ " action=self.agent.dqn.risk_action(torch.tensor(self.state).float().to('cuda').unsqueeze(0),alpha=4).argmax().cpu().item() \n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ " if self.balance is not None:\n",
+ " r=self.balance(r)\n",
+ " self.state=next_state\n",
+ " return next_state,r,complete,_\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ec6Ik7R7wdKG",
+ "outputId": "52c9c235-a93a-4aab-f291-09568a24f381"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bz3WNO-Ky9CG"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7Jaj_eyZy_Aq",
+ "outputId": "de24cd52-d84c-4ff9-a4c4-ea8128419809"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "FblTLZBve8a0"
+ },
+ "source": [
+ "import numpy as np\n",
+ "class pacman_windy():\n",
+ " def __init__(self):\n",
+ " print('windy pacman env 0.08')\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.grid=np.ones([7,7])\n",
+ " self.pos=np.array([6,3])\n",
+ " self.grid[6,3]=0\n",
+ " self.enemy1_pos=np.array([0,0])\n",
+ " self.enemy2_pos=np.array([0,6])\n",
+ " self.obs_list=np.array([[1,1],[3,1],[5,1],[5,3],[1,5],[3,5],[5,5],[1,3]])\n",
+ " self.cliff=np.array([3,3])\n",
+ " for pos in self.obs_list:\n",
+ " self.grid[pos[0],pos[1]]=-1\n",
+ " self.grid[pos[0],pos[1]]=0\n",
+ " self.last_action=0\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=4 and pos[0]>=2 and pos[1]<=4 and pos[1]>=2:\n",
+ " return True\n",
+ " return False\n",
+ " def wind_direction(self,pos):\n",
+ " if pos[0]==2:\n",
+ " return 2\n",
+ " if pos[0]==4:\n",
+ " return 0\n",
+ " if pos[1]<3:\n",
+ " return 1\n",
+ " else:\n",
+ " return 3\n",
+ " def state(self):\n",
+ " out=self.grid.copy()\n",
+ " \n",
+ " out[self.cliff[0],self.cliff[1]]=-3 \n",
+ " out[self.pos[0],self.pos[1]]=2\n",
+ " out[self.enemy1_pos[0],self.enemy1_pos[1]]=-2\n",
+ " out[self.enemy2_pos[0],self.enemy2_pos[1]]=-2 \n",
+ " return out.reshape(1,7,7)\n",
+ " def obs(self,pos):\n",
+ " return np.any(np.all(pos == self.obs_list,axis=1))\n",
+ " def reset(self):\n",
+ " self.grid=np.ones([7,7])\n",
+ " for pos in self.obs_list:\n",
+ " self.grid[pos[0],pos[1]]=-1 \n",
+ " self.pos=np.array([6,3])\n",
+ " self.grid[6,3]=0\n",
+ " self.grid[2,3]=0\n",
+ " self.grid[3,2]=0\n",
+ " self.grid[3,3]=0 \n",
+ " self.grid[3,4]=0\n",
+ " self.grid[4,3]=0\n",
+ " self.enemy1_pos=np.array([0,0])\n",
+ " self.enemy2_pos=np.array([0,6])\n",
+ " return self.state()\n",
+ " def move(self,pos,a,agent=False):\n",
+ " #print(a)\n",
+ " if agent:\n",
+ " if self.is_windy(self.pos): #if windy\n",
+ " #print('windy')\n",
+ " if np.random.random()<=0.1: #if p<0.1\n",
+ " next_pos=pos+self.d[self.wind_direction(self.pos)]\n",
+ " else:\n",
+ " next_pos=pos+self.d[a].copy() \n",
+ " else:\n",
+ " next_pos=pos+self.d[a].copy() \n",
+ " else:\n",
+ " next_pos=pos+self.d[a].copy()\n",
+ " if self.obs(next_pos):\n",
+ " return pos\n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>6:\n",
+ " next_pos[0]=6\n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>6:\n",
+ " next_pos[1]=6 \n",
+ " return next_pos\n",
+ " def shortest_path(self,present,goal):\n",
+ " path=np.full(self.grid.shape,np.inf)\n",
+ " #for pos in self.obs_list:\n",
+ " #path[pos[0],pos[1]]=100\n",
+ " path[goal[0],goal[1]]=0\n",
+ " queue=[]\n",
+ " queue.append(goal) \n",
+ " while path[present[0],present[1]]==np.inf:\n",
+ " current=queue.pop(0)\n",
+ " for i in self.d:\n",
+ "\n",
+ " out=self.move(current,i)\n",
+ " #print(out,path[out[0],out[1]],path[current[0],current[1]]+1)\n",
+ " if path[out[0],out[1]]>(path[current[0],current[1]]+1):\n",
+ " path[out[0],out[1]]=path[current[0],current[1]]+1\n",
+ " queue.append(out)\n",
+ " #plt.imshow(path)\n",
+ " return path\n",
+ " def shortest_path_direction(self,present,goal):\n",
+ " path=self.shortest_path(present,goal)\n",
+ " out=[]\n",
+ " for i in range(4):\n",
+ " next_pos=self.move(present,i)\n",
+ " out.append(path[next_pos[0],next_pos[1]])\n",
+ " return np.argmin(out)\n",
+ " def step(self,a):\n",
+ " isdone=False\n",
+ " next_pos=self.move(self.pos,a,agent=True)\n",
+ " if int(next_pos[0])==self.cliff[0] and int(next_pos[1])==self.cliff[1]:\n",
+ " reward=-1.0\n",
+ " isdone=True\n",
+ " self.pos=next_pos\n",
+ " return self.state(),reward, isdone, None\n",
+ " p= self.move(self.pos,self.last_action)\n",
+ " if max(np.abs(self.enemy1_pos[0]-self.pos[0]),np.abs(self.enemy1_pos[1]-self.pos[1]))>2:\n",
+ " enemy1_next_pos=self.move(self.enemy1_pos,self.shortest_path_direction(self.enemy1_pos,self.move(p,self.last_action))) \n",
+ " else: \n",
+ " if np.random.random()>0.85: \n",
+ " enemy1_next_pos=self.move(self.enemy1_pos,self.shortest_path_direction(self.enemy1_pos,p)) \n",
+ " elif np.random.random()>0.85: \n",
+ " enemy1_next_pos=self.move(self.enemy1_pos,self.shortest_path_direction(self.enemy1_pos,self.pos)) \n",
+ " else:\n",
+ " enemy1_next_pos=self.move(self.enemy1_pos,np.random.randint(0,4))\n",
+ " if max(np.abs(self.enemy2_pos[0]-self.pos[0]),np.abs(self.enemy2_pos[1]-self.pos[1]))>2 : #if far away take shortest path\n",
+ " enemy2_next_pos=self.move(self.enemy2_pos,self.shortest_path_direction(self.enemy2_pos,p)) \n",
+ " else:\n",
+ " if np.random.random()>0.85:\n",
+ " enemy2_next_pos=self.move(self.enemy2_pos,self.shortest_path_direction(self.enemy2_pos,p)) \n",
+ " elif np.random.random()>0.85:\n",
+ " enemy2_next_pos=self.move(self.enemy2_pos,self.shortest_path_direction(self.enemy2_pos,self.pos))\n",
+ " else: \n",
+ " enemy2_next_pos=self.move(self.enemy2_pos,np.random.randint(0,4))\n",
+ " if self.grid[next_pos[0],next_pos[1]]==1:\n",
+ " self.grid[next_pos[0],next_pos[1]]=0\n",
+ " reward=0.08\n",
+ " else:\n",
+ " reward=0\n",
+ " if np.all(next_pos==enemy1_next_pos) or (np.all(self.pos==enemy1_next_pos) and np.all(next_pos==self.enemy1_pos)): #run into same position or swapped position\n",
+ " #reset position\n",
+ " reward=-1\n",
+ " isdone=True\n",
+ " elif np.all(next_pos==enemy2_next_pos) or (np.all(self.pos==enemy2_next_pos) and np.all(next_pos==self.enemy2_pos)):\n",
+ " reward=-1\n",
+ " isdone=True\n",
+ " elif np.all(self.grid!=1):\n",
+ " reward=1.0\n",
+ " self.pos=next_pos\n",
+ " isdone=True\n",
+ " else:\n",
+ " self.pos=next_pos\n",
+ " self.enemy1_pos=enemy1_next_pos\n",
+ " self.enemy2_pos=enemy2_next_pos\n",
+ " self.last_action=a\n",
+ " return self.state(),reward, isdone, None\n",
+ " def render(self,*args,**kwargs):\n",
+ " plt.imshow(self.state())\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " np.random.seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "MTa4aNS3bMOM"
+ },
+ "source": [
+ "import pacman_base"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YWwjQE9VzoIr",
+ "outputId": "50375d2e-4a91-4bd8-88da-e44b42ef6863"
+ },
+ "source": [
+ "base_env=pacman_windy()\n",
+ "num_frames = 80000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "optimal_agent = pacman_base.DQNAgent(base_env, memory_size, batch_size, target_update, epsilon_decay)\n",
+ "print(optimal_agent.dqn.load_state_dict(torch.load('pacman_windy.pt')))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "windy pacman env 0.08\n",
+ "cuda\n",
+ "<All keys matched successfully>\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "542XMmdMsBMX"
+ },
+ "source": [
+ "def discount_rewards(rewards, gamma=0.99):\n",
+ " r = np.array([gamma**i * rewards[i] \n",
+ " for i in range(len(rewards))])\n",
+ " # Reverse the array direction for cumsum and then\n",
+ " # revert back to the original order\n",
+ " r = r[::-1].cumsum()[::-1]\n",
+ " return r "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "k8qL6QInsA1b",
+ "outputId": "b8fc9e46-ed4d-4345-9b60-8729afc226b5"
+ },
+ "source": [
+ "env=super_env(base_env,optimal_agent,balance=None)\n",
+ "s = env.reset()\n",
+ "pe = policy_estimator(env,optimal_agent.dqn).to('cuda')\n",
+ "print(pe(torch.tensor(s).float().unsqueeze(0).to('cuda')))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.5015, 0.4985]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BDSoJhZPLQHj",
+ "outputId": "ad17b342-9e9e-4542-ef0a-a55583b28a31"
+ },
+ "source": [
+ "pe.load_state_dict(torch.load('sc_combined_c.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 39
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "po646HRKMfkw"
+ },
+ "source": [
+ "def test(lim=None):\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " \n",
+ " state = env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " frames.append(state)\n",
+ " rews=[]\n",
+ " c=0\n",
+ " actions=[]\n",
+ " while not done:\n",
+ " action = pe(torch.tensor(state).float().to('cuda').unsqueeze(0)).argmax().item()\n",
+ " actions.append(action)\n",
+ " next_state, reward, done,_ = env.step(action)\n",
+ " frames.append(next_state)\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " rews.append(reward)\n",
+ " c+=1\n",
+ " if lim is not None and c>lim:\n",
+ " break\n",
+ "\n",
+ " print(\"score: \", score)\n",
+ " \n",
+ " return frames, rews,actions"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "itZYQ2spL8O5",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "df8c70bb-8302-40ff-ee87-44cfb881099d"
+ },
+ "source": [
+ "pe"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "policy_estimator(\n",
+ " (conv): Sequential(\n",
+ " (0): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+ " (1): ReLU()\n",
+ " (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+ " (3): ReLU()\n",
+ " )\n",
+ " (network): Sequential(\n",
+ " (0): Linear(in_features=1356, out_features=1024, bias=True)\n",
+ " (1): ReLU()\n",
+ " (2): Linear(in_features=1024, out_features=2, bias=True)\n",
+ " )\n",
+ " (softmax): Softmax(dim=-1)\n",
+ " (net): Network(\n",
+ " (conv): Sequential(\n",
+ " (0): Conv2d(1, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+ " (1): ReLU()\n",
+ " (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))\n",
+ " (3): ReLU()\n",
+ " )\n",
+ " (layers): Sequential(\n",
+ " (0): Linear(in_features=1152, out_features=1024, bias=True)\n",
+ " (1): ReLU()\n",
+ " (2): Linear(in_features=1024, out_features=1024, bias=True)\n",
+ " (3): ReLU()\n",
+ " (4): Linear(in_features=1024, out_features=204, bias=True)\n",
+ " )\n",
+ " )\n",
+ ")"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "inreP0e4NNII",
+ "outputId": "a6e9416c-68e6-4b88-f150-5c1df5234812"
+ },
+ "source": [
+ "frames,rews,actions=test()\n",
+ "print(len(frames))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "score: 1.4000000000000008\n",
+ "115\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hb1loZObNVnG",
+ "outputId": "2f3a35d8-18b3-411c-ef79-4af11a2bbe4f"
+ },
+ "source": [
+ "n=np.bincount(np.array(actions))\n",
+ "rews[-1],n,actions[0]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(-1, array([114]), 0)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 38
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "NevjMfKQbf77"
+ },
+ "source": [
+ "def get_image(frame,shape=[5,18,3]):\n",
+ " color={-3:[120,100,255],\n",
+ " -2:[0,0,255],\n",
+ " -1:[0,0,0],\n",
+ " 0:[150,255,255],\n",
+ " 1:[255,255,255],\n",
+ " 2:[0,255,50]} \n",
+ " out=np.zeros(shape)\n",
+ " for k in range(shape[0]):\n",
+ " for l in range(shape[1]):\n",
+ " out[k,l]=np.array(color[frame[0,k,l]],dtype=np.uint8) \n",
+ " return out.astype(np.uint8)\n",
+ "def resize_image(frame,mag=100,shape=[5,18]):\n",
+ " img=cv2.resize(frame,(shape[1]*mag,shape[0]*mag),fx=0, fy=0, interpolation = cv2.INTER_NEAREST)\n",
+ " cv2.rectangle(img, (0,0), (shape[1]*mag-1,shape[0]*mag-1), (0,0,0), 1)\n",
+ " #cv2_imshow(img) \n",
+ " return img\n",
+ "def get_image_from_frame(frames):\n",
+ " final=[]\n",
+ " \n",
+ " for f in frames:\n",
+ " out=get_image(f,shape=[7,7,3])\n",
+ " out=resize_image(out,shape=[7,7])\n",
+ " final.append(out)\n",
+ " return final "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "82lE8rIgR6ii",
+ "outputId": "8f6ca08a-4b9d-4c2d-fc48-5c647cb0a589"
+ },
+ "source": [
+ "np.where(np.array(actions)==0)[0],np.where(np.array(actions)==1)[0]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "(array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,\n",
+ " 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
+ " 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38,\n",
+ " 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
+ " 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64,\n",
+ " 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77,\n",
+ " 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90,\n",
+ " 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103,\n",
+ " 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116,\n",
+ " 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129,\n",
+ " 130, 131]), array([], dtype=int64))"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 32
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Iks-uHO_Tj8G",
+ "outputId": "abd81e41-8203-4093-de7f-be4d3b4900bf"
+ },
+ "source": [
+ "all_frames=get_image_from_frame(frames)\n",
+ "print(len(all_frames),len(frames))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "76 76\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "2wztDvUCSG_T",
+ "outputId": "8c8ad7e7-6c5d-48d0-ee75-78c587972d49"
+ },
+ "source": [
+ "f=[]\n",
+ "a=[]\n",
+ "for i,frame in enumerate(frames):\n",
+ " pos=torch.tensor(frame).float()\n",
+ " g=optimal_agent.greedy_action(frame)\n",
+ " r=optimal_agent.risk_action(pos,alpha=3)\n",
+ " if i==len(frames)-1:\n",
+ " continue\n",
+ " if g!=r:\n",
+ " f.append(i)\n",
+ " a.append(actions[i])\n",
+ "print(f)\n",
+ "print(a)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "[4, 6, 7, 9, 14, 19, 21, 22, 36, 40, 44, 51, 67, 68]\n",
+ "[1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0]\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 734
+ },
+ "id": "ezTDjiZFS2Lj",
+ "outputId": "cf035d25-9c26-42b7-eb5d-33dd4d9a5559"
+ },
+ "source": [
+ "n=19\n",
+ "cv2_imshow(all_frames[n])\n",
+ "d[optimal_agent.greedy_action(frames[n])],d[optimal_agent.risk_action(frames[n],alpha=4)],actions[n]"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<PIL.Image.Image image mode=RGB size=700x700 at 0x7F6052E02B10>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ }
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "('U', 'L', 1)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 123
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 496
+ },
+ "id": "9rlMlQZwTQs8",
+ "outputId": "4b7665e1-b1cc-411e-869b-9d2dc9af26c8"
+ },
+ "source": [
+ " d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "plt.figure(figsize=[15,4],dpi=150)\n",
+ "pos=torch.tensor(frames[4]).unsqueeze(0)\n",
+ "p=optimal_agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=optimal_agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(optimal_agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(optimal_agent.support.cpu().numpy(),p[i])\n",
+ "print('RN:',d[np.argmax(m)])\n",
+ "print('RA:',d[np.argmax(m-4*np.sqrt(variance))])"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [5.378173 5.369012 5.3822527 5.360159 ]\n",
+ "Var Q: [3.1587105 3.1563442 3.1681843 3.1622562]\n",
+ "Entropy: [2.503379 2.5065067 2.5074878 2.5146065]\n",
+ "RN: D\n",
+ "RA: U\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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xSW+o+v6IpNskHaw+kZm9UdIbS4flHr4HzezLwdfe5O7rdf4BgB1rdLrSdGX/YNeWznEoaP26nHeNTi/qwK7uDf4CZY2oqIy0KVjj89jbFLi7rfVT+ko+eO/zcdwDAGTJ/FJezwctd27bYkVlT2d7ZGGm/WvzYA4XAESNBZvOfVvYdIYbTioqt4x9IwBkjLvreDCj8pYtJPP0dLZHOg+cvMy+sR7uviDp1ZJ+Q8VE1+9XMVD5UUkvc/fn6jjdYRUDnHerkgDUGbx3t6StZTIDQIsbDSoq9w1sLbjY35XTUG9l1vO5KdbEWm07UEmbAgDIlmcuTa/2VO/KtUUyXOt1ZLjyt6fGWVzrkJU5XG9q5ByutX4knWjE+QEgLgvLeU0vrKweb2XTGc4aoaJya9g3AkD2XJ5Z0pX55dXjW/dvdWxIZdvzPHMq6+bu8+7+Dne/xd273P2gu7/e3a8ZJeLuD5SSan56g882+vl8jfdkzKcEsJOMbjO5tSzsKnCWJNeaNWrBoU0BAGRE2Pb1tgMDam+zDb69sRv29OqrJyckSafYcNYjS3O47l/j8/ZgJtcvuvujMd0HAKQurKY0k/b0d9Z9DioqG4Z9IwBkyPHRyt5xT1+nhvvqXyMl6caRPn3l+dK+8TL7RgBA87nUgIpKSTo81KOvnbsiiUBlPRoSqHT3BTN7taS3S/oxFdsUTKjYpuD+tTKANlBuUxDqrHqPNgUAsI6nLl5d/f22LWbElh0JZlJSUVkX5nABQEaMTlc2nMO9nepor7+pDBWVjcG+EQCy5dmg7evN++pv+1p2dKTSieckCa4AgCbUiBmVUnTveHaSZ6m1algJv7vPS3pH6Wez7z4g6YF6P9vCPW29jAgAmlQYULx1/9Y3m1KxonL1vMyorEdkDtcaFYuxz+Fa7zMzcxXncNHGB8COMHq1suHcO7C1DWdYUXnx6oJW8gXlthDwBPtGAMiSMFB563YClWHrVyoqAQBN6FKwb9xuRWUZFZW1Y3cNAC1mLJIBtPWFVZKOBPMtT4/PysvDL7Eh5nABQHaMzTQgUBlkxeYLrotBWyAAAJrV8UuVQOUtDaqoPDMxr3yBfSMAoLmMBZ14tlNRyYzKreJ14NYAACAASURBVKGaAgBaTNjibjsZQFK09evsUl7js0sa6d/6Yr3DMIcLADJgtAGZsd0d7Rrp79TlmSVJ0vmphcgGFACAZvTsWFhRufWxIUeGK4HKpXxB56fmdf0w6yQAoDks5wurez1pmxWVw9GxIYWCq62NBi6boaISAFpIoeCRhXWrlSNlu3s7NNBdyWlhTmXt3H1B0qsl/YakORXncB1RcQ7Xy9z9uTpOV57Ddbcq8y07g/fuFnO4AGBNYaeB7ayL10XmVLIeAgCa29TcUmSN3M7YkJ7Odh0IuvkwpxIA0EwuB114JGlfg2ZULq0Urjk31kagEgBayMTcUqTNznYWVkkyMx0J5o2cnmDDWQ93n3f3d7j7Le7e5e4H3f317n52je8+4O7m7j+9wWcb/Xy+xnsy5lMC2EminQYas+E8RwsfAECTC+dTDnTltrVGStLRkcq+8SQJrgCAJhLOpxzszqm7o33L5xro7tDu3o7V4zPsHWtCoBIAWkiYEdvd0aaBru3Ho8I5lVRUAgCazWiwNjYqM/bcFJtNAEBzCwOVt+zvl9n22tLdGMypPHmZBFcAQPMYvRrOp9zeGC0punc8O8mz1FoQqASAFjJa1d5uu5tNKTqn8jSBSgBAk4m0ft3GnOVDQ+Fmk0AlAKC5HQ8DlXu33va1LJrgSqASANA8GpXcWnaYvWPdCFQCQAsJM4C2M/g5FLZ+PTVBoBIA0DzyBY/MBNm3jexYKioBAK0kDFRuZz5l2dEgUPk8FZUAgCbS6Oeph4cqz1IJVNaGQCUAtJCxmcZUjYRuGCYzFgDQnCZmlxSMbtbe7cyoDLJiz0/Ny903+DYAANn23FhQUblv+4HK64cr6+TFKwsbfBMAgGyJs6KSJNfaEKgEgBYyerWxC6sUrai8PLOkmcWVhpwXAIC4jU5XHpT2drarfxuzmw/vrqyHC8sFjc8ubeveAABIi7vrUlA9cn1Q+bFVe/oq+8/ZpbwWlvPbPicAAEm4FGtFJd3pakGgEgBaSBwVlQcGu9XRXpl1efEKmUAAgOYwVjW7eTsGe3KRQOc5WvgAAJrU5NyylvOVzgCNeCg71NcROZ6aW972OQEASEJYUbm/0RWVk3TjqQWBSgBoIWMxVFS2tZmGejtXjydm2XACAJpDpIXPNgOVZsacSgBASwgrR7pybRrs2XrHgcp5op0LxmcXN/g2AADZcSl8ntqA5J1wbMjiSiFSWIK1EagEgBYSLnyNWFjLhvvCQCWt7gAAzWFsOr4NJxWVAIBmFQYq9w92y8w2+HbtwqrKSRJcAQBNYCVfiCTXNKKicrC7Q4PdleSds+wdN0WgEgBayGiw4dxui7tQtKKSQCUAoDk0svWrJCoqAQAtodEt7sqGw33jHPtGAED2XZ5ZUtiZtVGFH9E5lewdN0OgEgBaxOziimaX8qvH221xFxrur2w4J9lwAgCaxOh0YxN4riNQCQBoAWGCayM78QwFnXgmSXAFADSBcM840J1TT2d7Q85bPacSGyNQCQAtIqwaMYu2a92uYSoqAQBNqOEVlWw2AQAtIDKLK6aKynH2jQCAJhCdT9m4NTFaUTnXsPO2KgKVANAiwvY9e/q6lGtv3H/ih5hRCQBoQqPTjd100voVANAKqmdUNsowFZUAgCYTVlQ2ck0MKypp/bo5ApUA0CLGGvwwNrSHQCUAoMm4u0Yj2bHb33SGm80r88uaWVzZ9jkBAEjapZhmVEYSXBkZAgBoAnFVVB6KBCqpqNwMgUoAaBGNnsMViswaYcMJAGgCs0t5zS9XZjc3Ym3c29+lzqBjAe1fAQDNaCysqGzgjEoqKgEAzWYsoYpKd2/YuVsRgUoAaBGNbm8XiswamWHDCQDIvtHgIWx7mzVkdnNbm+ng7srm9dwUmbEAgOZSKHh079jIispeOvEAAJpLWFHZyMKPcEbl4kpBl3meuiEClQDQIsLWr42uqBymohIA0GTGIrObO9XeZg05b2ROJRWVAIAmMzG3pJVCpapjX0wzKglUAgCaQVwzKnf1dGigO7d6fG6KveNGCFQCQIuItaIy2HDOLeW1ELTSAwAgi+KqFgkDlWfZbAIAmsyloONAT0e7BrpyG3y7PtUJrrS5AwBkXVwzKqVoVSVzKjdGoBIAWsRY5IFs4zKAJGmoryNyTHYsACDrIp0G+hsYqByiohIA0LzCRJ79g10ya0zHASkaqFzOu2YWVxp2bgAAGi1fcI3PhOtiY5+nVs+pxPoIVAJAiwiHPze69WtXrl39QaYtgUoAQNZFOw00bsMZVlSep6ISANBkwhnOjU5w3dXToTDuOTm73NDzAwDQSOMziwq6oTe0E49U1Y2HisoNEagEgBawki9oPAgeNrpVgRStqmROJQAg68JZIw1t/RpWVBKoBAA0mThb3LW3mXb3VPaN47OLG3wbAIB0hWviQFdOvZ2Na4cuUVFZDwKVANACxmeXFI7/aHRFpSQN91ba+FBRCQDIukjr1waui2FW7Oj0opZWCg07NwAAcQtnVDa6xZ107ZxKAACyKkxu3dvgakopGqi8MLWwwTdBoBIAWsBokAHUH0MGkBTdcBKoBABkXWR2cwMDlQd39ay2tXOXLlwhMxYA0DzC6pH9MTyUje4baf0KAMiuyJrYwHEhZSP9lXV2nGepGyJQCQAtYGwmaG8XQzWlJA2FmbEsrgCAjIurorIz1xZZa8/RwgcA0ETGpuOtqBzqZd8IAGgOcY0LKavuMlAIB2IigkAlALSAsKJyJKZAZdj6lSwgAECWLV8zu7mxD2LD9q9nmVMJAGgiYfVILCNDwopKWr8CADIs2mWg8ck7e4KKynzBdWWeTgPrIVAJAC0grvZ2oeF+Zo0AAJrD+Ex0nWr0g9hDQ72rv1NRCQBoFvmCa2wm3oeyYSeeiRn2jQCA7Aq7DMTxPHWwO6eOdls9pvBjfQQqAaAFjMbU3i4UVlQyoxIAkGVhC5+B7py6O9obev6wovIcFZUAgCYxPruofNB2LpbqESoqAQBNIqyo3BfDmmhmkZbo40GyEKIIVAJAC4j0VI9h+LNUPaOSVgUAgOwKW6LHkRl7aCgIVFJRCQBoEuH62NfZrv6uXMOvwYxKAECzGI25olKKtn+l8GN9BCoBoAWMJVBRGWbG0qoAAJBlYVu7ONbFw1RUAgCaUPhANo5qSokZlQCA5pAveOR5alzrIs9Ta0OgEgBawGgCMyojFZVzS3L3Db4NAEB6ohWVjd9whhWVF67Mq1BgTQQAZF/Y4i6uBNfIjEoeyAIAMmp8dlHhNi6u56lhAs84s5vXRaASAJqcezQDaN9g/DMq8wXX1YWVWK4DAMB2jc1UKkbieBB7YFcl+Lmcd03N0xIdAJB9l64mUFEZ7BuvzC9rJV+I5ToAAGxHmNza35VTXwzt0KWqTgOzzKhcD4FKAGhyVxdWtLhS2fzt7Y8nULmrp0NtVjkmOxYAkFVxz6gc6Mqpo72yKI7PsOEEAGRfWFG5P64E1/7KA1n3YrASAICsSWI+pSSN9NP6tRYEKgGgyYXVlLk201CQwdpIbVXnJlAJAMiq0ZhnN5uZ9vRVzsuGEwDQDMYSmFHZ19muzvbK48ZJ5lRuysx6zOxdZvaMmS2Y2Xkz+7CZHarzPPea2TvN7LNmNmZmbmYnN/h+h5m9xsw+YGZPmNmcmc2b2VNm9ntmtnfb/zgAyKgweSeu7nSSNBzuG2n9uq546lkBAIkJM4BG+rvUFpY9NthQX+fqw9hJHsoCADIq0hI9hhmVkrSnv1MXSy302HACAJpB9KFsPOujmWmor2P1WhOzVFRuxMy6JX1O0j2SLkj6lKSjkl4v6XvN7B53f67G071f0kvquPy9kv629PtJSX8tqUPSKyS9VdK/NLNXufvTdZwTAJpCtAtPPGuiVN36lX3jehpWUUn2DwCkI4n5lGXDVFQCADIuqdnNe/rDikpavwIAsi+cURlnm7toJx7WyE38uopByi9JeoG7/4i7361ioHCvpA/Xca4HS+f7Tkl31vD9gqQ/l3S3u9/o7j/k7vdJukXFAOYBSR+p4/oA0DQuRboM0Po1bQ2pqCT7BwDSE60aiTlQGWYB0cIHAJBBV+dXtJSPf3bzSLAmXqaismZm1iPp7ZJ+VNINkiYk/Y2k+939XB3nuVfSqyR9c+lnRNIpdz+6zvc7JL1a0n2lv7tJkqm4h/yspN9x97Et/JMAoCms5Au6PBPOqIyvemRPfxiopKJyPWbWKenNpcOfd/eZ8mfu/j4z+ylJ95rZy939oc3O5+5vC859oIbvf07F57nV718xszdIOifpFWZ2xN1Pbf4vAoDmkUZF5eTckgoFj7UbXrNqVEUl2T8AkJK453CFhsLFlSwgAEAGhS3RO9pNu3s7YrlO+BB2fIZqkVoECa73S+pXMcH1jIoJro+Y2U11nO79kh6Q9N0qBik3U05w/XlJfSomuP5d6W/fKulxM7utjusDQFMZn11SwSvHSVVUMqNyQ6+UtEvSCXd/ZI3P/6L0+trkbqnI3c9LKifwXJf09QEgbuG+Mc4OdXuCGZX5guvKPAk8a9l2RSXZPwCQrrFIoDK+DCBJGu6rPOylXQEAIIsi62J/l8ziyVYdDjaczKisWZjg+pry3tHM3iLpvSomuL6qxnM9KOkTkv5Z0llJT27y/XKC63vd/avlN81sl6SPq5go+xFJ31Lj9QGgqYSVIwNdOfV1NaTJ2pqYx1Wzcke5h9f5vPz+XQncS4SZ7ZY0VDq8mPT1ASBuSVVUDvbklGszrZSyhcZnlyKFIChqREUl2T8AkKIwAyjuisrwoSwVlZtjfjMAJC/SaSCxtnasiZvZLMFV0uMqJbjWcj53f5u7/6a7P6hi+9jNvv+5Uuefr1a9f0XSG0qHrzCzI7VcHwCaTTifcm+MlSNSVUUla+RGbii9nl3n8/L7aaxNP69igcvX3P35FK4PALEpFFxjkXbo8a2LZhZJ4KEbz9oakT5F9g8ApCiaARR3oLJSUcmMyo0xvxkA0hFp4RPjujgSBCovz7LZrEEtCa53qZjgumknnkZy9/NmNqbi2JLrJNGJB0DLuRSsj/tj78QTPJAlULmR/tLr3Dqfz5ZeBxK4l1Vm9g0qdkGQpF+u82/X63Bw87ZuCgAaaHx2SfmgH/q+GBNcpeK6WE6oJcl1bY2oqCT7BwBSFGYAxR2oDDNjWVg3xfxmAEjBWEKzm/fQ+rVeJLgCQIouXU2mckSKBiqZUdlczGy/pL+U1C3p9939r1O+JQBouDC5ta+zXf0xtkOXpJH+YO/I89Q1NeL/AmT/AEBKFlfympqrDGGOu/Vr+FCWQOX6mN8MAOkJW7/GmcATtn69Mr+spZWCOnONyANtWSS4AkCKRoPWr/sTqBwpY9+4ofI+sXedz/tKr9MJ3IvMbEDSX6nYCegTKibZ1sXd10ysLT1rfeF27g8AGiXSnS7mNVGq6jRAkuua4g0Vp4TsHwA7xeWqxS3uQOVQ0Pp1emFFy/mCOtp5KLsG2tsBQErSqKiUihUjcT/4bXIkuAJAiiKJPDGvV8yorNnp0uvhdT4vvx/7nq00uuTTkl6mYkefH3f3QtzXBYA0JDUupCyawMPYkLU0IlBJ9g8ApCTMit3V06GuXHus1wsXVqm46Uwi86gJ0d4OAFISraiMb43q6WxXb2e75pbykqTLM4sEKpsMCa4AdpJLV5N7KBvuG2eX8lpYzqu7I969apN6rPT6snU+L7//eJw3YWY5SR+X9CpJX5T0g+5OhBlAy7qUcEXlSD+zmzfTiDIYsn8AICVjCbW3K+vtzKm7o7J0TDBvZD20twOAlIRJPLG3RO+ntV0dWjLBda0fSScae7cAsH3RGZUxV1QGnXgk5lRu4AuSrki62cxeusbnryu9fiauGzAzk/QRSfdJelTS97j77MZ/BQDNLayo3J9IRWUwo5LWr2tqRKCS7B8ASEm0fU/8C6skDffyULYGLdnebq0f0d4OQIYsLOd1dWFl9TjuJJ49bDjrQYIrAKRkJV/Q+GwYqIx3fezKtau/q9LEjX3j2krPLT9QOvygmZWTdmRmb1GxA88/uvtDwftvNrNjZvaeBt3G70v6cUnHJL3G3acadF4AyKxoRWXSrV9ZE9fSiNavkewfd3+06nOyfwAgJmGgcm9/MoHKob5Onb9SzDxicW0etLcDsBOEnQYkaSTmtTFs4XN5hlkjmyDBFQBScnlmSe6V4zhbo5cN9XVoZrGYPDQ5uxz79ZrYuyV9h6RvkXTczP5Jxc47d0sak/SGqu+PSLpN0sHqE5nZGyW9sXRYLms9aGZfDr72Jnd/uPT975P0C6X3z0j6d8VHrNf4bXc/Vue/CwAyK6lxIWW0ft3ctgOV7r5kZh+Q9GsqZv+8phwk3Cj7R9KbJf1Xd3/7du9BZP8A2KEirV8TmosVZgFNsriupyXb261zbuY3A8iMsSBYONTboc5cIxrIrC9SUcmauBkSXAEgJeF8ysHunHo6458XOdzbqTMT85IYGbIRd18ws1dLerukH5P0/ZImJH1U0v3uvt44kbUcVjHAGeqsem8w+H0o+P1/3eC8H1XxmSsAtIRwXEjSFZWTc0sqFFxtbWsmhuxYjaiolMj+AYBUjAU91ZOqqIy2KyAzdh20twOAFIxeTTYzNpxROU5F5YZIcAWA9FyKPJBNJsF1KNw3skZuyN3nJb2j9LPZdx+Q9EC9n63z/Y+qGIQEgB2jUPBo4UcS+8YgwTVfcF2ZX46sk2hQoJLsHwBIx1gKMyqHIjMq2XCug/Z2AJCCsKJyb8zzKaVo8g4zKmtCgisApODSdHLzKcsiCa5zJLgCALJhYm5JK4VKP/Qk1sXBnpxybbZ63fHZJQKVVRpVUUn2DwCkII0ZlXvYcNaC9nYAkIKxsGIkgUBlOAOT1q+bI8EVANIRtrjbn0DliFRs/VrGyBAAQFaEXXh6OtrV39WwENm6zEzDfZ2rz3HHZxZ1y77+2K/bTOId2gIAiM01rQqSqqhkRuWmSlWLHygdftDMyjMpN2xvZ2bHzOw9DboN2tsB2HEiCTwJBCojrV/pMlATd59393e4+y3u3uXuB9399WsFKd39AXc3d//pDT7b6Ofzwfc/WsP3I38DAK0i0ho9jdavzKgEAGTEpWCM1v7BLq3TZaXhoqO0WBerxR8uBgDEYmp+OdKqYG9SmbFhmzsW1o3Q3g4AEjaWdKAymDVC61cAQFZVP5RNQuSBLGskACAjxq4mO5+yrNiNZ1oSz1PXQqASAJrUaLDZ7My1abA7mf+kD1NRWRPa2wFA8kank60YGQkqKueW8ppbWlFvJ1ssAEC2XErhoexQ2PqVikoAQEZcCseFJJS8I1UVfpDAcw120QDQpCJtXwdSalUwtyR3T+zazYb5zQCQrLGEZzeHbe2k4oazd5gtFgAgWyIzKhN6KBu2R6fFHQAgKyLJrQlWVEZbvzI2pBozKgGgSYVzRpJob1cWZsYurRQ0u5RP7NoAAKynUHBdnkl2dnNHe5t29XSsHtPCBwCQNcv5QmR92p/UjMqqikp33+DbAAAk41IKyTtStBsP+8ZrEagEgCY1NhOtqEzK7t6OyDHtXwEAWTAxt1Q1uzmNihEyYwEA2RJ2G5CSWx/DypHlvGtmcSWR6wIAsJHouJAkW79WrkXr12sRqASAJjWa0vDn6uoR2vgAALIgfBDb3dGmga5kWrCOBBvOy2w4AQAZE1aO7OrpUHdHeyLX3dXToXBCCPtGAEAWjGWi9StrYjUClQDQpEanKxvOJFu/SiyuAIDsqZ41ktT85LCiksxYAEDWXAoSXJNscdfeZtpNgisAIGPC9Wikn9avWUGgEgCaVDQDKNlA5VAvG04AQLaE62KSCTzRQCWtXwEA2RImuCY1n7IsTHCdnGPfCABI1/xSXvPL+dXjob6ODb7dWNVrYqHA7OYQgUoAaFJpPZCVon3V2XACALIgfBCbZAJPZNYIyTsAgIxJa2SIVN2JZznRawMAUG2i6hnmUG/nOt9svD3BvjFfcF2ZZ10MEagEgCaVVk91SRoOMo54KAsAyIK0EnjCFj6XqagEAGRMOKMyydavUvQB8MQsayQAIF2TwTPMge6cOtqTC48N9uSUa6uMJ+F5ahSBSgBoQvNLeU0vrqwe70t6wxm2K2BhBQBkQCRQmeCskTAzlnboAICsuZTiyBAqKgEAWRLu18I1KglmFrkmY0OiCFQCQBMK29uZSXsSXlz3RDacPJQFAKQvXI/2JBmojMyoZE0EAGTL6NWMzKhk3wgASFk4virJtq9lwzxPXReBSgBoQmHVyJ6+TuUSbFUgRRdzZlQCALJgcq5SqRG2KI9b2Pp1fHZR7p7YtQEA2EzY+nVfioHK6rlgAAAkLc2KSkkaCRJqaf0aRaASAJrQaBCoHEmwaqQs0qqAhRUAkAFhpcbuBLNjw9avy3nX1YWVDb4NAEByFlfykUSeNGdUUlEJAEhbuBZRUZktBCoBoAmFFZVJZ8VKtPABAGSLu0cq/JPMjt3V06H2Nls9ZtYIACArwn2jJO1NdUYl+0YAQLrC6v5whEdSmFG5PgKVANCEwhmV+xLebErRhXVqfln5Am3uAADpmV/Oa3GlsHq8uze51q9tbRbJxqXTAAAgKy5drTwEHertUFeuPdHrD9H6FQCQIZOzlS4DaVRU7qFD3boIVAJAEwqzUdPIAAo3nO7SFJtOAECKqqs0kt50RuZUzrAmAgCyYTSYT7k/hU484QPZK/PLWskXNvg2AADxGp+tJPAM9yWX3Fq2JxjfRaeBKAKVANCEIoHKFIY/D3Tl1NFeaXM3SaASAJCiqWD+1kB3Th3tyW5zwqShcPMLAECaRlMeGVKd4HplfnmDbwMAEK+0KyqjrV95lhoiUAkATSjthdUs2uZuYpYNJwAgPWECTzotfCqZsWw4AQBZcSmsqExhZEhfZ7s6g+QhElwBAGkK25APp1D4EU1wZU0MEagEgCaU9sJafd0JqkcAACkKH3wOpb3hnGFNBABkQzijct9g8oFKM9NQ0FqPZB4AQFrcXZOzKe8bg2tOzi2pUPDE7yGrCFQCQBNKe2GVqgOVVFQCANITWRd7U5g1EqyJl8mMBQBkxOh0ujMqpWinAyoqAQBpmV5c0UoQGBxOuRNPvuC0RA8QqASAJlMoeLRyJIWFVYoGSNlwAgDSNBHMqExlw9kftn6lohIAkA1jwYzKvf3JV1RK0a4DJLgCANISJre2mbSrJ/kE18GenHJttnpM+9cKApUA0GSuLiwr7AyQxgPZ6uvSwgcAkKapIGFmdyqZseFDWNZEAEA2hA9A96QUqKSiEgCQBROz0aKPtiBgmBQzq+pQx7pYRqASAJpMuIi1t5kGunOp3McwFZUAgIwI18bhvhRav0YqKlkTAQDpq57FNZyBkSGskQCAtExkYIyWVL0u0o2njEAlADSZaNvXjlQygKTqGZVsOAEA6ZkKWr+msekcCdvazS0pH7Y+AAAgBVfno7O4wrUqSVRUAgCyIJLcmlJ3OinaEp3WrxUEKgGgyYRzPdKaTylFHwQTqAQApKm6jU/SwopKdx7EAgDSd3m2UqXR3mYa7E6+44BUPaOS9REAkI5I4UcKXXjK9vRV9o6sixUEKgGgyUxmpFUB87gAAFkR7TaQ/NrY19muzlxla0VrOwBA2iaq2r6m1YmHikoAQBaEhR9ptUOvvjatXysIVAJAk5mYy0arAjacAICsSDs71sw0woYTAJAh4Vq0JyMPZElwBQCkZTLlLjxl4ZpM69cKApUA0GSyUlEZbjjnlvJaWM6ndi8AgJ1rfimvheXC6nFaSTxh+1c2nACAtI1XVVSmJXwYTKASAJCWSOFHmh3q+mn9uhYClQDQZKItfNLrqV5dscLiCgBIQ3VV/+7UApVUVAIAsmMiaEMePhRNGgmuAIAsmMxIAk+09SvPUssIVAJAk0l7DldZV65d/V251WMClQCANITrT39XLjIrMkl7+qioBABkR7gWpdn6tTrBlbEhAIA0TGSkQ10kwZV94yoClQDQZCYykgEkRTedBCoBAGlIez5l2Uiw4bxMZiwAIGVZaf1KgisAIAsirV8zMqNycm5JhYKndi9ZQqASAJrM1Nzy6u9pZgBJ0YWdzFgAQBomw3UxxQ1ntIUPrV8BAOmamK2sRWH1RhpIcAUApGklX9CV+cq+MdUZlUEnnnzBI/e1kxGoBIAmk5UMICm6sLPhBACkIZw1kmagMpz/RQsfAEDawrlXabZ+laL7VvaNAICkXZlflgeFi2kWfgz25JRrs9Vj9o5FBCoBoIlkKQNIii7sbDgBAGmIzm5Or/VrWK3CmggASFu09WvXBt+MX7hvnGSNBAAkLNwzdra3qa+zPbV7MTMKP9ZAoBIAmkiWMoAkMmMBAOmLVFSmuC6OBA+BL9P6FQCQokLBI+tj2q1fIw9k52hxV83MeszsXWb2jJktmNl5M/uwmR2q8zz3mtk7zeyzZjZmZm5mJ2v4u3Yz+yUz+5qZzZf+9s/N7I4t/6MAIEMmZsMxWh0ysw2+HT/Ghlwrt/lXAABZkaUMIEka7mdGJQAgXeGMyjRboocPgacXVrS4kldXLt11GgCwM11dWNZKoZLhmqXWr1RURplZt6TPSbpH0gVJn5J0VNLrJX2vmd3j7s/VeLr3S3pJnddvk/QJST8gaUrSZyWNSHqdpO8xs1e7+1frOScAZM1EhroMSNG9I61fixpWUUn2DwDEL3MZQL1hBhALKwAgeWGizO4UH8RWt2On08Da2DcCQPzCh565NtNgd3qt0SVGhmzi11UMUn5J0gvc/Ufc/W5Jb5W0V9KH6zjXg6XzfaekO2v8mzeoGKQ8Lul2d3+du79K0g9L6pX0MTOj0AVAU4sGKtNdEyVpTxAsZV0sashCQ/YPACQjXLyGUqwaWb2HPioqAQDpimw6U1wbUMnvRQAAIABJREFUuzva1d+V08ziiqRiAs/BXT2p3U8WsW8EgGRMVLVFb2vLTos7HshWmFmnpDeXDn/e3WfKn7n7+8zspyTda2Yvd/eHNjufu78tOPeBGm/jLaXXt7n7peBcnzSzT0u6T9L3SfpkjecDgMwJn1lm4XkqrV+v1aiKSrJ/ACAB4cJaXbmRhj2RDSezRgAAyZsKWr8O9aabHRu28GFO5ZrYNwJAAsKHnmm3fZWiD4VJcI14paRdkk64+yNrfP4XpdfXxnFxM7tR0h2S5lVM3kn0+gCQlGhFZfrrYrg20/q1aNuBys2yfyQ9rlL2Ty3nc/e3uftvuvuDkiZqvI11s38kfVrSLSpm/wBAU8t6RWUhmIMC2tsBQBKqq0bStIeKkXWxbwSA5IQPPcMkmrQMdldyQKYXVlK8k8wpdwZ4eJ3Py+/fFfP1n3D3tTKP474+ACRiMmPPU/f00/q1WiMqKsn+AYCERBbWDPRU391TuYd8wTW7xKazLGhvd7+kfhXb251Rsb3dI2Z2Ux2ne7+kByR9t4ot6mq5frm93fskHVZxjXxSxfZ2/5+ZfXMd1weATFpYzmt+Ob96nHZ2bLjhZHbzNdg3AkBCwjVoOJiDlZaBYEbm9AKdeAI3lF7PrvN5+f0jLXp9AEjERMY61EVbv7JvlBozo5LsHwBISGRhzUAGUH93dBmZWVyJbEJ3uLC93WvKlSNm9hZJ71Wxvd2rajzXgyoGHf9Zxc3ikzX8Tdje7lvLlSNm9kMqPoz9mJnd4e5ElwE0rer2cbtTbv06ErZ+naX1axX2jQCQkLA6IwutX8N948ziitxdZunOzcyI/tLr3Dqfz5ZeB5rl+ma23l715lrPAQCNNpmhLjxStNsBrV+LGlFRmXb2TdrXB4DEZG1h7cq1qzNXWUpmaOMjifZ2AJCU8EFsX2e7unLtKd4NmbGbSHvflvb1ASAx4xkLVA4EgcqCS3NL+Q2+DQBAY2Wt8GMPo7Su0YiKSrJ/ACAhE3OVAoAstCqQpIGunMZXigv+9CKBypJa2tvdpWJ7uYcaffEa29vdV7r+Jxt9fQBIylSwLu7OxIYzbP1KRWUV9o0AkJBwDRrOwIzKgTU68fR1NeKRZNMrJ7T2rvN5X+l1ulmu7+53rvV+ac18Ye23BgCNMzmbreep4b4xX3BdmV/OREFKmhpRUQkASEjWhj9LVW18qKgso70dACQgrKjMxIaTFj4AgAzIWuvXrly7OtsrjyCZU7nqdOn18Dqfl98/1aLXB4DYLa7kNRMUVmRh3zjYk1OurdICnb1jYyoqyf4BgIRMZuyBrCT1d0XnjUBS+u3l0r4+ACRiai5bLdFH+sOKSjabVdg3AkBCIq1fg7UpTQPdudX7ukqCa9ljpdeXrfN5+f3HY77+i8ysY40k17ivDwCxC6spJWl3b0dKd1JhZhru69TodLEDwgSByoZUVKadfZP29QEgEUsrhUhr1Sw8kJWqApVsOMtasr3dWj+ivR2AFE0Em86hDGw4oxWVi3Jn1kgg7X1b2tcHgEQUCp65jgMSnXjW8QVJVyTdbGYvXePz15VePxPHxd39eUlPSeqR9D1JXx8AkhCuiX2d7eruaE/xbirC9ZmxIY0JVGYq+yeF6wNAIsKqESkbw5+l6LwRZlQCAJI0OZetlujhrJGF5YLmlvIp3k3msG8EgARcXVhWvlBJlBnpy05FZdk0gUpJkrsvSfpA6fCDZlau7peZvUXFUR3/6O4PBe+/2cyOmdl7GnQb7yu9/q6Z7Quu84OS7pP0rKRPNehaAJC4yYx14SljbEhUI1q/RrJ/3P3Rqs9jz/4xs6ck3aFi9s9/S/L6AJCUiWBh7e5oU09nNjKAqKhcE+3tACABWQtU/v/s3XuQ7Old3/fPt6/TPddz2bN7hCwhrbQbS7IAYWtlBJFxUTaGiEuAEBKSQiqnTEnKCktgLILEIuSIqMxGIInYTkXgClWAkWUoVbg4ZRBWMIgY1ggEYlerCyjac86emTnnzH16up/80d3Tz9NnZk739K+7f7/neb+qtub0dE9PH0ml736f53sZ7upc3z7UYj2LlCsK5I0AMAP+YWelZFpp5CMOLdcHMXL7gB2VnndJ+jpJXyXpKTP7mLorOh6R9Kyk1w+9/rKkhyVdHX4jM/v7kv5+72H/P/CrZvZ73sve4Jz7Q+/xByV9g6RvlfQpM/t3vd/xGkl7kr7bOUeiD6Cw8jhlQAqLXBn9mkFHJdU/ADAbQWDNwWFsXzDCh4Szb97j5eb9+wFgJsKkc/6jXyvlUnBZeXOHET595I0AMBv+juQLizWZ2Rw/zcASHZUncs7tS/paST+m7uqOb1H3ovJnJb3COfeZMd7uuepecD6iwaSAmve9RyStDP3+jqTvkPRWSV+U9F9I+muS/rWkv+6c+/h5/l4AkBd5K27tY/RrKKuyKqp/AGDK/OXPeRpVsBRUxvJ/tT25Gm/nnBu+QWa8HYAo3Nod/N/bWk6SzktLdW32Ppd/WAxJ5I0AMHUbXpHMpRzljf7o1ztcVAacc3uS3tH7516vfUzSY+M+d4/3bKtbzPP4vV4LAEXjF7fmKS76n4XRr9nsqKT6BwBmwB/9mqdRBewaOVEw3u6E56c+3k7Sn0lqqDvebqa/HwBmJY9jfPyEc4OOygB5IwBM302vSMbffzVvy6wMAQDMgZ8z5qnxw/8sfgFuqjK5qJS61T/OuXc4517knKs75646517nnPvCCa99zDlnzrnvOeO5s/756Ak/13bOPe6ce5lzruGcu+yc+w7n3J9m9XcEgHna3MnnqIJgRyUdlZIYbwcAs5LHMT6Xlwa7Rm7SUXkX8kYAmK6wiKd+xitna3lhMIlna58DWQDAbOSxuFWS1ryVIbf3iIv52KgNALinzZx2VC5RGXsaxtsBwBTtt9raPWwfP76Qgx2VUti9wuhXAMCs5XXEnT+JhwJXAMCs5LG4VZJWG4P89dYeeWNmHZUAgOnKbUclCeeJGG8HANM1PB4nL7HxYrBrhNGvAIDZurmdzx2VS6wMAQDMwcbOIG+8mJPiVim8qLzN6Fc6KgGgKDZ28xlY/V0jJJwh59yepHf0/rnXax+T9Ni4z93jPdvqjoB9/F6vBYCi8StjG9WyFqrlOX6agUve6Fc6KgEAsxaMuMvTjkpGvwIA5iCvjR9rjcFn2To4UrvjVC7ZHD/RfNFRCQAFsZnT5c90VAIA5mEzp7tGLnufxe9qAQBgFsLRrznaUekXuJI3AgBmwDmnjZyu0vI7Kp2jiIeLSgAoiKAyNkcVQMGOyoMjOefm+GkAAKnY9CYN5GU/pTTUUblDRyUAYLZuet38l3LVUckkHgDAbO0etnV41Dl+nKfGj+WFisxroLy9x0UlAKAA/BF3a3m6qPQSznbHab/VOePVAABkw6+MzdMIH388+63dQwp4AAAz0+m4IG/M045Kf/TrNheVAIAZ8Js+zKS1Rn4KXEslC6YNcFEJAMi9/VZbu4ft48d5GlWwXA+D/NZB2oEVADAbed01surtGmm1XRC/AQCYpjv7LbU7gwKZPI1+9Qtc91pttdoUuAIApsu/qFxtVFUp5+s6zG9E4aISAJB7flWsJK0181MBtFAtBcueqY4FAMzCZtBRmZ+4OByjh2M4AADT4o99rZRMK43KGa+eLX/0q0TeCACYvmA/ZY6KW/v8PZW3drmoBADknF8BtFgra6FanuOnCZlZkHRuH5BwAgCmL+iozNGkgWq5FOxvTj3hBADMjp83XlysyfzlV3O2WBu6qCRvBABMWV5zxj7/opKOSgBA7m3uDIJVHgOrfyBLZSwAYBY2vQvAPI1+lcKuSi4qAQCzsrFzcPznPK0LkaRyyYK88c4+8REAMF0bOV0X0sdF5QAXlQBQAMGogpwlnFJ4UblFZSwAYAaC0a85i41+EszoVwDArPijXy8t5Ss2SuH4VwpcAQDTthmcp+ZnXUjfqlfgeoeLSgBA3m3mvALITzi3SDgBADMQjLfLWWwMOioTTzgBALPjx8ZLi/U5fpKTBQWu5I0AgCnbyPmEOnZUDnBRCQAFMLxrJG/C0a9pB1YAwGz4iZx/MZgHa97F6a0dOioBALOxvp3f0a/SUIHrAXkjAGC6NoMCnvzFRUa/DnBRCQAFEIy3y1nXiCQtLQwC6zajXwEAU3Z41AniTd4OYy94F6ebiVfGAgBmZz3nB7JB3khHJQBgythRWRxcVAJAAYQdlfnqGpHYUQkAmK1bQ3sf85Z0rvkjfPboqAQAzEYw+nUpf6Nf/Y7KO1xUAgCmbGM33xPq1rioPMZFJQAUQNBRmcPA6iecVMYCAKbNTzgXqiU1auU5fpq7BaNf6agEAMzI+na+D2RX/LyRAlcAwJT5o1/zeJ5KR+UAF5UAUAD+8ueLOesakYZ2VJJwAgCmLJg0kMO4uBaMfqWjEgAwG/7o18tL+YuPwSSe/bQPZAEA09XpuCAXy2PeuMJF5TEuKgGgAPJeARRcVNJRCQCYMr9LcS2HCac/ivY2HZUAgBm460A2h3njsrejcou8EQAwRXf2W+q4weM8nqf6HZXbB0c6anfm+Gnmi4tKAMg551zuZ6ovLbCjEgAwO+Hu5vzFRToqAQCzdnuvpbZ3IntpMX87KilwBQDMip8zlksWjB/PCz9vlNLe38xFJQDk3O5hW4dHg4qaCznsHFkm4QQAzNAt7/JvOLnLA7/L8/ZeSx2/lBcAgCnwx75WSqaVRv4OZJf9AlfyRgDAFPkFoxeaNZnZHD/NyZbqFZVLg891K+EiVy4qASDn/AogKZ8Hsn5HJTsqAQDTFuxuzmFH5QUvVncch7EAgOkbnjaQxwPZYPQreSMAYIrWtwdx8VIOc0ZJMgs7PVPeU8lFJQDknF8BtLJQUbWcv//rDkb4kHACAKZsuDo2b5YXqvLPhxn/CgCYtvXtg+M/57GIRxruqEz3MBYAMH1BzriYv6aPvuFpPKnK32k3ACCwuZvvrhEpTDgZ/QoAmLbwojJ/SWe5ZFptDD7XrYQTTgDAbPijXy8v5W8/pcToVwDA7OR9Ck/fipc3clEJAMitzR2/AiifgXWpPgiqh+2ODo7ac/w0AIDYFSE2+p2edFQCAKZtePRrHg1P4nGOHc4AgOnI+xSevlUuKiVxUQkAuecnnHkNrP6OSomuSgDAdPnTBvIaG4OEczfdhBMAMBvFGP06iI3tjtNeiwJXAMB0FKGARyJv7OOiEgByrggVQM1qOdjFxZ5KAMA0bRYg6fRH0tJRCQCYtnD0az5j4zIFrgCAGdksQOOHJK3RUSmJi0oAyL2wAih/e7gkqVQyLdXYNwIAmL7Do462vIKYtRzuqJSktWD0a7oJJwBgNta3/bwxnzsq65WSquVBhesd8kYAwJRs7Oa/uFUKOypvcVEJAMiroKMyx4HVH/9KRyUAYFpu7YXdiXlNOv0L1Nt0VAIApswvcL2U045KMwvGv27tp3sgCwCYrkKOfuWiEgCQV0FgzfGogqW6d1FJZSwAYEo2dwbJW71SUqNanuOnOd0FOioBADPkj369lOMD2SBvpMAVADAlXFQWCxeVAJBz/oEsHZUAgNQN7242f0lyjvgdlSmP8AEATF+n44L4mOcDWX9PJStDAADT0Gp3ghiT5/PUVS9vvJNw3shFJQDkXFFmqvuVsVtcVAIApmRzpxgj0f0dlbcY/QoAmKLbey21O+748aWlfO6olMKLSibxAACmYXMo/8rzhLpgR2XCk3i4qASAHHPOhQeyOQ6sK96uERJOAMC0+GNUL3jVp3njf7bhRBkAgCz5Y1+rZdOKdxmYN0t1r3OEHZUAgCnwp9MtVEtq1PK5LkRi9GsfF5UAkGNbB0c68ipji9JRuX2QbmAFAExXMPo1x3FxreF3VBIXAQDTs759cPznPI9FlxRcojL6FQAwDcF+yhw3fUjhReVeq63Do84cP838cFEJADnmd1OahcErb5YY4QMAmIGiJJ3+jsqt/SMdtdNMOAEA0+fHxjyPfZWG8kZWhgAApqAoxa1SmDdK6XZVclEJADnmJ5xrjarKpfxWxrKjEgAwC0HSmePRryScAIBZ8Ue/Xsr5gexy0FFJbAQAZM+Pi3meTidJjWpZ1fLgvPf2XpprQ7ioBIAcK1IF0DIdlQCAGQh2N+c4Ni7VK6p4BUabjH8FAEzJ+nZxDmSXFwaFPHRUAgCmYbNAF5Vmxp5KcVEJALm24S1/zvN4O2l4RyUJJwBgOvwLvws5jo1mprWmv6cyzcpYAMD0bewMdlReWspvbJSGJvFQ4CpJMrOGmb3TzJ40s30z+6KZfdDMvuQc73XBzH7SzD5vZge9r+81s7UzfuYhM/uZ3msPzWzLzP5fM/uHZpbv/0EBwAn8CXV5zhn7Vrio5KISAPKsKF0jUrhrhIQTADAtRZo24I9/vUVHJQBgSoo6+vUOeaPMbEHSb0p6u6QlSb8i6S8lvU7SE2b2wjHe67Kk35f0qKQjSb8saUvSmyV93MwunvAzXyXpCUnfI2mn9zP/QdJLJT0u6d+aWWX45wAgz/ycMe8dlVJ33VcfF5UZoAIIALK14QfWnFcA0VEJAJiFsDo2vzsqpfDzbdJRKYmcEQCmwR/9emmpPsdPcm/hypA0D2OH/LCkV0n6XUkPOee+0zn3iKS3SrpP0gfHeK/3SnqRpA9Lerj3Xi+T9D5JD6l78Tjs/ZKakt7mnHuJc+6/cs793d77fEbSayT9d+f7qwHAfGwUqPFDUjD6NdUC18wuKqkAAoDsFamjcpmOSgDAlLXanSDG5H2Mz2rDH/2aZsLpI2cEgOnYKNAuLn9HZep5Y69A5k29h290zm33n3POPS7pE5JeY2ZfOcJ7XZX0XZIOJb3BOef/h/sDkp6V9N1mdsX7mSVJXyFpV9J7/Pdzzn1R3UtMSfobY/7VAGCugriY85xREjsqlW1HJRVAAJCxMOHMd9fIUn3w+bYP0gyqJ6FzBACyM3zZl/fDWL+j8tYeHZUiZwSAqVj3d1TmPDYGHZVM4nm1pFVJTzvnnjjh+Q/1vr52hPf6enXPeT/mnLvuP+GcO5D0EUllSd/gPdWS1BnhvddHeA0A5MZmgQp4JGnNu0zlonICVAABwHQEe7hyXgHk76jcb3XUao+S78SNzhEAyNYtLy7WyiU1a+U5fpp786chbCbeUUnOCADT0em4IMbkffSrvzJk97Cto7Tzxi/rff3DU57vf//l03iv3gXmv1e3iOcf+S82s+dIeqO6l5n/5wi/HwBywTkX7G4uwkXlit9RmWjemFVHJRVAADAFRRrh4yeckrRDdaxE5wgAZCrcNVKVmc3x09zbKgmnj5wRAKbg9l5L7Y47fpz3vNEf/Sol31X5vN7XL5zyfP/7z5/ie32vusW07zazPzWzf2Vmvy7p05JM0jc6554c4fcDQC7sHrZ1cDT41/68x0WJ0a9SdheVVAABwBT4lbF531E5fFHJvhE6RwAga0WaNCCFn9H/7IkiZwSAKfDHvlbLppWFfA9MIW8MLPW+7p7y/E7v6/K03ss59+eSvlrd2PlXJX2HpL8raUHSb0n65Ai/+5iZffKkfyQ9OM77AMB5+cWtUriOI6+4qMzuopIKIADIWKfjghF3eT+QLZcsGMGXeGWsROcIAGQuKODJeVyUpDUvKU599KvIGQFgKta3wyk8eZ82UC6ZFr28MfGLyrkzs78t6Y8kVSX9bUkrkl4g6Z+ou7Lkd8zsvvl9QgAYj39RudqoqlLO6gpseta4qFRWZVa5qAAys6+W9G8kvULdKiBJchqzAqhX6XMSqn8AzMyd/Za8CT66WIAD2aV6RbuHbUlcVGr2nSOv11DniJn9e0l/S93OkR/vP0fnCICiKtJIdCm8qLxNR2VUOaNE3gggH8LYmO/9lH3LC1XtkDdKUn/qTvOU5xd7X7em8V5mdlHSL6l7Sfn3nHP/n/eat5vZqqT/UdL3S/rBET6DnHMvPen7vZj5klHeAwAm4cfFSwXIGSVp1csbbyV6UZn/6+QRUQEEIDZ+YC2XTMs5H+EjSUveZ9ymMpbOEQDImD9pYK0AI3zC0a9pJpx5Qs4IIEY3C3gg6+eNW/tJx8e/6H197inP97//+Sm91zdKuijp97xLSt8v9b7+5yP8fgDIhaIVt0qMfpWy66iMqgKI6h8AeRDu4aqqVMr3CB9JWvb2jWylXRkrRdY5QtcIgDxYL1jS6V+m7rXa2m+1tVAtn/ETUYsqZ5TIGwHkw4Y3+vXSUv5jo6SgCDfx0a9/1Pv6ilOe73//E1N6r/7l5e1Tfqb//Qsj/H4AyAX/ovJCAXJGKbyoPDzqJJk3ZtVRSQUQAGRsY6dYe7gkOirzhs4RALHZLNhF5XD8TrU6toecEQCmYGPn4PjPRYiNUndlSF/iBa6/o+5l4INm9uUnPP/tva8fGeG9fl1SR9LXmNkV/wkzq0t6raS2pF/1nrrW+/oVZnbSifjf6H393Ai/HwByYb2Akwb8i0opzbwxq4tKKoAAIGObBawA8hPO7YP0guqQvHWO/JZzbss59znn3NslfUDSl6rbOXJPzrmXnvSPpKdH+XkAyELRxvgsVMtaqA5Srs2091SSMwLAFBRx9OvKwuBANuXRr865Q0nv7z38gJn18zqZ2VskvVzSbzvn/sD7/pvM7FNm9u6h93pG0s9Lqkn6aTPzp+i9R9J9kn7OOXfD+/6vSzpQt5j1x8zs+F9azOxhSe/sPfzQZH9TAJidIp6nLlTLqlcGeeOtBNeGZHVRSQUQAGQsGG9XlI7K+iDhpKOSzhEAyFrRRr9K0lpj8DlTTDg95IwAMAXh6Nf6HD/J6JaZxON7l6SPS/oqSU+Z2S+a2e9J+glJz0p6/dDrL0t6WNLVE97r+9QtJP02SZ8ys18wsz+W9KikpyS9xX9x73Lz+9VdDfK23u//kJn9lrpFQc9RN5b+bAZ/TwCYiSJ2VErsqczkopIKIADIXjDCp4i7RtIe4SPROQIAmSva6Fcp3FN5K+GOSnJGAJiOdS9vLMqBbDD6NfGLSufcvqSvlfRjknYlfYuk56t7OfgK59xnxnivm5JeKel96sbIb5W0KumnJL3SObdxws+8X901Ib+s7gSfb1Y3v3xC0hslfZNzLu3/kgAUShFHoktcVFbu/ZKRvUvS12lQAfQxdQPrIzpfBdCrNKgA+o+SXirpZTqlAsjMvl/dwPs2Sd9pZk9IuiTpb0qqiwogAAVTxAqgYPRr4gmnhjpHnHP/aej5c3eO+Aevo3aOOOfaQ+9J5wiAQtlvtbVzOPi/sqIkneFFZXoJ5xByRgDI2HohOyoZ/epzzu1Jekfvn3u99jFJj53x/Ia6HZSPjvH7Pyrpo6O+HgDybNPLuYoy+lUK88YULyqzGv1KBRAAZKxoe7ikoRE+iXdU0jkCANka3u94oSBj0f3PuZn4RSU5IwBkq91xQXwsTIEreSMAYErWt4s3aUAKOypTnMSTZUclFUAAkKEiXlSScN6FzhEAyIjfMbJYK2uhetKawfxZa/o7KtNLOIeRMwJAdm7tHqrjBo8vFXBlyB0m8QAAMtJqd4K4UpTzVEla8S4q79BRCQDIi2CEz2IxRviwayRE5wgAZCco4CnIQazE6FcAwPT460JqlVKQj+XZygIrQwAA2RuewlOki0p2VAIAcmm9gMufGf16NzpHACAbftJ5sSAFPJJ0wbuoHE6cAQCYRFjcWpOZzfHTjG6p7u2oPEjvMBYAMB1+cetCtaRmrTjXX2uNwdlviheVdFQCQA7tHh5pv9U5flyUET5+wkllLAAgS/5h7EXv8i/v/ITzVoIJJwBgevzi1qLkjFJY4MokHgBAVjYKOJ2ub7UxiI0p5o1cVAJADvmHsZJ0oVmMpNMfNURHJQAgS0XtqAxHv9JRCQDIThHXhUjS0tDoV+fcGa8GAGA0G16+dWGxOMWtkrTaTHv0KxeVAJBD/qiClYWKapVi/N/18OjXToeEEwCQDX8P18UCJZ0XvPHtm+yoBABkyI+NRe2oPOq4YJoQAADntbFTzOJWidGvxTj5BoDEbAQJZ3ECq99RKUk7h3RVAgCysVnQpHOt4VXG7rboGgEAZGZ92xv9ulici8qVhbDgiD2VAIAsDO9uLpKVobwxNVxUAkAOhV0jxQmsi0MXlYx/BQBkpagdlWve+PbDdke7h+05fhoAQEyCA9kCFbjWKyVVSnb8mD2VAIAs+OtCirJGq2+1EY5+Ta3AlYtKAMihjZ1BZWyRLiprlZLq3pjabRJOAEBGijrGx084pTB5BgBgEus7xeyoNLNg/CsXlQCALBR1JLoU5o1HHZdcgSsXlQCQQ0FgLVDCKYX7Ru6QcAIAMrJZ0GkDtUopGI1+K8ExPgCA6SjygeySlzdS4AoAyMLGdjFzRunuAtfU9lRyUQkAOVTkwOofxjL6FQCQhU7HBZ2IRYuNa81B0slFJQAgK+EuruJMG5Ck5fogNm7tExsBAJMr8ujXWqWkZq18/Di1vJGLSgDIoaLuqJSojAUAZO/2Xksdb0VH0WJjcFG5x+hXAMDkWu1O0G1RtI7KYPQrBa4AgAwUedKAdPeeypRwUQkAOVTkwBp2VKYVVAEA0+HHxUrJtOIdbhaBX827mVhlLABgOvyR6FIBOyrZUQkAyJBzrrDrQvq4qAQA5MrGzsHxn4uWcC4FI3xIOAEAkwtG+CzWZGZz/DTjW/MuKm/t0FEJAJjcTW/sa7NWVsMbF1cEywuMfgUAZOfO/pGOvDE8Fws2+lUKLyrvcFEJAJi3Iu+o9Ctj2VEJAMhCuIOrWHFRktYa/ujXtBJOAMB0rPvFrQWbwiMNTeKhwBUAMKENryC0XLLg0q8oVhvprgzhohIAcma/1dbOYfv4cdGSThJOAEDW/KTzQgErYy94Oyr97lAAAM5rIxhvV6wpPBKjXwEA2fKn011oVlUqFWu/SWAOAAAgAElEQVQKj8ToVwBAjmwMjYQrWkflEh2VAICM+Zd7FwtWwCNJq97l6m12VAIAMuCPfr1csJxRGhr9ekBsBABMZmNnEEuKWNwqcVEJAMgR/6JyqV5RvVKsXSN+R+UWF5UAgAz4o1+LuGuEjkoAQNbWtws++pWOSgBAhvyOyqI1ffStNf2LyrRiIxeVAJAz6zvF3U8pDe2oJOEEAGQg6KgsYGz0K3pv0VEJAMiAX+B6aal4o19XuKgEAGRoPYiLxcsZpaEdlYkVuHJRCQA5U/QKoGBHJR2VAIAMFL2IZ9WrjL2V2AgfAMB0+KNfLxUwNpI3AgCytOnljEUd/briXVTeSSxv5KISAHJmPaaEk8pYAEAGil7EE3ZUHqrTcXP8NACAGKzvFHv0a7Cjcj+tw1gAQPaCjsoC5owSOyoBADlS9FEF/q4RKmMBAFnY3BkkaUVMOte8hLPj2OEMAJhcWOBavNGvy4x+BQBkaKPgU3gkac0rcOWiEgAwVxvbfmAtXsK5QmUsACBjftfIhQImnSuNqswGj1PbNwIAyF7RD2T9STy7h221mTYAAJhAMPq1gHFRurujMqVJPFxUAkDOFH1UwfCuEefSCaoAgOztHba13+ocPy5ibCyXLEg6N3cp5AEAnN9+qx1Mr7m8VOwCV4m1IQCAyYTnqcWLi1J4Udlx0vZhOrGRi0oAyJmi7+HyR792nLTXas/x0wAAis7vppTCcThF4o9/paMSADAJ/zBWKmbeuFgvB4+3DijiAQCcX9EnDUjSinemKkm3Eypw5aISAHImCKxF3FFZD4MqlbEAgEn4+ymXFyqqVYqZwvgXrLcSSjgBANnz14UUNTZWyiU1a4PLSvZUAgDOa7/V1u7hoFGiqBeVlXJJy965akp7Kov3bzIAELmij36tV0qqlgeLuLYOSDgBAOe3XvBJA31rTToqAQDZuOnFxiKOfe1b9jpHuKgEAJzXxtCkgQuL1VNemX8rQ3sqU8FFJQDkyOFRJ0jQingga2bhnkoSTgDABGIY4SNJF7yOSnZUAgAmsb5d7OLWviBvZPQrAOCc/JxxqV5RvVI+49X5tspFJQBg3jaHOiyKuvzZ31O5TUclAGACGwWfNNBHRyUAICsbXkflpQKuC+lbXhjERjoqAQDnFUtxqxTmjVxUAgDm4ub2IOFsVMtq1IpZAbRUJ+EEAGTDTzr9rsSiWWt4OyoTSjgBANnzOyovFrS4VWL0KwAgGzFdVPodlbcSmsTDRSUA5EjQNVLkytg6HZUAgGz40wYuFjg2+ntSGP0KAJjETe+i8nKBYyMXlQCALKxHelFJRyUAYC5iGW8XjH7dTyeoAgCyF3SNFLmj0vvstxn9CgCYwLo/+rXAeeNyMImHvBEAcD6bXFQWHheVAJAj4Qif4gbWJToqAQAZiWWMz1qDjkoAQDaC2LhU3NGvQYEreSMA4JzWI2n8kKRVb0flHS4qAQDzEB7GxpFwbpFwAgAmsLEbx1h0f7/mJh2VAIAJ+AWulwt8IMvoVwBAFja8SQMXChwXpaEdlXvp5I1cVAJAjqzHuKOShBMAMAG/iOdCoUe/+uPtjnTU7szx0wAAiso5p5vb3ujXIndU1rmoBABMbnNn0HlY5Ck8EqNfAQA54FcAFTmwknACALJw1O4EydmlAk8b8C8qpbSSTgBAdnYP2zo4GhS7FDlvXFlgRyUAYHKx7G6WuKgEAORALHu42DUCAMjCrb2WnBs8vrBYPf3FObdUr6hSsuPH7KkEAJyHP/bVTLrQLG5sZPQrACALwRSeAp+nStJaY/D5byeUM3JRCQA54iedRa4AWmL0KwAgA5tewlkrl4L4UjRmFnRV3k5o3wgAIDs3/T1czZoq5eIe7VHg2mVmDTN7p5k9aWb7ZvZFM/ugmX3JOd7rgpn9pJl93swOel/fa2Zr9/i5JTP7ETP7hJltm9ltM/sTM/uAmS2d/28HANPV7jjdCqbwFPc8VQo7Ku/sH6ndcWe8Oh7F/bcZAIjQeiQdlUFlbMIJJwBgMutBZWxVZnbGq/Nvzdux6e9RAQBgVBvbceSMkrTM6FeZ2YKk35T0dklLkn5F0l9Kep2kJ8zshWO812VJvy/pUUlHkn5Z0pakN0v6uJldPOXnXiDpE5Iek7Qo6dck/bakqqQ3SDrzkhMA5unW7mEwhafosdG/qJTSiY+ZXVRS/QMAk2kN7eG6vFTcPVxL9UFQ3T5II6ACALIXjkQvblzsW/OSzlsJ7RvxkTcCwGRi2sMVTOI5OJJzaXSNDPlhSa+S9LuSHnLOfadz7hFJb5V0n6QPjvFe75X0IkkflvRw771eJul9kh6S9PjwD5hZXd2LyedJ+l7n3IPOue9wzn2Tc+5hSX9N0sb5/3oAMF1+zlgtW6Gn8Ejd5g+/PjeVPZWZXFRS/QMAk9vcDUfAFbkCKBjhk/DoVw5jAWAyftJZ9MNYKeyovLWb3uhX8kYAmNxNr6OyyMWtkrTi5Y2tttPBUWeOn2b2zKwm6U29h290zm33n3POPa5uvHqNmX3lCO91VdJ3STqU9AbnnJ+I/4CkZyV9t5ldGfrRN0t6WNLjzrl/Pvy+zrk/cc7tjvHXAoCZ2hiaTlf0KTylkmllwV8ZwkXlOKj+AYAJ+YG1XimpWSvP8dNMhspYDmMBIAsbwejX4l9UXvB2VA4XKCWCvBEAJhQU8SwVOzb6o18l6U4i4+08r5a0Kulp59wTJzz/od7X147wXl+v7jnvx5xz1/0nnHMHkj4iqSzpG4Z+7n/ofX3fqB8aAPIktik8Ujj+9dZuGrFx4otKqn8AIBv+rpFLBa8AWk68MraHw1gAmFB8HZXpJZx95I0AkI317cHo1yJP4ZGkhWpJ5dIg701wGs+X9b7+4SnP97//8mm8l5n9FXXzzC845/7SzF5tZv+Lmf0zM/tBM3vRCL8XAOZqPbiorJ7xyuLwLyrpqBwd1T8AkIEgsBa8MnZ4Hvz2QVoJJ4exAJCNoKOyWezYKA2Pfk0j4fSQNwJABtaDjspid46YWVDkupXeReXzel+/cMrz/e8/f0rv9ZLe1y+a2Qck/T+S/pGkfyDpxyX9mZm9dYTfDQBzsxlhR6Vf4MpF5eio/gGADMQ0qqBZK8srjE2xMpbDWADIgD8etehFPNJQR+VecqNfyRsBIAPBjsqCd1RKd68NScxS7+tpBaQ7va/LU3qvC72vr5D0vequDPkrkq5K+sHec//UzL5xhN8vSTKzT570j6QHR30PABjHemRTeCRpJcGOysq9X3JPeav+ecPQz7zLzP6xc+4nRvj9krpB9ZSnCKoApiamwGpmWqpXdKd3QZlgwjnrw9jX6x6HsZK+Sd3L089K+tfOuU+P8LsBYK7Wt+OJjVLYFZpgR2V0eSMAzMPGTjyjX6X+nso9SdJWejsq563fwFKR9L85537Ue+49ZnZZ3Sk+PyTp/5r1hwOAUcQ2hUdKc/RrFheVear++evqVv/8H5KOJP33kv6JutU/n3LOEVQB5FZMu0akbsLZv6hkhM9dOIwFgBHElnQuel0ju4ftOX6SuYgub6TAFcCsOefCIp6Cj36VFIx+vZNe3thfEdI85fnF3tetKb3XtvfnnznhZ35G3YvKR8xswTm3f68P4Zx76Unf78XMl5z0HABMIrYpPJK0sjC4qLzDRWVhZF79Q1AFMA8bwa6R4gdWRvhI4jAWAM7NOaeN3bhiY6NaPv7z7mFysXHe6BoBUHh39o501HHHjy9HEBuX/bwxvYvKv+h9fe4pz/e///kpvZf/58+d8DP975UlXZT0xRE+BwDMVGxTeCRpsebnjWkUuGaxo7II1T9Sr/pnhM8AAHMR0+hXSVpa8C8q06j+yRH/MPafO+d+1Dn3BefcNefceyT9r73nf2g+Hw8A7m3nsK3Do87x4xg6KpsJJpye6PJG59xLT/pH0tOj/DwAjGvdG/taLlnQcVFUfkdlgpN4/qj39RWnPN///iem9F6fktTvkrygu130/rx9wvMAMHexTeGRpIaXN+610sgbs7ioLFr1DwDkkh9YLy4Wf4TPUtqVsRzGAsCENr24KEkXmsU/jA0SzvQuKskbAWBC60HOWFOpZHP8NNlIvMD1dyTdlvSgmX35Cc9/e+/rR0Z4r1+X1JH0NWZ2xX/CzOqSXiupLelX+993zh1I+o3ew791wnu+pvf1M865OyN8BgCYqRin8EhSszaIjankjVlcVFL9AwAZ2BhKOovOTzi30hv9ymEsAEzIP4xdbVRVKWeRusyX31F51HFBx2gCyBsBYELr24OOyhim8EjSstcVmlpHpXPuUNL7ew8/YGb9IlSZ2VskvVzSbzvn/sD7/pvM7FNm9u6h93pG0s9Lqkn6aTPz1329R9J9kn7OOXdj6GO8p/f17Wb2kPd7XiDpx3oP/9l5/44AME3DU3hiOE+VhifxpBEbs8j2qf4BgAm1Oy5Y/hxD0pn4rhEOYwFgQhs78R3GNquV4HEq1bE95I0AMCG/iOfyUvGn8EjJj36VpHdJ+rikr5L0lJn9opn9nqSfkPSspNcPvf6ypIclXT3hvb5P3Yk33ybpU2b2C2b2x5IelfSUpLcM/4Bz7j9Ieqe6BbBPmNm/NbNfUzcPfaGkX5P0+MR/SwCYgo3tcArPWqP4U3gkaaGa3sqQiS8qqf4BgMnd2j2Uc4PHFyMYVRCMfk2vo5LDWACY0MbOYPxbLJWx/uhXSdptpRMfyRsBYHLr23FN4ZHCAtcEJ/HIObcv6WvVjUO7kr5F0vMl/aykVzjnPjPGe92U9EpJ71M3Rn6rpFVJPyXplc65jVN+7kfUvdz8A0mvUjdffFrSP5T0Tc65NE7JARSOP/Z1rRnHFB4p7KhMZUdl5d4vGcm7JH2dBtU/H1M3qD6i81X/vEqD6p//KOmlkl6mM6p/zOydkt6hbvXP76h7aPtqScui+gdAzvljX6tlC5K1ogp2jSRWGeucOzSz90v6n9Q9jP07zrkd6ezDWElvkvRvnHNv897rGTP7eUn/rbqHsf+1c67/H2j/MPZfnnIY+83qHsZ+1Dn3ZO/3cBgLoBD8jsoLkRzG1iolVUqmo063OimxjkqJvBEAJhKMfo2guFWSGsEerrTyxj7n3J66sekdI7z2MUmPnfH8hrodlI+O+Rk+LOnD4/wMAMybnzPGUsAjDY9+TSNnzOSKmeofAJjM+tB+SjOb46fJxlLilbFihA8ATMTvqIxl9KsUdlWmknT2kTcCwGRiHP2a4mEsACAbwaSBZpw5YyrFrZm17FD9AwDnF47wiSPhXE64o1LqHsaa2ddKepuk/0bdw9gNdQ9j3+6c+8IY73XTzF6pbuz8FnUPY6+rexj7I865W6f83I+Y2R9p0HVSkfTnkv6lpPdzGAsgz2LsqJSkRrV8vIMrlTE+PvJGADi/GEe/NhM8jAUAZGNzN764KElNb9rA7uGRnHNRNLWcpfizBQEgAv5h7OVIRvgs1QcLrBPcUSmJw1gAmESsHZV0jgAAzmvdyxtjiY3+YexOoqNfAQDnMzyhLhZ+zthx0sFRRwvV8hk/UXxxbBcFgIKLMbAGOyoTvagEAJxfrPtG2MUFADgvv6PyEqNfAQCJ24hw0oAUjn6V0pg4wEUlAOTARowXlf6Oyv3WGa8EAOBufmyMafQrB7IAgPNod1ww4i6ejspw9Ktzbo6fBgBQJNGOfh3qntxNYGUIF5UAkAN+R2UsCad/UblzEH9ABQBkayPC2ChxUQkAOJ9bu4fqeHd4lyJZGeKPfj3qOB22O3P8NACAIolxQp0kVcol1cqDq7sUJvFwUQkAORCOKohvhM9eq61Oh8pYAMBoWu2O7uwPkrELzXiSzkY17BwBAGAU/mFsrVIKCkOLLMXxdgCAbMQ4oa6vkViBKxeVAJADMQbW5nDCmcCYAgBANvwRPlI8XSPS3YU8AACMwt9PeXmxJjOb46fJznDemMJhLAAgG+EUnjgaP/qGR6PHjotKAMiBYPRrJIexi0MVvjsJjCkAAGTDTzjrlVLQhVh0DW/EHYexAIBRre8cHP/5YiQ5oyRVh8bb7ZI3AgBGcHjU0ZY/hWexOsdPk72gozKBAlcuKgFgzjodF+Xy53qlpJJX5LvLnkoAwIj8keiXIuoakYZHv3IYCwAYzfp2xF0j9bTG2wEAJnfXFJ7YYiMdlQCAWbq911Lb2994KZKLSjPTotc1QkclAGBUG34BT0RdI1KYcHIYCwAYVYxTePqaVWIjAGA8/hSeRrV8187jomskFhu5qASAOfMTzkrJtLIQz6gCKmMBAOfhJ50XmnEdxqY2wgcAkI317cHo11iKW/uC2EiBKwBgBH7OGMt0Op+/MiSFSTxcVALAnAWHsYs1lUrxjLcLOioP4g+qAIBs+LExtsPY1Eb4AACyEYx+XYprvN1inf3NAIDxrEd+UZnatAEuKgFgzjZ20qiM5TAWADCq4SKemDTpGgEAnEPMRTypjbcDAExuM/aLysRWhnBRCQBzFnMFULijMv6gCgDIRtSHscEIH2IjAGA0N/0C19h2VPqHsUziAQCMIObzVGmo+SOBlSFcVALAnG1sxxtYwx2VJJwAgNFE3VFZTSvhBABkIxj9uhjX6NemP/qV2AgAGIE/oS6281QpvUk8XFQCwJytR9w1Eu6oJOEEAIwm5o7K1Eb4AAAm12p3dHuvdfw4uo7KKitDAADj2Yi+ozKt/c1cVALAnIWBNbLK2MSqfwAA2Yg5Ni6wvxkAMCZ/D5cUYUelFxspcAUAjOLGnUFH5X3LccVFKYyNKeSNXFQCwJyt+6MKIquMXazTUQkAGI9zTpu7/kVldY6fJnt0VAIAxnXTG/varJWDvVUx8Ee/7rUocAUA3Nv1rf3jP9+/sjDHTzIdqeWNXFQCwJz5u0YuRzaqIFz8TMIJALi3rYMjtdru+HFsHZXNqn8Y21an4854NQAAYXFrbGNfpXD0awqHsQCAyTjndN3rqLx/Ja6cUZIaiY1F56ISAOYs5pnqi4zwAQCMacMr4DGTVhtxdVQOd8HsHxEfAQBnC3c3R3gYS94IABjDnb0jHR51jh/fvxxjR6W3ozKB5g8uKgFgjobH28VWHRsEVXZUAgBGsO4dxl5o1lQu2Rw/TfaaQxeVdI4AAO7FH/16KbLiVilcGcIkHgDAvfhjX2vlktaacRW3SuyoBADM0J39uMfbLdapjAUAjGcz4kkDUjjCR0oj6QQATGZ9O/LRr4nt4QIATOb6ncFF5ZWVusziKm6VhtZpJRAbuagEgDnyR/iUTFqLbLwdHZUAgHEFI9Gb8R3GlkqmheogDeNAFgBwLze2BheVl5fiKm6VwiKeXQpcAQD34O+nvLIcX1yUhmJjK/7YyEUlAMzRxs4gsF5o1lSKbLxd0FHJQSwAYAQ3vdgYY0elFBby7CWQdAIAJuN3jjywGt8eLn/0awp7uAAAk/Hj4v0r8cVFKb1pA1xUAsAcrW/HPt7OO4hNIKgCACZ3w6uOvX8lgepYJg4AAO7h2u24D2RTG28HAJjMjQQuKv3YeHjUUbvjznh18XFRCQBzFPsIn7CjkoNYAMC9hftG4k86OZAFANzLNb+jMsLY6HeN7DD6FQBwD/556pVIi1v9KTxS/AWuXFQCwBz5lbFXIxzhE+yoJOEEAIwg9sNYKb0xPgCA89s9PNLW/uBwMsrRr0Mj0TuRd40AACYTjH5dji8uSmHOKMVf4MpFJQDM0bXod414YwraHR0edeb4aQAARRCOfo0vNkrh6NfYE04AwGSue3GxZNKlGFeGDB3G7h8RGwEAp7ueQM5Yr5RkNngce4ErF5UAMEd+R2WMF5XDYwo4jAUAnKXTcUF17AOrsY7xYUclAGA0fs5433JdlXJ8R3nDXSOMfwUAnMY5pxtb/rqQOHNGM1Ozms4knvj+7QYACiT28XZ3JZwcxgIAzrCxe6gjb9xbrDsqg9HorbgTTgDAZK5HnjNK0kKlHHSNUOAKADjN5m5LrfYgZ4x19KskNYLR6HGfqXJRCQBzFHtHZbVcUq0yCDWxV/8AACbjx8VmrazleuWMVxeXP+KOw1gAwFn84tZYx9uVShaMRd+N/DAWAHB+fgFPvVLSSiPOnFEansQTd97IRSUAzMnWfkvbB4MELMaLSonxdgCA0fkjfO5fWZD57RURaXJRCQAYUezFrX1+bGT0KwDgNDe2wv2UseaMEheVAIAZ8CuAKiXT5cU4Z6ovemMKSDgBAGe5dttPOuOMi5KGukaIjQCA011PoKNSCseiU8QDADhNGBfjzRmltCbxcFEJAHMSHsYuqFSKswKIjkoAwKhSOYxNKeEEAEzmWgI7KiXyRgDAaG54cfFKxHFRGprEE3mBKxeVADAnz9zeO/5zzBVATW+/2A6HsQCAM1znMBYAgMCNO4MC15hHvzYSGm8HADi/615cvH853rgoSY3q4Ew19tjIRSUAzIl/GHt1tTHHTzJdi37CecBhLADgdNcTqY5t1NJJOAEA59fpuGRG3C0SGwEAIwhzxnjjojTUURl5gSsXlQAwJ8/cTmO8XZOEEwAwomt+10jMsbHK6FcAwL2t7xzqqOOOH8ecNzaYNgAAGMH1LX+VVtwXlY1qOtMGuKgEgDm5dtvvqIw34WS8HQBgVDcS6RppMt4OADACv2tksVbW8kJ1jp9mulKOjWbWMLN3mtmTZrZvZl80sw+a2Zec470umNlPmtnnzeyg9/W9ZrY24s/XzOxPzcyZGQk8gNwJcsbYR78mFBszu6gkqALAeK75gTXii8rF+iCosqMSAHCaw6OO1ncOjx+n0jWy10orNpI3AsDo/OLWmHNGKd1JPGa2IOk3Jb1d0pKkX5H0l5JeJ+kJM3vhGO91WdLvS3pU0pGkX5a0JenNkj5uZhdHeJsfkvSfjfN3AIBZ6XScnvU6KmNeFyINj36NOzZmclFJUAWA8aXTUeklnIntqOQwFgBGd2NrP3gc874RPzbGnnD6yBsBYDx+cWvMI9GlpCfx/LCkV0n6XUkPOee+0zn3iKS3SrpP0gfHeK/3SnqRpA9Lerj3Xi+T9D5JD0l6/KwfNrO/Kultkv73sf8WADADG7vDI9HjzRmlodgYeYFrVh2VBFUAGMPBUTvoGok56VyspdlRyWEsAIznuref8uJiTfVK+YxXFxuHseSNADCK68leVKaRN5pZTdKbeg/f6Jzb7j/nnHtc0ickvcbMvnKE97oq6bskHUp6g3PO/xeMH5D0rKTvNrMrp/y8SfoXkm5J+sfn+OsAwNT5cbFZK2upXjnj1cXXCApc484bJ76oJKgCwPhueIexUuRdI3V/hE/cQXUIh7EAMAY/6byyHG9clKSFanqjX8kbAWB8qY5+TWjawKslrUp62jn3xAnPf6j39bUjvNfXq3vO+zHn3HX/CefcgaSPSCpL+oZTfv4fSPpqSW91zm2O8PsAYOb889Qry3V1/7U+XikV8WTRUUlQBYAx+SN8Li/F3TWymFBQ7eMwFgDGF3SNRH8YO4iNrbZTq92Z46eZGfJGABjTdW8PV0odlTvpFLh+We/rH57yfP/7L5/me/Vyzh+X9O+ccz83wu8CgLkIilsjj4sSF5XjIqgCwJie8StjIw+sjWBHZdxB1cNhLACMyS/iuX857tjoJ5xS/ElnD3kjAIzpepA3xj1toJHQYazneb2vXzjl+f73nz/l93q/pAVJbxjh9wDA3PjrQmI/T5Wkhj+JJ/LYmMUQX4IqAIzJTzivRt41skhl7EmyPox9/UnvxWEsgCLxx/jEPt6uMXRRuXfY1mqjOqdPMzPkjQAwpqCIJ/ID2cU0R78u9b7unvL8Tu/r8rTey8y+WdJ/KelHnXNPjvB7zmRmnzzlqQcnfW8AuLHlF7fGXcAjhWPRd1txn6lmcVFJUAWAMaXUURnuqEwm4eQwFgDGdC2hrpFauaRyydTuOEnJ7HAmbwSAMey32rq91zp+nNJY9IQKXOfKzJbVzRmflPTuOX8cALin5Doqa3RUFgZBFUAR+TPVk+qoPEgm4eQwFgDGdN2rjo19D5eZqVkta6sXFxMq5Jkb8kYAReMX8JRMum8p7iKelA5jPdu9r81Tnl/sfd2a0nv9z5KeK+nremtFJuace+lJ3+/lky/J4ncASJffUXkl8uJWKSziiT02ZnFRSVAFgDE9c3vv+M+xVwAFYwoiD6p5wWEsgCK6ntC0Aal7INu/qNxvJREfyRsBYAz+2NfLS3VVyqU5fprpW0wzb/yL3tfnnvJ8//ufn9J7vVbSvqS3m9nbT/iZspl9tPfn73PO/acRPgcATI3f+HFlOf6c0b+o3G215ZyTmc3xE01PFheVBFUAGJM/quDqamOOn2T6FuteUD08ijqoejiMBYAxbB8cacc7lEzhojJIOtM4kCVvBIAx+IexsY99lcKOykRGokvSH/W+vuKU5/vf/8QU32tB0mvOeN/+c2sjfAYAmJp2x+nZLX/0a/wdlX5sdE46OOpooVo+4yeKK4uLSoIqAIyh03FDSWfcgdUPqp3Ig6qHw1gAGIM/3q5SMl1arM3x08yGHwsTuagkbwSAMVxLbNKAX+Daaju12h1VI+8ilfQ7km5LetDMvvyEvOzbe18/MsJ7/bqkjqSvMbMrzrkb/SfMrK5ujtiW9Kv97zvnvvS0NzMzJ6ntnCv82jAAcVjfPlDHDR5fSSA2NobOT3cP29GeqWYR8YOgesLz5w6q/hNnBVXnnJ30T+8lbe97Hx3vrwYA2bu5c6AjL7I+EHtHZS3MaxLZU5mnw9jhf/r6jzmMBTB3N4IRPnWVStF33of7RlpJxEbyRgAYgz+FJ4WukWY1zBtTKOJxzh2qu7JDkj5gZv1pOTKzt0h6uaTfds79gff9N5nZp8zs3UPv9Yykn5dUk/TTZub/B/oeSfdJ+jn/AhMAiuSG1025VK9oqR5/HUWzNhwb480bJ76oJHiCD6MAACAASURBVKgCwHj8ytgUAutJ1T8J4DAWAMbg7+FKoTJWSm+HM3kjAIwnmMKTQGz0J/FIcR/GDnmXpI9L+ipJT5nZL5rZ70n6CUnPSnr90OsvS3pY0tUT3uv7JD0t6dskfcrMfsHM/ljSo5KekvSW6fwVAGD6gv2UCRTwSFK5ZKpVBld4exHnjVnNUCCoAsCI/IvKFHaNlEoWdI3sJJBwchgLAOPxu0ZSOIyVwgPZmBPOIeSNADAiv4gnhdGvtUpJ1fJgokIKRTyS5Jzbl/S1kn5M0q6kb5H0fEk/K+kVzrnPjPFeNyW9UtL71M0fv1XSqqSfkvRK59xGph8eAGYomDSwHH9c7GvW0lgZkslFJUEVAEZ3LbHKWCnsGtk5iDeoDuEwFgBGdD04jE2jOjaVhNNH3ggAo0utwFUKp/EkVMQj59yec+4dzrkXOefqzrmrzrnXOee+cMJrH+tNxvmeU95rwzn3qHPueb33ep5z7s3OuVtjfiZjPyWAPEmxo1KSmtU08sbMtlITVAFgNCkmnIv19BJODmMBYHTBRWUisTHFi0qJvBEARtHpON3YSr3ANf5JPACA0flxMYVJA33BJJ5WvLGRZAwAZiy4qEwksPqVsSmMfu1zzu1Jekfvn3u99jFJj53x/Ia6HZSPTviZ7N6vAoDZCsbbJTLGp1EdpGL7rXQuKgEA97axe6hW2x0/TqaIxytw3SU2AgA8/ujXK8sJdVR6RTwxF7hm1lEJABhNMPo1kYRzse4H1XQuKgEAo7nh76hMJDaGHZXERgDAgF/c2qyVtVxPo8+gmeb+ZgDACOio5KISAJChFDsq/YQzoR2VAIARdDouyR2VqSScAIDxDY99NUtjKEqzyuhXAMDJ/I7KlC4qUyni4aISAGbIOZdmR2WNjkoAwMk2dg911BmMt7uSSNLpj0WPOeEEAIzv2m1vvF0iBTxSOPp1j9GvAICeo3ZHN7f9i8qEYmMiBa5cVALADN3ZPwqCSioXlX7CSUclAMDHeLu4E04AwPiC4tZECngkYiMA4GQ3tw/lBrWturKcTmxseNMGYi7i4aISAGbIH21XK5d0sVmb46eZHb+jMuagCgAY3/CukVTG2wWjX4mNAADPda+I5/5Eilul8DB2l9GvAIAe/zx1eaES5FKxC0e/xhsbuagEgBl6xks4r6zUVSqlcRgb7qiMN6gCAMbnj7dLa4SPV8QTccIJABhfqh2Vi3U6KgEAd/MvKlPaTymlM22Ai0oAmCG/MvZqQpWxzWBHZbxBFQAwvlSTzlQSTgDA+K4nelHJtAEAwElubKVZ3CpJC1W/ozLe2MhFJQDMkN9RmdJh7GKdjkoAwMk4jJX2OYwFAHj8jsqURr82Gf0KADjBDT8uJrSfUkqnwJWLSgCYIT/hpKMSAIDwovJKQheVqSScAIDx7LfaurXbOn6cUhEPo18BACe5fmfQUZlSzigN5Y0RF7hyUQkAM3Tt9t7xn5PtqGQPFwDAc81LOlM6jG34I3xabTnn5vhpAAB5ccOLi2bSfcvpjLjzpw3sRXwYCwAYz/Utf0JdOnFRkhpe88dexGeqXFQCwAz5h7FXVxtz/CSz1QyCKgknAGAgGOOTUNLpH8Y6J+23OnP8NACAvPCn8FxarKtaTufozu8aYWUIAKAv6Khk9GuU0vm3HQDIAb+j8oHVdA5jg4Qz4uofAMB4Do7aWt85PH6c0rQBv4hHknaJjwAAhReVKeWMEitDAAAnS7W4VRqaNhBxbOSiEgBmZL/V1qa/aySpjkqv+ucg3qAKABjPs1sHweMrCSWd/uhXiQNZAEDX9dveRWVCBTxSmDcy+hUAIEmtdifZ4lZJalbpqAQAZOi6V/1jJl1JaNfIYn1QGUtHJQCgz4+NFxdrqlfKZ7w6LuWSqV4ZpGMcyAIApLCjMrnD2GD0K3ERAHB3cWtKu5ul4WkD8Z6pclEJADNyzauMvbyU7q6R/VZH7Y6b46cBAORFuGskrYRTSmffCABgdMHo1+QuKgeHsXsRH8YCAEbnF7euNataqKZT3CoNjX6NuLg1nVNyAJgzP+G8uppWwrnIHi4AwAn8Ip4HEouN0vCBbLxJJwBgdP7o1/sTi41BAU+rLecocAWA1PnFrfcvpxUXpTA2ttpOrXZnjp9merioBIAZ8Q9jkxvhU2cPFwDgbte3vNiYYNIZVsdSxAMASLuj0o+LznWn8QAA0nbDyxmvrKQ9hUeK90yVi0oAmJFnbqfbUVkrl1Qp2fHjWIMqAGA8KXeNSFKjyuhXAMCAc043/M6RxC4qmcQDABjmj369knhxqyTtRzr+lYtKAJgRP7CmlnCaWRBYdw5IOAEAQ2N8EqyObbCjEgDg2dxt6dAb6ZZcR2U1ja4RAMDobiSeM9bKJZUTaP7gohIAZiTljkoprI6NNagCAMZzPeHxdlI4xocdlQAAf13IQrWklUbljFfHp1QyLVQHR5XkjQCA61vpThqQus0fzWAST5zNH1xUAsCMJH8Y6+2p3Ik0qAIAxpPytAEpvKjkMBYAMJwzmtkZr45TWOBK3ggAqbsR5IzpdVRK0kICBa5cVALADLQ7Tje8CqAHUu+oPIgzqAIARrd9cKQdL8lK8aKyUR3Exj0OYwEgedcSL+CRwrHosR7GAgBGF+yoTDQ2plDgykUlAMzAze0DtTvu+HGKF5V+UKWjEgDgj7erlEyXFmtz/DTzkULCCQAYnR8bU8wZpeG8kdgIACk7OGprc7d1/PjKcpodlY1q/HkjF5UAMAP+fsqVhYqatbR2jUjSYt3vGokzqAIARueP8LmyXFeplN54u2BHZYvYCACpS31diKQgV2b0KwCk7cadg+DxfYleVIZ5Y5yxkYtKAJgBKmPDET50VAIArjHCh/F2AIAAo1+HDmOJjQCQtBtbg7h4cbGmeqV8xqvjFRbxxBkbuagEgBm4dnvv+M8PrDbm+EnmZ9Efb8eOSgBI3nWvOjbdrpH4R/gAAEbnx0YuKhn9CgCp8zsqUx37KqVR4MpFJQDMwDPBCJ80A6tf/UNHJQDgetA1kmZsDHaNMPoVAJIXjH5dTTM2+nnjHnkjACTtOpMGJKVR4MpFJQDMwPVg9GuiHZV1OioBAANB0pnsWHQOYwEAXQdHbW3sHB4/TvVANoXDWADAaK4FkwbSLOCR0oiNXFQCwAw8419UJptw0lEJABgI9nAtpxob4084AQCj8cfbSdKVRGNjg9gIAOj5/PrO8Z+fs5Zm44ckNarxF7hyUQkAM+B3jVxNtGtkMYF56gCA0fkHsg8kGhtT2DUCABiNX8BzeammWiXNI7tFr8B1N9LDWADAaD7z7OCi8sH7lub4SeYrhQLXNP+tBwBmyDkXdFSmO8KHjkoAQFen49hRKalZjT/hBACM5ho5oyQ6KgEAXe2O02e9jsoX3rc4x08zX0GBayvO2MhFJQBM2e29lg6OOsePU+2obNZJOAEAXRu7hzrquOPHqR7I+kU8sSacAIDRXGNdiKQ0ukYAAPf2xVt7OvTOU19wOd2LymYCk3i4qASAKfO7KWuVktaa1Tl+mvnxR/jsHNBRCQAp8w9jm7WyluqVM14dL0a/AgD6nn52+/jPz7vUnOMnmS9GvwIApDAuPmd1ISjyTE0KRTxcVALAlH3u5mBMwXPXGjKzOX6a+UkhqAIARuOPfX1gZYHYKOmw3dFRu3PGqwEAMXvqxuBA9sVXluf4SeaLIh4AgBTup3xhwvspJWnBXxkS6SQeLioBYMqevO4lnPenG1gX63RUAgC6Pr++e/zn56w15vhJ5qvhJZxSvEknAOBszjl92ruofNGVlPPGQWzc4aISAJLld1SmvJ9SGloZEum0AS4qAWDKnryxdfznh+5PtzK2mcDiZwDAaJ7iMFZS2DUi0TkCAKm6uX2o23ut48dJx8aqfxhLXASAVAUdlQnvp5TSmFLHRSUATNlT1wcXlS9O+qJykHC22i5YiA0ASMvTXFRKkuqVkkre1NtYk04AwNme8opbLy3WdHGxNsdPM1/hYWycXSMAgHv7zM1BzvhgwjmjlMZY9MwuKs2sYWbvNLMnzWzfzL5oZh80sy85x3tdMLOfNLPPm9lB7+t7zWzthNdWzezvmNn7zexPzGzXzPbM7M/M7J+a2X3Z/A0BYHytdkef9XZUPpTw6NdmfWi8HUknACTLP5BN+aLSzIJCnhRiI3kjANzNL+BJ/TA2xdGvxEYACG0fHOn6nYPjx6nvqKSjckRmtiDpNyW9XdKSpF+R9JeSXifpCTN74RjvdVnS70t6VNKRpF+WtCXpzZI+bmYXh37kNZJ+Q9IbJS1K+jVJ/7eky5LeKukTZvbwuf9yADCBz93cUavtJEnlkukFCY8qaA7t4Uoh6SThBIC7rW8faHOX8XZ9KVTH9pE3AsDJ2E850PAKeA6POjpqxz2Jh9gIAHf7rDf2daFa0tWVhTl+mvlr+mPRW211Om6On2Y6suqo/GFJr5L0u5Iecs59p3PuEXWD2n2SPjjGe71X0oskfVjSw733epmk90l6SNLjQ6/vSPpXkh5xzr3AOfdtzrlv6r3Hb0h6QNLPnP+vBgDn9+T1QcL5pZeaqlfKZ7w6bpVySfXKIOzsHsTdNULCCQAn8/dTXmhWdSnh8XZScjucyRsB4ASffnYQG1+c+EXlcIHrLrGR2AggOf7Y1xdcXlLJ35eRIL+4VZL2j+KLjRNfVJpZTdKbeg/f6Jw7/l+Rc+5xSZ+Q9Boz+8oR3uuqpO+SdCjpDc45/xT7ByQ9K+m7zeyK9zt+sxd4f99/L+fcbUmv7z38m2b2/PH/dgAwmT/39lM+/EC6+yn7FuuDCqAEOipJOAHgBMNdI2aJJ53V+Mf4SOSNAHCWp67TUdk3vDIk5mkDxEYAONnTXkflC+9LdzpdX7M2vE4rvtiYRUflqyWtSnraOffECc9/qPf1tSO819f3PtPHnHPX/SeccweSPiKpLOkbRvlgzrkvqhuIJek5o/wMAGTpKe+i8sVXuKgMZ6rH21FJwgkApwsvKomNzXRGv5I3AsAJ7uy3dGNrsIcr9YvKWrmkstc5E+NhrIfYCAAneNqbNPBgwmu0+hrV+It4srio/LLe1z885fn+918+4/dSb2/Xhd7Da6P8DABk6UnvovKh+zmMDS4qD+ILqh4STgA4BXu4Qo1aGh2VIm8EgBP5cXGpXtEDie/hMrNg/OtO3CtDiI0AcILPBB2V5Iylkmmh6q3TijBvrNz7Jff0vN7XL5zyfP/7o3RtZPleUnc3V0XSHzvnPjvizwBAJg6O2vrc+u7x44fuJ7A2a/7oVxJOZZdwvn7E9yLhBDB3XFSGGtVBbIx52oDIGwHgRJ/2xr4+yEh0Sd3xr1u9C8rI9zdHFxvN7JOnPPXgqO8BIG2djtNnvR2VD3JRKal7prrfOpQUZ2zM4qKy/7+U3VOe719/j9JKlNl7mdlXqLsfTJJ+cITf7f8sQRXAxD57c0ftjpMkVcumL2VUgRbryXSNkHACwAnu7Ld07c7+8eMXc1GZ0uhX8kYAOMGnvfF2L+IwVlK/wLU7DjfyvDG62AgAk3rmzr72W53jxy9gR6WkcPxrjAWuWVxU5o6Z3S/pw5IWJL3XOfdrc/5IABL0pFcZ+4LLi6qWs5i2XWxBR2XcI3xIOAHgBE973ZSLtbKurqY93k4aGoseYWVsnpE3AsgDf9LAi5nCI2noMDbuvDF3Jo2NzrmXnvK+n5T0ksk/IYDYfcYr4Ll/pa6lepRXWGOLvcA1i/+W+//LaZ7yfP/Ke+uU5zN9LzNblvSrkr5U0i9JeusIvzdAUAWQhSevsZ9y2GI6e7hyh4QTQB48NTT2lfF24Y7KGBNOD3kjAJzgqRuD/6uio7IroUk80cVGAJhUsJ/yMnGxrxn5mWoW7T1/0fv63FOe///bu/cgOav7zOPPb+7TrbtGEkgICQ2SkASCBQIYLwaFSxwbiAm4nPV6dxNvpbZqQ2yHeFPrjb2bspPyOtm4vA52qrJVhFRly7tJvI4hF4LBYC4GE8ARjkBoNBIyV82MLqAZzYxGM2f/6Nt5e6Znumd6+u33vN9Plar77e555wxNzzPnPef8TuHxI4t9LjPrkvSApMslPSzpE865qZleCwCL7cBRBirLZTr9fbjCC1UPHU4AmIG/orKXsq+Swp8Z66HfCABlxiYm9caJ0eIxezfndHuVeAKvNkA2AkCZfm9F5RbKvhZ1tYfdb6zHQOXe/O3lFZ4vPP7SYp7LzNok/V9JN0j6oaRfdM6dqeJ7AsCi8FeNbKOEjyQpE3g9dQ8dTgCYwcGyFZWIlkUP/GIs/UYAKNM/OCzncvc72lq0cVWluYnpkklP6VeyEQDKRFZUUmmgKLqiMrxsrMdA5dOS3pXUa2aXzfD8XfnbB6s410OSpiRdZ2Zr/SfMrFPSbZImlVsV4j9nkv5U0u2S/knSh51zIwKAmIxNTOrIsdKvoa2sqJQUXVE5Ms7FWNHhBJAy/iSerWvJRim6D9dogB1OD/1GACjjT+DZ0pNVawsl0SUpk57Sr2QjAJTx96jsZUVlUegTXBc8UJm/4Hlv/vAbZlb8v8fM7pG0W9IPnHMveI/fbWb7zezLZed6W9K3JHVI+mb+AmvB70taI+nPnXMDZc34mqRPSNov6Rbn3MmF/lwAsBD9g8OaKsyMbW3RJmbGSirfo5KLsaLDCSBFxiYm9fqJ08VjVlTmdAe+10gB/UYAmI5KAzOLlEUP8GJsAdkIAFGnz5zVW++OFY97WVFZ1B34liFtc7+kKr8r6SZJ10rqM7MnJW2SdLWkQUmfLHt9j6Ttks6d4VyfkXSNpDsl7Tez5yXtknSxpD5J9/gvNrNfkPSp/OHrkv4gd212mv/unNtf808GAPPQdzS6B1dbaz0WsCdfZEVlgKFa4Jw7Y2b3Svpt5TqctxQGCWfrcEq6W9J3nHOf8871tpl9S9K/Vq7D+UvOucIob6HD+Wd0OAE0u2nl7VZ2x9ugJpFJyUBlHv1GAPAwUDkzf9XISNilXyWyEQCKDg+V5td3tLVo/Qr6jAWh9xvrMlDpnBszsz2SPifp45I+Ium4pPslfcE590YN5xoys6sk/U7+PHdIOirp65L+2wwXWld692+e5dT3K3exFgAW3atHTxXvsz9lSWRFJR1OOpwAUqW8vB2TeHKipV/D63D66DcCQBQl0WeWCXzViI9sBIASf3/KC1ZTEt0XeiWeeq2olHNuVNJ/zf+b67W/o1xoVnr+uHIXWD9V6TXea+9XLjABoGn0RQYq6XAWROqpBxiqPjqcABDlD1T2smqkKLrXSPCTeOg3AkDexOSUXvNWjrCisiT0VSPlyEYAyPEHKrewP2VEpr3UbxwNcDutug1UAgBKDhz1Z8bS4SzIdqZmj0pJdDgBwHdwgGycSeh7jQAAZnbk2GmdncrVRG8xaXNPJuYWNY9uv/RrCvqNAICc/kGvCg8DlRGhT+Kh3hIA1NnomUm9fuJ08ZgVlSV+qIa8RyUAYDr24ZpZmsrbAQBK/FzcvDqrzrbWWV6dLlmyEQBS6dCQv10IfUZfZILrRHjZyEAlANTZwYFhudzEWHW2tWjjKmbGFkTK24W/RyUAIG9ickqHvfJ27MNVEpkZOzEpV/gjAgAQtIMDpe1CKIkeFfqqEQDAdM45HfZKv5KNUaFnIwOVAFBnB7z9KbeuW8LGz55sZB+uSU1NcTEWANKA8naV+TNjnZPGz07F2BoAQKNQaaAyv/RrGrYMAQBIR98bj1Rfo/RrVOiVeBioBIA6O+DNjN3GipGITGf0YuzY2fCCFQAwnb9qZBPl7SK626P/LUKcHQsAmO7gIHs3V5INfNUIAGC6Q14u9izp1LKu9hhb03z8STyUfgUAzOnAO/6KSgYqff6KSolOJwCkBatGKstMy0ZWjgBA6KamHNk4i+7AV40AAKbr97YKYTXldNHSr+H1GRmoBIA6O3C01OHcto4Op6+rvUXmVcI9PU6nEwDSgIuxlbW2mDraSt0yLsgCQPjePDmqsYlSqe/eNWSjz5/EM3LmLPs3A0AK+CsqexmonMavxBPiwg8GKgGgjkbGz+rNk6PF422sqIwwM2W8YB0JcAYQAGC6vgHK280mQ4k7AEgVv+zrhhXdyna2zfLq9PFLv06xfzMApEL/oLeisoc+Y7nQqw0wUAkAdeRfiO1ub9WGFd0xtqY5ZbxOeIilCgAAUVNTTv2DrKicTSbw2bEAgKiDXhWeXnJxGv9irBTmBVkAQJS/opLSr9P5k1vPTjmdCWwSDwOVAFBHB46W9qfctm6JWlpsllenkz87doTSrwAQPMrbzc2/IDs2QTYCQOgiJdHJxWnK92+mEg8AhG1sYjJSoY4+43SZ9mg2hjaJh4FKAKijPm+gcitlX2fkdzpZUQkA4fMvxlLebmbRbAyrwwkAmM4v/bp1HRdjy7W2mDrZvxkAUuO1YyMqbEfc3mo6byUV6sqVVxs4PRHWNVUGKgGgjl71Svhso8M5o2wn5e0AIE36BkqTeChvN7PuyB6VYXU4AQBRzrnIBFdKos+M/ZsBID0OeftTblqdVVsrw1blOtpa1OZV7gstG3nHAaCOWFE5N3/VyEhgoQoAmM5fUbmVi7Ez6vb2qByl9CsABG1weFzvjZUmpVD6dWbRfiOTeAAgZJH9KXvYn7ISf4JraNUGGKgEgDp5b2xCb787VjzexkDljCIzY8fpcAJA6CL7cDFQOSNWjQBAevi5uDrboZXZjhhb07wyAV+MBQBE9XsrKrcwgaeikPuNDFQCQJ30eWVfl3S2af3yrhhb07xYUQkA6eGcUx8DlXPqDrjDCQCIYgJPdUK+GAsAiIqsqFzDispK/GuqoW0ZwkAlANRJtOzrEpnZLK9Or8gelayoBICgDZ4a1ynK280pumqEbASAkDFQWR32bwaAdHDORfao7GWgsqLIliGBTeJhoBIA6uSAt6Jy21rKvlbCikoASA9/NWXPEsrbVRKdGUs2AkDIGKisTpZsBIBUGBwe1ylvIcOWHrKxksgE14mwspGBSgCokwNlKyoxsyyrRgAgNbgYW52QZ8YCAKL8bNzKBNeKKIsOAOngr6Zcxd7Nswo5GxmoBIA68Qcqt62jw1lJppMVlQCQFn0DpWxkoLKykGfGAgBK3h2d0MCp8eIx2VhZNuB9uAAAJf4Eni09lH2dTXTLkLD6jQxUAkAdnBg5E+lwMlBZWYa9RgAgNSIrKtmfsqJMwDNjAQAlfi4u6WzTumWdMbamuYW8agQAUPLD/qHi/R3nLouxJc3Pr8QTWjYyUAkAdfBE32Dxfs+SDjqcs/Avxo6MhxWqAICogwOlMj5bmcRTUbe3aiS0mbEAgJIXjhwv3t+6bonMLMbWNLeQV40AAHImJqf05IHSQOUN29fE2Jrm5/cbT0+EtfiDgUoAqINHXhko3t+zfS0dzllQwgcA0uHk6TMaGqa8XTUiM2MD63ACAEq+9/LR4v3rLuyJsSXNL8uWIQAQvBeOnNCp8Vz/p6OtRe/rXR1zi5pbyJN4GKgEgAWamJzS46+WBipv2rkuxtY0v0wnKyoBIA32vvFu8f7SzjatXUq1gUoo/QoA4Ts2PK4XjpwoHtNvnJ0/iWeUCa4AEKTHvOup12xZrYy3uAHThdxvZKASABboH187rlNjpdk/121lZuxs/BWVoxNhhSoAoOTvXnq7eP/qLauoNjCL7oBnxgIAcr6/f0BTLnf/nGVdumTD8ngb1ORCvhgLAMh5fH9pK609lH2dU8j9RgYqAWCBHnm5NPvn2l5m/8wlG1lRycxYAAjRmbNT+vt/Lg1U3nbp+hhb0/y4GAsA4fPLvt60k+1C5pKh9CsABO3Nk6N69eip4vGe7WtjbE0yZPwtQwKrNsBAJQAsgHNOj+73Opw7KN8zF3/j5/GzUzo7ORVjawAAi+Gpg4N6L19toLOtRTeSj7MKea8RAIA0NjGpJ/uGisf0G+eWofQrAATN30brgp6sNvdkY2xNMviLY0Kb4MpAJQAsQP/gsI4cO108vnEHs3/mkvUuxkrSacq/AkBwHtxbWk154461WtJJtYHZ+JN4zkwyiQcAQvP0waHithfZjla9r3d1zC1qflQbAICwPeaVfb2Bsq9ViZR+Dex6KgOVALAA3/PKvu5av0znLu+OsTXJUF4a9/R4WMEKAGk3NjEZKW93627Kvs7FXzUihdfpBIC083Px+u1r1NnWOsurIUVLvzJQCQBhGT87qacPlioNUPa1OiFP4mGgEgAW4NFXKPtaq462FrW3lvZjGaGMDwAE5fFXBzSc34M429FKp7MK3WXVBij/CgDhmJpyeuSV0gTXm3fSb6xG9GIsfUYACMlzh48XJ2d2t7fqqgtWxdyiZOgOeMsQBioBYJ6ODY/rxZ+eKB4zUFk9f1VlaMEKAGn34Eulsq837Vw3bRAO03W2tchKc3iCmx0LAGn2T2+c1NDwuCSptcWYwFOlbq/awNjElCanXIytAQDUk1/29f0XrlZXO33GakSupwZWhYeBSgCYp8deHVShr7RuWacu3rAs3gYliL9P5cg4s2MBIBQj42cj1QZuo+xrVcwsUv6VgUoACMcjXtnXn9m8UisyHTG2JjmyZftbh3ZBFgDS7PFXS5UGbmACT9VCrjbAQCUAzJN/IfbGHetk/lIIzKo74JrqAJBmj+4f0NjElCRpaVebrtvWE3OLkqM7Mjs2rE4nAKSZvz/lzTvPibElyZIpq8gQ2gVZAEir14ZGdGhopHh8w/Y1MbYmWcqrDUwFVG2AgUoAmIfxs5N64kCpTMFNO5j9Uwt/dix7VAJAOB7c+1bx/gd3naPONkr4VCvDJB4ACM5rQyPqGxguHt/MdiFVKy+LzpYhABAGfzXltnVLdN7KTIytSZbySTwhVRtgoBIA5uHZQ8c1ku8odbW36NpeVozUInIxdjycUAWAZwPMoQAAEflJREFUNHtvbEI/eLU0iefWSyn7WgsGKgEgPI94VXi2r1uq81dzMbZaZqasV21ghH4jAAThMa/PyL7NtemeVm0gnGxkoBIA5sEv+3rd1jVs+lyjSIeTFZUAEISH9x3Vmclc2ddV2Q5d27s65hYli9/pHAtoZiwApNnDkbKvrKaslZ+NlEUHgOQbPTOpZw4dKx6zP2VtusoqFoVUbYCBSgCokXNOj75SKlNA2dfaZbzSryHN/gGANPubl0plX3/+4nPU3kpXoxasqASAsBwfOaPnXztePL6JgcqakY0AEJZnDg3pzNnc5NYlnW26cvPKmFuULC0tFtmn8nRAk3i4egAANXrl7VN68+Ro8XjPRQxU1iob6XCGE6oAkFYnRs7oqb6h4vGtuyn7WqvudibxAEBIHts/oCmXu792aad2b1geb4MSKEPpVwAIymP7S2Vfr9vaw+TWeQh1Eg//JwBAjfyyr5duXKG1S7tibE0y0eEEgLA8tO8dnc1fjV27tFNXXbAq5hYlT6S8HZN4ACDxvueVfb1xxzq1tFiMrUmmDKVfASAYzjk99mqpQh37U85PtN8YzjXVug1Umlm3mX3RzA6Y2ZiZvWVm95nZhnmca6WZ/U8zO2Jm4/nbr5nZilm+ptXMfsPMfmJmo2Y2aGZ/YWY7FvaTAUDUI/tLoXozZV/nJZOSFZVkI4C0eHBvqezrhy45V61cjK1Zpj3MmbHlyEYAaTA2Makn+kqrRm6h7Ou8+P3GkCe4ko0A0qB/cFhvnChVqLt++5oYW5NcrKichZl1Sfq+pC9IWiLpu5Jel/Qrkn5sZltqOFePpOckfUrSWUl/LemUpE9L+pGZTZuebWYtkv5S0lclnSfpbyXtk3SXpOfN7Kp5/3AA4Bl4b0x7Xz9ZPL5xBx3O+ch0eh3OgELVRzYCSIuBU2N69tCx4vFtl1L2dT66A+1w+shGAGnxTP+x4u/yTEer3te7OuYWJVMm0FUjPrIRQFr4ZV93rV+mdcuoUDcf3R3+liHhLP6o14rKz0u6RtIzkrY55z7mnLta0m9KWiPpvhrO9TVJF0r6f5K25891saQ/krRNueAs90lJd0jqk3SRc+4u59wNkj4qKSPpf5tZ2wxfBwA1+b63mnLDim5ddM7SGFuTXFk/VMfDCdUyZCOAVPj7n7xT3INrw4puXX5+xQn7mEUaLsaKbASQEg97ZV8/sHWNurxV86hepiMV+zeTjQBSgbKv9eFX4gmp37jgoDGzDkl35w9/zTk3XHjOOfdVM/t3kq43syuccy/Mca5zJf0rSWck/UfnnH/1+j9J+iVJnzCz33LODXjP3ZO//S3nXPGvQefct83sAUm3S/oFSd+e309ZH/veele/97evxNkEAAt0aHCkeP+mHWtlRmm7+fAvxr5w5IQ+/r+erev577z8PN15xXl1PWctyMbqPdU3pG8+fjDOJgBYoANHi7/idOvuc8nGefKz8dH9A3XPxl/9wJZYLwiQjdV7YO9b+j/P/TTOJgBYoJfeeLd4/2bKvs6bX23gL55/XT86fGyWV9fuP//8Rdp9XnwTrMjG6t3/9OHIBAAAyfPc4ePF+3suouzrfPn9xj954pAe8LZhqYev3LlbG1dl6nrOatRjRsz7JS2X1O+c+/EMz/+VpN2SbpM0a6hK+qByqzyf9MNRkpxz42b2oHIzfT4k6X5JMrMLJO2QNKpceYKZvv/t+e8fa6i+N3pWP+yv7x9VAOJD2df5y3aW4ue9sfr/brz6gthLK5GNVRoaHicbgYDcupuyr/PlrxoZGh7X0PB4Xc//kctq3uaq3sjGKr15YpRsBALRYtKei1g1Ml9Z72LsmydH9ebJ0VleXbuTpyfqer55IBurdHhohGwEArEi067LNq6MuxmJlfGuqR4aGtGhoZFZXl27sYl4VmnWo/TrpfnbFys8X3h89yKdq/A1/+ycm+kvjFq+PwBUZcuarK7ZEvtgWGJds2W1utrrVX28KZGNAFLnyk0rdfGGZXE3I7Gu375GgS9GJRsBpM6Hd6/XqmxH3M1IrBQM8pKNAFLnY1duVGtL2B2fxbRne5irUeuxovL8/O0bFZ4vPL5pkc5Vz++/qHrXZPWVOy+JuxkAFqizrVUf2LZGHW1BD7QtqlXZDj3+2T16sm9QU87V/fy71i+v+zlrRDZW6bKNK8hGIACZjrb8QBsdzvnqXbNEj3/2Bv3o0HE51T8br9wc+6xlsrFK129bo1XZ9ribAWCBVmQ6dP22MC8mNsq1vT166DPXae/rJxfl/FvXLVmU89aAbKzS7Zdt0M71TIgDkm7tsi79ywt74m5Got3xLzZoc09WfUdPLcr51y7tWpTzzqUeA5WFVD9d4fnC2tOli3Suen5/SZKZ7avwVG+155jJ2mVd+tjPnD/3CwEgBc5Z3qWPXrkx7mYsFrKxSpt7strck13IKQAgGJtWZ7VpdbC/E8nGKu1cv4yLsQCQd9E5y3TROcH+TiQbq3TFppW6YlPsk64AIHZmpsvPX6nLzw/rdyLLgQAAAAAAAAAAAAA0XD1WVA7nbzMVni9MCa5mLep8zlXP7y9Jcs7tmunx/KygndWeBwCQWmQjAABRZCMAAFFkIwAAqs+Kyp/mb8+r8Hzh8SOLdK56fn8AAOqBbAQAIIpsBAAgimwEAED1Gajcm7+9vMLzhcdfWqRzFb7mYjNrX+D3BwCgHshGAACiyEYAAKLIRgAAVJ+ByqclvSup18wum+H5u/K3D1ZxrockTUm6zszW+k+YWaek2yRNSvq7wuPOucOSXpHULenDC/z+AADUA9kIAEAU2QgAQBTZCACA6jBQ6Zw7I+ne/OE3zKxQv1xmdo+k3ZJ+4Jx7wXv8bjPbb2ZfLjvX25K+JalD0jfNzN9D8/clrZH05865gbJmfLXwGj+MzewXJd0u6aCk7y7gxwQAoGpkIwAAUWQjAABRZCMAADltc7+kKr8r6SZJ10rqM7MnJW2SdLWkQUmfLHt9j6Ttks6d4VyfkXSNpDsl7Tez5yXtknSxpD5J98zwNfdJ+pCkO/Jf82j+e1wvaVTSJ5xzZxfyAwIAUCOyEQCAKLIRAIAoshEAkHr1KP0q59yYpD2SviTptKSPKBeq90u63Dl3qIZzDUm6StIfKTcL6A5JyyV9XdJVzrnjM3zNlKSPSvpNSW9JulXSJZK+LelK59yP5vuzAQAwH2QjAABRZCMAAFFkIwAAkjnn4m5DYpjZvp07d+7ct29f3E0BACySXbt26eWXX37ZObcr7rYkAdkIAOEjG2tDNgJA+MjG2pCNABC+hWRjXVZUAgAAAAAAAAAAAEAtGKgEAAAAAAAAAAAA0HAMVAIAAAAAAAAAAABoOAYqAQAAAAAAAAAAADQcA5UAAAAAAAAAAAAAGo6BSgAAAAAAAAAAAAANx0AlAAAAAAAAAAAAgIYz51zcbUgMM3uvs7NzaW9vb9xNAQAskv7+fo2Pj59yzi2Luy1JQDYCQPjIxtqQjQAQPrKxNmQjAIRvIdnIQGUNzOwdSRlJr8fdloQr/FXSH2srUC3er+TgvaqPjZJOO+fOibshSUA21g2f32Th/UoO3qv6IBtrQDbWDZ/f5OC9Shber/ogG2tANtYNn9/k4L1KFt6v+ph3NjJQiYYzs32S5JzbFXdbMDfer+TgvQKSi89vsvB+JQfvFZBcfH6Tg/cqWXi/gOTi85scvFfJwvsVP/aoBAAAAAAAAAAAANBwDFQCAAAAAAAAAAAAaDgGKgEAAAAAAAAAAAA0HAOVAAAAAAAAAAAAABqOgUoAAAAAAAAAAAAADWfOubjbAAAAAAAAAAAAACBlWFEJAAAAAAAAAAAAoOEYqAQAAAAAAAAAAADQcAxUAgAAAAAAAAAAAGg4BioBAAAAAAAAAAAANBwDlQAAAAAAAAAAAAAajoFKAAAAAAAAAAAAAA3HQCUAAAAAAAAAAACAhmOgErEzsxvMzM3y79m425g2ZtZtZl80swNmNmZmb5nZfWa2Ie62IcrMHp/j8/PBuNsIoHZkY/MhG5ODbATCRDY2H7IxOchGIDzkYnMiG5ODbGwubXE3APD0S3qqwuNoEDPrkvR9SddIelvSdyVtlvQrkm41s2ucc4fiayEq+Lak4Rkef7PRDQFQV2RjEyAbE4tsBMJENjYBsjGxyEYgPORikyAbE4tsbAIMVKKZPOWc++W4GwF9XrlAfUbSLc65YUkys3sk/aGk+yTdEFvrUMlnnXOvxd0IAHVHNjYHsjGZyEYgTGRjcyAbk4lsBMJDLjYPsjGZyMYmQOlXAEVm1iHp7vzhrxUCVZKcc1+V9JKk683sijjaBwBAo5GNAABEkY0AAESRjcDCMFAJwPd+Scsl9TvnfjzD83+Vv72tcU0CACBWZCMAAFFkIwAAUWQjsACUfkUz2WpmX5a0WtKQcvXVH3LOTcXbrFS5NH/7YoXnC4/vbkBbUJt/b2arJU1JOiDpr51zP425TQAWjmyMH9mYXGQjECayMX5kY3KRjUB4yMXmQDYmF9nYBBioRDO5Nv/P9xMzu9M51xdHg1Lo/PztGxWeLzy+qQFtQW0+X3b8P8zsS865L8XSGgD1QjbGj2xMLrIRCBPZGD+yMbnIRiA85GJzIBuTi2xsApR+RTN4V9IfKLfZ8Or8vxslPSvpEkkPm9ny+JqXKkvyt6crPD+Sv13agLagOk9I+jeSeiVlJG2X9NuSzkr6opl9Osa2AZg/srF5kI3JQzYCYSIbmwfZmDxkIxAecrG5kI3JQzY2EXPOxd0GJJyZfUfSjhq/7N86556b47ytkh6TdJ2k/+Kc+/I8m4gqmdmfSPpVSb/nnCufTSIzu1BSn6Q+59y2RrcP1TOzWyT9g6STktY750ZjbhKQKmRjOMjGcJCNQLzIxnCQjeEgG4H4kIthIRvDQTbGg9KvqIcLlJtxUIvMXC9wzk2a2VeUC9afk0SwLr7h/G2l9yebvz3VgLZgAZxzD5vZ85KulHS1pMfjbRGQOmRjOMjGQJCNQOzIxnCQjYEgG4FYkYthIRsDQTbGg4FKLJhz7rJFPH2hlvq5i/g9UFLYKPi8Cs8XHj/SgLZg4fqUC1U+P0CDkY1BIRvDQjYCMSEbg0I2hoV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+ "text/plain": [
+ "<Figure size 2250x600 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CvJC5_LYTVGs"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/polgrad_risky_mdp.ipynb b/code/polgrad_risky_mdp.ipynb
new file mode 100644
index 0000000..ac29a30
--- /dev/null
+++ b/code/polgrad_risky_mdp.ipynb
@@ -0,0 +1,554 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "polgrad_risky_mdp.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3P1g559FrwfN",
+ "outputId": "db674d16-f7a6-4538-812b-e4f5418b4808"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import gym\n",
+ "import sys\n",
+ "\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch import optim\n",
+ "\n",
+ "print(sys.version)\n",
+ "print(torch.__version__)\n",
+ "print(torch.version.cuda)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "3.7.11 (default, Jul 3 2021, 18:01:19) \n",
+ "[GCC 7.5.0]\n",
+ "1.9.0+cu102\n",
+ "10.2\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LoesPrPVr4rC"
+ },
+ "source": [
+ "import torch.nn as nn\n",
+ "class policy_estimator(nn.Module):\n",
+ " def __init__(self, env, return_dist=False):\n",
+ " super(policy_estimator, self).__init__()\n",
+ " if return_dist:\n",
+ " self.n_inputs = 2*51\n",
+ " else:\n",
+ " self.n_inputs = 1#env.observation_space.shape[0]\n",
+ " self.return_dist=return_dist\n",
+ " self.n_outputs = 2\n",
+ " self.softmax=nn.Softmax(dim=-1)\n",
+ " # Define network\n",
+ " self.network = nn.Sequential(\n",
+ " nn.Linear(self.n_inputs, 200), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(200, self.n_outputs)).to('cuda')\n",
+ " def forward(self, state):\n",
+ " if self.return_dist:\n",
+ " state=state.reshape(-1,self.n_inputs)\n",
+ " action_probs = self.network(state.float())\n",
+ " return self.softmax(action_probs)\n",
+ " def predict(self, state):\n",
+ " action_probs = self.network(state.float())\n",
+ " return self.softmax(action_probs)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "79yfRBcavc5v"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class mdp():\n",
+ " \n",
+ " def __init__(self):\n",
+ " self.pos=0\n",
+ " self.action_space = spaces.Discrete(2)\n",
+ " self.observation_space = spaces.Discrete(2)\n",
+ " self.d={-1:-1,\n",
+ " -2:1,\n",
+ " 0:0,\n",
+ " 1:-0.1,\n",
+ " 2:-0.1,\n",
+ " 3:1,\n",
+ " }\n",
+ " def reset(self):\n",
+ " self.pos=0\n",
+ " return self.pos\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " if self.pos==0:\n",
+ " if a==0:\n",
+ " isdone=True\n",
+ " if np.random.random()<0.1:\n",
+ " self.pos=-1\n",
+ " else:\n",
+ " self.pos=-2\n",
+ " elif a==1:\n",
+ " isdone=False\n",
+ " self.pos=1\n",
+ " elif self.pos==1:\n",
+ " if a==0:\n",
+ " isdone=True\n",
+ " self.pos=2\n",
+ " elif a==1:\n",
+ " isdone=True\n",
+ " if np.random.random()<0.2:\n",
+ " self.pos=2\n",
+ " else:\n",
+ " self.pos=3\n",
+ " reward=self.d[self.pos]\n",
+ " return self.pos,reward, isdone, None\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " np.random.seed(seed)\n",
+ "base_env=mdp()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ec6Ik7R7wdKG",
+ "outputId": "f88f3ddc-2840-45c8-e502-1cf8d361d0f7"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bz3WNO-Ky9CG"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7Jaj_eyZy_Aq",
+ "outputId": "9ddc5076-c949-485b-e561-0a743306f214"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "6qRvcwUr0_9u",
+ "outputId": "889040e8-64d5-4e06-cbfc-3d38e1a0838c"
+ },
+ "source": [
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, 200), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(200, 200), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(200, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def risk_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " std=self.std_dev(dist,q)\n",
+ " return q-alpha*std\n",
+ " def std_dev(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var.sqrt()\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist\n",
+ "net=Network(1,2,51,torch.linspace(-2,2,51).to('cuda')).to('cuda')\n",
+ "net.load_state_dict(torch.load('mdp.pt')) \n",
+ "print(net(torch.tensor([0]).float().to('cuda')).argmax().item())\n",
+ "print(net.risk_action(torch.tensor([0]).float().to('cuda'),alpha=5).argmax().item())\n",
+ "print(net(torch.tensor([1]).float().to('cuda')).argmax().item())\n",
+ "print(net.risk_action(torch.tensor([1]).float().to('cuda'),alpha=5).argmax().item())"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "1\n",
+ "1\n",
+ "0\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "z7TFyWup2Q5A"
+ },
+ "source": [
+ "class super_env():\n",
+ " def __init__(self,env,nn):\n",
+ " self.env=env\n",
+ " self.agent=nn\n",
+ " self.state=self.env.reset()\n",
+ "\n",
+ " def reset(self):\n",
+ " state=self.env.reset()\n",
+ " self.state=state\n",
+ " return state\n",
+ " def step(self,a):\n",
+ " if a==0: #risk neutral\n",
+ "\n",
+ " action=self.agent(torch.tensor([self.state]).float().to('cuda')).argmax().item()\n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ " r=r-0.95\n",
+ " else: #risk averse\n",
+ " action=self.agent.risk_action(torch.tensor([self.state]).float().to('cuda'),alpha=5).argmax().item() \n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ " #r=-np.exp(-r)\n",
+ " self.state=next_state\n",
+ " return next_state,r,complete,_"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "542XMmdMsBMX"
+ },
+ "source": [
+ "def discount_rewards(rewards, gamma=0.99):\n",
+ " r = np.array([gamma**i * rewards[i] \n",
+ " for i in range(len(rewards))])\n",
+ " # Reverse the array direction for cumsum and then\n",
+ " # revert back to the original order\n",
+ " r = r[::-1].cumsum()[::-1]\n",
+ " return r #- r.mean()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "k8qL6QInsA1b",
+ "outputId": "930c1903-2dba-4598-bad7-55e6fb631371"
+ },
+ "source": [
+ "env=super_env(base_env,net)\n",
+ "s=env.reset()\n",
+ "return_dist=True\n",
+ "pe = policy_estimator(env,return_dist=return_dist).to('cuda')\n",
+ "if return_dist:\n",
+ " print('Using return dist')\n",
+ " dist=net.dist(torch.tensor([s]).float().to('cuda'))\n",
+ " print(pe(dist))\n",
+ "else:\n",
+ " print('Using state')\n",
+ " print(pe(torch.tensor([s]).to('cuda').float()))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Using return dist\n",
+ "tensor([[0.5183, 0.4817]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WL9Ow8iisF8I"
+ },
+ "source": [
+ "def reinforce(env, policy_estimator, num_episodes=10000,\n",
+ " batch_size=32, gamma=0.99, agent=None):\n",
+ " print('training...')\n",
+ " # Set up lists to hold results\n",
+ " total_rewards = []\n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Define optimizer\n",
+ " optimizer = optim.Adam(policy_estimator.network.parameters(), \n",
+ " lr=0.0002)\n",
+ " \n",
+ " action_space = np.arange(2)\n",
+ "\n",
+ " for ep in range(num_episodes):\n",
+ " start=0\n",
+ " s_0 = env.reset()\n",
+ " states = []\n",
+ " rewards = []\n",
+ " actions = []\n",
+ " complete = False\n",
+ "\n",
+ " while not complete:\n",
+ " # Get actions and convert to numpy array\n",
+ " if agent is not None:\n",
+ " dist=agent.dist(torch.tensor([s_0]).float().to('cuda'))\n",
+ " action_probs = policy_estimator(dist).cpu().detach().numpy().flatten()\n",
+ " else:\n",
+ "\n",
+ " action_probs = policy_estimator(torch.tensor([s_0]).float().to('cuda')).cpu().detach().numpy()\n",
+ " if np.any(np.isnan(action_probs)):\n",
+ "\n",
+ " print(action_probs)\n",
+ " print(s_0)\n",
+ " raise ValueError()\n",
+ " if action_probs[0]<0.01:\n",
+ " action_probs=np.array([0.01,0.99])\n",
+ " elif action_probs[1]<0.01:\n",
+ " action_probs=np.array([0.99,0.01])\n",
+ " \n",
+ " action = np.random.choice(action_space, p=action_probs)\n",
+ " s_1, r, complete, _ = env.step(action)\n",
+ " #print(s_0,action,r,s_1)\n",
+ " states.append(s_0)\n",
+ " rewards.append(r)\n",
+ " actions.append(action)\n",
+ " s_0 = s_1\n",
+ " start+=1\n",
+ " if start>1000:\n",
+ " print('\\n')\n",
+ " print(states)\n",
+ " print(action_probs)\n",
+ " raise ValueError()\n",
+ " # If complete, batch data\n",
+ " if complete:\n",
+ " \n",
+ " batch_rewards.extend(discount_rewards(rewards, gamma))\n",
+ " batch_states.extend(states)\n",
+ " batch_actions.extend(actions)\n",
+ " batch_counter += 1\n",
+ " total_rewards.append(sum(rewards))\n",
+ " \n",
+ " # If batch is complete, update network\n",
+ " if batch_counter == batch_size:\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " state_tensor = torch.FloatTensor(batch_states).to('cuda')\n",
+ " reward_tensor = torch.FloatTensor(batch_rewards).to('cuda')\n",
+ " # Actions are used as indices, must be LongTensor\n",
+ " action_tensor = torch.LongTensor(batch_actions).to('cuda')\n",
+ " \n",
+ " # Calculate loss\n",
+ " if agent is not None:\n",
+ " dist=agent.dist(state_tensor.unsqueeze(1))\n",
+ "\n",
+ " logprob = torch.log(policy_estimator(dist)) #for return dist\n",
+ " else:\n",
+ " logprob = torch.log(policy_estimator(state_tensor)) #for state input\n",
+ "\n",
+ " selected_logprobs= reward_tensor * torch.gather(logprob,1,action_tensor.unsqueeze(1)).flatten()\n",
+ " loss = selected_logprobs.mean()\n",
+ " \n",
+ " # Calculate gradients\n",
+ " loss.backward()\n",
+ " # Apply gradients\n",
+ " optimizer.step()\n",
+ " \n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Print running average\n",
+ " print(\"\\rEp: {} Average of last 15: {:.2f}\".format(\n",
+ " ep + 1, np.mean(total_rewards[-15:])), end=\"\")\n",
+ " \n",
+ " return total_rewards"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 531
+ },
+ "id": "y8X6yw0mtk1p",
+ "outputId": "8d479fb1-5b9e-409f-a4bc-3fa4130699cc"
+ },
+ "source": [
+ "rewards = reinforce(env, pe,agent=net)\n",
+ "window = 10\n",
+ "smoothed_rewards = [np.mean(rewards[i-window:i+1]) if i > window \n",
+ " else np.mean(rewards[:i+1]) for i in range(len(rewards))]\n",
+ "\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(rewards)\n",
+ "plt.plot(smoothed_rewards)\n",
+ "plt.ylabel('Total Rewards')\n",
+ "plt.xlabel('Episodes')\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "training...\n",
+ "Ep: 10000 Average of last 15: -0.13"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "W4YkmQtc7ewI",
+ "outputId": "090e36ef-13e0-4891-b9de-15ecb2885149"
+ },
+ "source": [
+ "dist1=net.dist(torch.tensor([0]).float().to('cuda')).squeeze()\n",
+ "dist2=net.dist(torch.tensor([1]).float().to('cuda')).squeeze()\n",
+ "print(pe(dist1),pe(dist2))\n",
+ "print(pe(dist1).argmax(),pe(dist2).argmax())"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.3120, 0.6880]], device='cuda:0', grad_fn=<SoftmaxBackward>) tensor([[0.5620, 0.4380]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n",
+ "tensor(1, device='cuda:0') tensor(0, device='cuda:0')\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/polgrad_wcw.ipynb b/code/polgrad_wcw.ipynb
new file mode 100644
index 0000000..4aadc27
--- /dev/null
+++ b/code/polgrad_wcw.ipynb
@@ -0,0 +1,1009 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "polgrad.ipynb",
+ "provenance": [],
+ "collapsed_sections": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "b-RewR2fIgYJ"
+ },
+ "source": [
+ "**Policy gradient for Hierarchical Multi-Agent SC**\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3P1g559FrwfN",
+ "outputId": "5dfd327d-8e0f-4dc5-ee85-95146488fbac"
+ },
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import gym\n",
+ "import sys\n",
+ "\n",
+ "import torch\n",
+ "from torch import nn\n",
+ "from torch import optim\n",
+ "\n",
+ "print(sys.version)\n",
+ "print(torch.__version__)\n",
+ "print(torch.version.cuda)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "3.7.11 (default, Jul 3 2021, 18:01:19) \n",
+ "[GCC 7.5.0]\n",
+ "1.9.0+cu102\n",
+ "10.2\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "LoesPrPVr4rC"
+ },
+ "source": [
+ "class policy_estimator(nn.Module):\n",
+ " def __init__(self, env, return_dist=False):\n",
+ " super(policy_estimator, self).__init__()\n",
+ " if return_dist:\n",
+ " self.n_inputs = env.env.action_space.n*51\n",
+ " else:\n",
+ " self.n_inputs = 2\n",
+ " self.return_dist=return_dist\n",
+ " self.n_outputs = env.action_space.n\n",
+ " self.softmax=nn.Softmax(dim=-1)\n",
+ " # Define network\n",
+ " self.network = nn.Sequential(\n",
+ " nn.Linear(self.n_inputs, 300), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(300, self.n_outputs)).to('cuda')\n",
+ " def forward(self, state):\n",
+ " if self.return_dist:\n",
+ " state=state.reshape(-1,self.n_inputs)\n",
+ " action_probs = self.network(state.float())\n",
+ " return self.softmax(action_probs)\n",
+ " def predict(self, state):\n",
+ " action_probs = self.network(state.float())\n",
+ " return self.softmax(action_probs)\n",
+ "class combined_policy_estimator(nn.Module):\n",
+ " def __init__(self, env):\n",
+ " super(combined_policy_estimator, self).__init__()\n",
+ " self.n_outputs = env.action_space.n\n",
+ " self.softmax=nn.Softmax(dim=-1)\n",
+ " # Define network\n",
+ " self.network = nn.Sequential(\n",
+ " nn.Linear(4*51+2, 300), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(300, self.n_outputs)).to('cuda')\n",
+ " def forward(self, state, dist):\n",
+ " dist=dist.reshape(-1,4*51)\n",
+ " dist=torch.cat([dist,state.reshape(-1,2)],axis=1)\n",
+ " action_probs = self.network(dist.float())\n",
+ " return self.softmax(action_probs)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "79yfRBcavc5v"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)\n",
+ "class super_env():\n",
+ " def __init__(self,env,agent,fn=None):\n",
+ " self.env=env\n",
+ " self.agent=agent\n",
+ " self.action_space=spaces.Discrete(2)\n",
+ " self.observation_space=env.observation_space\n",
+ " self.state=self.env.reset()\n",
+ " self.fn=fn\n",
+ " \n",
+ " self.policy0=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=int)\n",
+ " self.policy1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=int)\n",
+ " for i in range(self.policy0.shape[0]):\n",
+ " for j in range(self.policy0.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " self.policy0[i,j]=agent.greedy_action(torch.tensor([i,j]).float())\n",
+ " self.policy1[i,j]=agent.dqn.risk_action(torch.tensor([i,j]).float().to('cuda'),alpha=8).argmax().cpu().item() \n",
+ " \n",
+ " def reset(self):\n",
+ " state=self.env.reset()\n",
+ " self.state=state\n",
+ " return state\n",
+ " def step(self,a):\n",
+ " if a==0: #risk neutral\n",
+ " action=self.policy0[self.state[0],self.state[1]]\n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ " #r=r-0.005\n",
+ " else: #risk averse\n",
+ " action=self.policy1[self.state[0],self.state[1]]\n",
+ " next_state, r, complete, _= self.env.step(action)\n",
+ " if self.fn is not None:\n",
+ " r=self.fn(r)\n",
+ " self.state=next_state\n",
+ " return next_state,r,complete,_\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Ec6Ik7R7wdKG",
+ "outputId": "d9ad7ca2-1837-4042-864f-f0ce6bef7f9d"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "bz3WNO-Ky9CG"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "7Jaj_eyZy_Aq",
+ "outputId": "310cd633-b83e-44b0-884f-76aad3181c89"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0PMldsLNzLzR"
+ },
+ "source": [
+ "import base"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YWwjQE9VzoIr",
+ "outputId": "270074d4-d1c6-4c23-94f2-130b871a4868"
+ },
+ "source": [
+ "base_env=cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "num_frames = 80000\n",
+ "memory_size = 3000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 80000\n",
+ "\n",
+ "# train\n",
+ "optimal_agent = base.DQNAgent(base_env, memory_size, batch_size, target_update, epsilon_decay)\n",
+ "optimal_agent.dqn.load_state_dict(torch.load('optimal_agent.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {},
+ "execution_count": 8
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 672
+ },
+ "id": "MJExl4md0L5i",
+ "outputId": "cae6b6da-7802-431f-a979-5c4cadef5898"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol=np.ndarray([base_env.pos_max_x+1,base_env.pos_max_y+1],dtype=object)\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=0).argmax().cpu().item()]\n",
+ " #pol[i,j]=arrow_list[optimal_agent.greedy_action(torch.tensor([i,j]).float())]\n",
+ " pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float().to('cuda'),alpha=8).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[15,5],dpi=150)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Risk Neutral Policy at each step\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([base_env.pos_max_x+1,base_env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 2250x750 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "542XMmdMsBMX"
+ },
+ "source": [
+ "def discount_rewards(rewards, gamma=0.99):\n",
+ " r = np.array([gamma**i * rewards[i] \n",
+ " for i in range(len(rewards))])\n",
+ " # Reverse the array direction for cumsum and then\n",
+ " # revert back to the original order\n",
+ " r = r[::-1].cumsum()[::-1]\n",
+ " return r #- r.mean()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "k8qL6QInsA1b",
+ "outputId": "916a09b6-f34c-491e-a370-d5dc49efb4b1"
+ },
+ "source": [
+ "def f(x):\n",
+ " #return -np.exp(-x)+1\n",
+ " return min(0,x)\n",
+ "env=super_env(base_env,optimal_agent,fn=f)\n",
+ "s = env.reset()\n",
+ "\n",
+ "#CHANGE THIS TO SWITCH BETWEEN RETURN DISTRIBUTION AND STATE\n",
+ "#True for using DSC, False for SSC\n",
+ "return_dist=True\n",
+ "\n",
+ "pe = policy_estimator(env,return_dist=return_dist).to('cuda')\n",
+ "print(pe.network(torch.FloatTensor(s).to('cuda')))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([-0.0093, 0.0176], device='cuda:0', grad_fn=<AddBackward0>)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WL9Ow8iisF8I"
+ },
+ "source": [
+ "def reinforce(env, policy_estimator, num_episodes=30000,\n",
+ " batch_size=32, gamma=0.99, agent=None):\n",
+ " print('training...')\n",
+ " # Set up lists to hold results\n",
+ " total_rewards = []\n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Define optimizer\n",
+ " optimizer = optim.Adam(policy_estimator.network.parameters(), \n",
+ " lr=0.0001)\n",
+ " \n",
+ " action_space = np.arange(env.action_space.n)\n",
+ "\n",
+ " for ep in range(num_episodes):\n",
+ " start=0\n",
+ " s_0 = env.reset()\n",
+ " states = []\n",
+ " rewards = []\n",
+ " actions = []\n",
+ " complete = False\n",
+ "\n",
+ " while not complete:\n",
+ " # Get actions and convert to numpy array\n",
+ " if agent is not None:\n",
+ " dist=agent.dqn.dist(torch.tensor(s_0).float().to('cuda'))\n",
+ " action_probs = policy_estimator(dist).cpu().detach().numpy().flatten()\n",
+ " else:\n",
+ "\n",
+ " action_probs = policy_estimator(torch.tensor(s_0).float().to('cuda')).cpu().detach().numpy()\n",
+ " if np.any(np.isnan(action_probs)):\n",
+ "\n",
+ " print(action_probs)\n",
+ " print(s_0)\n",
+ " raise ValueError()\n",
+ " if action_probs[0]<0.01:\n",
+ " action_probs=np.array([0.01,0.99])\n",
+ " elif action_probs[1]<0.01:\n",
+ " action_probs=np.array([0.99,0.01])\n",
+ " \n",
+ " action = np.random.choice(action_space, p=action_probs)\n",
+ " s_1, r, complete, _ = env.step(action)\n",
+ " #print(s_0,action,r,s_1)\n",
+ " states.append(s_0)\n",
+ " rewards.append(r)\n",
+ " actions.append(action)\n",
+ " s_0 = s_1\n",
+ " start+=1\n",
+ " if start>1000:\n",
+ " print('\\n')\n",
+ " print(states)\n",
+ " print(action_probs)\n",
+ " raise ValueError()\n",
+ " # If complete, batch data\n",
+ " if complete:\n",
+ " \n",
+ " batch_rewards.extend(discount_rewards(rewards, gamma))\n",
+ " batch_states.extend(states)\n",
+ " batch_actions.extend(actions)\n",
+ " batch_counter += 1\n",
+ " total_rewards.append(sum(rewards))\n",
+ " \n",
+ " # If batch is complete, update network\n",
+ " if batch_counter == batch_size:\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " state_tensor = torch.FloatTensor(batch_states).to('cuda')\n",
+ " reward_tensor = torch.FloatTensor(batch_rewards).to('cuda')\n",
+ " # Actions are used as indices, must be LongTensor\n",
+ " action_tensor = torch.LongTensor(batch_actions).to('cuda')\n",
+ " \n",
+ " # Calculate loss\n",
+ " if agent is not None:\n",
+ " dist=agent.dqn.dist(state_tensor)\n",
+ " logprob = torch.log(policy_estimator(dist)) #for return dist\n",
+ " else:\n",
+ " logprob = torch.log(policy_estimator(state_tensor)) #for state input\n",
+ "\n",
+ " selected_logprobs= reward_tensor * torch.gather(logprob,1,action_tensor.unsqueeze(1)).flatten()\n",
+ " loss = -selected_logprobs.mean()\n",
+ " \n",
+ " # Calculate gradients\n",
+ " loss.backward()\n",
+ " # Apply gradients\n",
+ " optimizer.step()\n",
+ " \n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Print running average\n",
+ " print(\"\\rEp: {} Average of last 15: {:.2f}\".format(\n",
+ " ep + 1, np.mean(total_rewards[-15:])), end=\"\")\n",
+ " \n",
+ " return total_rewards"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 531
+ },
+ "id": "y8X6yw0mtk1p",
+ "outputId": "1f162a3b-443c-4b2d-8188-2ca1895412e3"
+ },
+ "source": [
+ "if return_dist:\n",
+ " rewards = reinforce(env, pe,agent=optimal_agent)\n",
+ "else:\n",
+ " rewards = reinforce(env, pe)\n",
+ "window = 10\n",
+ "smoothed_rewards = [np.mean(rewards[i-window:i+1]) if i > window \n",
+ " else np.mean(rewards[:i+1]) for i in range(len(rewards))]\n",
+ "\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(rewards)\n",
+ "plt.plot(smoothed_rewards)\n",
+ "plt.ylabel('Total Rewards')\n",
+ "plt.xlabel('Episodes')\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "training...\n",
+ "Ep: 30000 Average of last 15: -0.12"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 882
+ },
+ "id": "uc_gw_NowGzg",
+ "outputId": "17cb1783-5ca9-4dc8-d606-a938bec8a5fe"
+ },
+ "source": [
+ "#SET output = True to obtain policy, else it will return SC output, either 0 or 1\n",
+ "output=False\n",
+ "\n",
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol=np.ndarray([base_env.pos_max_x+1,base_env.pos_max_y+1],dtype=object)\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=0).argmax().cpu().item()]\n",
+ " dist=optimal_agent.dqn.dist(torch.tensor([i,j]).float().to('cuda'))\n",
+ " if not output:\n",
+ " if pe(dist).argmax().cpu().item()==0:\n",
+ " pol[i,j]=arrow_list[optimal_agent.greedy_action(torch.tensor([i,j]).float())]\n",
+ " else:\n",
+ " print(i,j,'risk')\n",
+ " pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float().to('cuda'),alpha=8).argmax().cpu().item()] \n",
+ " else:\n",
+ " if return_dist:\n",
+ "\n",
+ " pol[i,j]=pe(dist).argmax().cpu().item()\n",
+ " else:\n",
+ "\n",
+ " pol[i,j]=pe(torch.tensor([i,j]).float().to('cuda')).argmax().cpu().item()\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "t='D' if return_dist else 'State'\n",
+ "o='Output' if output else 'Policy'\n",
+ "title='Hierarchical '+ t+\"SC \"+o\n",
+ "if env.fn is not None:\n",
+ " title = title + r\" under $U=min(0,r)$\" \n",
+ "plt.title(title)\n",
+ "grid=np.zeros([base_env.pos_max_x+1,base_env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "0 0 risk\n",
+ "0 1 risk\n",
+ "0 2 risk\n",
+ "0 3 risk\n",
+ "1 0 risk\n",
+ "1 1 risk\n",
+ "1 2 risk\n",
+ "1 3 risk\n",
+ "2 0 risk\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "t5aD1U3vVXrk",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "ce2dccda-2e0d-4dda-df4b-1bd1a669e59c"
+ },
+ "source": [
+ "#combined state and distribution as input\n",
+ "\n",
+ "env=super_env(base_env,optimal_agent)\n",
+ "s = env.reset()\n",
+ "\n",
+ "pe = combined_policy_estimator(env).to('cuda')\n",
+ "s=torch.FloatTensor(s).to('cuda')\n",
+ "dist=optimal_agent.dqn.dist(s)\n",
+ "\n",
+ "print(pe(s,dist))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "tensor([[0.4965, 0.5035]], device='cuda:0', grad_fn=<SoftmaxBackward>)\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "j0-l7f3UmgW8"
+ },
+ "source": [
+ "def reinforce_combined(env, policy_estimator, agent, num_episodes=20000,\n",
+ " batch_size=32, gamma=0.99):\n",
+ " print('training...')\n",
+ " # Set up lists to hold results\n",
+ " total_rewards = []\n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Define optimizer\n",
+ " optimizer = optim.Adam(policy_estimator.network.parameters(), \n",
+ " lr=0.0002)\n",
+ " \n",
+ " action_space = np.arange(env.action_space.n)\n",
+ "\n",
+ " for ep in range(num_episodes):\n",
+ " start=0\n",
+ " s_0 = env.reset()\n",
+ " states = []\n",
+ " rewards = []\n",
+ " actions = []\n",
+ " complete = False\n",
+ "\n",
+ " while not complete:\n",
+ " # Get actions and convert to numpy array\n",
+ " \n",
+ " dist=agent.dqn.dist(torch.tensor(s_0).float().to('cuda'))\n",
+ " action_probs = policy_estimator(torch.tensor(s_0).float().to('cuda'),dist).cpu().detach().numpy().flatten()\n",
+ " if np.any(np.isnan(action_probs)):\n",
+ "\n",
+ " print(action_probs)\n",
+ " print(s_0)\n",
+ " raise ValueError()\n",
+ " if action_probs[0]<0.01:\n",
+ " action_probs=np.array([0.01,0.99])\n",
+ " elif action_probs[1]<0.01:\n",
+ " action_probs=np.array([0.99,0.01])\n",
+ " \n",
+ " action = np.random.choice(action_space, p=action_probs)\n",
+ " s_1, r, complete, _ = env.step(action)\n",
+ " #print(s_0,action,r,s_1)\n",
+ " states.append(s_0)\n",
+ " rewards.append(r)\n",
+ " actions.append(action)\n",
+ " s_0 = s_1\n",
+ " start+=1\n",
+ " if start>1000:\n",
+ " print('\\n')\n",
+ " print(states)\n",
+ " print(action_probs)\n",
+ " raise ValueError()\n",
+ " # If complete, batch data\n",
+ " if complete:\n",
+ " \n",
+ " batch_rewards.extend(discount_rewards(rewards, gamma))\n",
+ " batch_states.extend(states)\n",
+ " batch_actions.extend(actions)\n",
+ " batch_counter += 1\n",
+ " total_rewards.append(sum(rewards))\n",
+ " \n",
+ " # If batch is complete, update network\n",
+ " if batch_counter == batch_size:\n",
+ " \n",
+ " optimizer.zero_grad()\n",
+ " state_tensor = torch.FloatTensor(batch_states).to('cuda')\n",
+ " reward_tensor = torch.FloatTensor(batch_rewards).to('cuda')\n",
+ " # Actions are used as indices, must be LongTensor\n",
+ " action_tensor = torch.LongTensor(batch_actions).to('cuda')\n",
+ " \n",
+ " # Calculate loss\n",
+ " dist=agent.dqn.dist(state_tensor)\n",
+ " logprob = torch.log(policy_estimator(state_tensor,dist)) #for return dist\n",
+ "\n",
+ " selected_logprobs= reward_tensor * torch.gather(logprob,1,action_tensor.unsqueeze(1)).flatten()\n",
+ " loss = -selected_logprobs.mean()\n",
+ " \n",
+ " # Calculate gradients\n",
+ " loss.backward()\n",
+ " # Apply gradients\n",
+ " optimizer.step()\n",
+ " \n",
+ " batch_rewards = []\n",
+ " batch_actions = []\n",
+ " batch_states = []\n",
+ " batch_counter = 1\n",
+ " \n",
+ " # Print running average\n",
+ " print(\"\\rEp: {} Average of last 15: {:.2f}\".format(\n",
+ " ep + 1, np.mean(total_rewards[-15:])), end=\"\")\n",
+ " \n",
+ " return total_rewards"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 532
+ },
+ "id": "Sz-Lqc_pnTAY",
+ "outputId": "c30ae5a5-9f31-4926-c47d-c2212d456178"
+ },
+ "source": [
+ "\n",
+ "rewards = reinforce_combined(env, pe,agent=optimal_agent)\n",
+ "window = 10\n",
+ "smoothed_rewards = [np.mean(rewards[i-window:i+1]) if i > window \n",
+ " else np.mean(rewards[:i+1]) for i in range(len(rewards))]\n",
+ "\n",
+ "plt.figure(figsize=(12,8))\n",
+ "plt.plot(rewards)\n",
+ "plt.plot(smoothed_rewards)\n",
+ "plt.ylabel('Total Rewards')\n",
+ "plt.xlabel('Episodes')\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "training...\n",
+ "Ep: 20000 Average of last 15: 0.88"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 864x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "pzuWskcRnZao",
+ "outputId": "70fe7883-c823-4922-85c2-968c9dd677dc"
+ },
+ "source": [
+ "#output = True for policy, else it will return SC output, either 0 or 1\n",
+ "output=True\n",
+ "\n",
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol=np.ndarray([base_env.pos_max_x+1,base_env.pos_max_y+1],dtype=object)\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " #pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float(),alpha=0).argmax().cpu().item()]\n",
+ " dist=optimal_agent.dqn.dist(torch.tensor([i,j]).float().to('cuda'))\n",
+ " if not output:\n",
+ " if pe(dist).argmax().cpu().item()==0:\n",
+ " pol[i,j]=arrow_list[optimal_agent.greedy_action(torch.tensor([i,j]).float())]\n",
+ " else:\n",
+ " print(i,j,'risk')\n",
+ " pol[i,j]=arrow_list[optimal_agent.dqn.risk_action(torch.tensor([i,j]).float().to('cuda'),alpha=8).argmax().cpu().item()] \n",
+ " else:\n",
+ " pol[i,j]=pe(torch.tensor([i,j]).float().to('cuda'),dist).argmax().cpu().item()\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "o='Output' if output else 'Policy'\n",
+ "title=\"SDSC \"+o\n",
+ "if env.fn is not None:\n",
+ " title = title + r\" under $U$\" \n",
+ "plt.title(title)\n",
+ "grid=np.zeros([base_env.pos_max_x+1,base_env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "5gIM77FVoegL"
+ },
+ "source": [
+ ""
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
\ No newline at end of file
diff --git a/code/safety_classifier_investigation.ipynb b/code/safety_classifier_investigation.ipynb
new file mode 100644
index 0000000..01e638a
--- /dev/null
+++ b/code/safety_classifier_investigation.ipynb
@@ -0,0 +1,3021 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.9"
+ },
+ "colab": {
+ "name": "safety classifier investigation.ipynb",
+ "provenance": [],
+ "collapsed_sections": [
+ "Ax_tMYQiHvmT"
+ ]
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "mptOyMKizAHW"
+ },
+ "source": [
+ "import torch"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "olE-mblK1og3",
+ "outputId": "6bcfa269-d55e-47d3-e92c-2284872e1a10"
+ },
+ "source": [
+ "from google.colab import drive\n",
+ "drive.mount(\"/content/drive\", force_remount=True)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mounted at /content/drive\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "T66_VR9i1qhu"
+ },
+ "source": [
+ "import sys\n",
+ "sys.path.append('/content/drive/My Drive/indiv')"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "iY5kHbla1wQI",
+ "outputId": "9a00917e-5c07-4ea3-8723-b57309ff53d5"
+ },
+ "source": [
+ "cd /content/drive/My Drive/indiv"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "/content/drive/My Drive/indiv\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Ax_tMYQiHvmT"
+ },
+ "source": [
+ "## Configurations for Colab"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oCtNap9Lkmi2"
+ },
+ "source": [
+ "import os\n",
+ "from typing import Dict, List, Tuple\n",
+ "\n",
+ "import gym\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import torch\n",
+ "import torch.nn as nn\n",
+ "import torch.nn.functional as F\n",
+ "import torch.optim as optim\n",
+ "from IPython.display import clear_output"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "NmvdzO0Rmab1"
+ },
+ "source": [
+ "## Environments"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "U4LL8VVQmz5L"
+ },
+ "source": [
+ "from gym import spaces\n",
+ "class cliff_walking():\n",
+ " def __init__(self,start_pos=[3,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[2,9],pos_max=[4,12],reward_region=9):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " \n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[2] #go down\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a] \n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==3 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "IKz7cJfYo5e-"
+ },
+ "source": [
+ "class inv_cliff_walking():\n",
+ " def __init__(self,start_pos=[0,0],goal=[3,9],e=0.1,windy_x=[0,3],windy_y=[1,5],pos_max=[4,9],reward_region=5):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ " self.start_pos=np.array(start_pos)\n",
+ " self.pos=np.array(start_pos)\n",
+ " self.pos_max_x=pos_max[0]-1\n",
+ " self.pos_max_y=pos_max[1]-1\n",
+ " self.goal=np.array(goal)\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(self.pos_max_x+1),\n",
+ " spaces.Discrete(self.pos_max_y+1))) \n",
+ " self.windy_x=windy_x\n",
+ " self.windy_y=windy_y\n",
+ " self.e=e\n",
+ " self.reward_region=reward_region\n",
+ " def reset(self):\n",
+ " self.pos=self.start_pos\n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[0]<=self.windy_x[1] and pos[0]>=self.windy_x[0] and pos[1]<=self.windy_y[1] and pos[1]>=self.windy_y[0]:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ " \n",
+ " if self.is_windy(self.pos) and np.random.random()<self.e: \n",
+ " next_pos=self.pos+self.d[0] #go up\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a] \n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if pos[0]==0 and pos[1]>0:\n",
+ " isdone=True\n",
+ " if pos[1]>self.reward_region:\n",
+ " reward=1\n",
+ " else:\n",
+ " reward=-1\n",
+ " else:\n",
+ " reward=-0.01\n",
+ " isdone=False\n",
+ " return reward, isdone\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, _\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " torch.manual_seed(seed)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "sVrs7hkH_ioo"
+ },
+ "source": [
+ "class modified_cliff_walking():\n",
+ " \n",
+ " def __init__(self):\n",
+ " self.d_names={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'} \n",
+ "\n",
+ " self.d={0:np.array([-1,0]),\n",
+ " 1:np.array([0,1]),\n",
+ " 2:np.array([1,0]),\n",
+ " 3:np.array([0,-1])} \n",
+ "# self.start_pos=np.array([np.random.randint(4),0])\n",
+ " self.start_pos=np.array([np.random.randint(5),0]) \n",
+ " self.pos=np.array(self.start_pos)\n",
+ " self.pos_max_x=4\n",
+ " self.pos_max_y=4\n",
+ " self.action_space = spaces.Discrete(4)\n",
+ " self.observation_space = spaces.Tuple((\n",
+ " spaces.Discrete(5),\n",
+ " spaces.Discrete(5))) \n",
+ " self.e=0.1\n",
+ " def reset(self):\n",
+ " self.pos=np.array([np.random.randint(5),0]) \n",
+ " return self.pos\n",
+ " def is_windy(self,pos):\n",
+ " if pos[1]<=3 and pos[1]>=1:\n",
+ " return True\n",
+ " else:\n",
+ " return False\n",
+ " def is_tzone(self,pos):\n",
+ " return False\n",
+ " def is_cliff(self,pos):\n",
+ " if pos[0]==2 and pos[1]>=1 and pos[1]<=3:\n",
+ " return True\n",
+ " return False\n",
+ " def is_goal(self,pos):\n",
+ " if pos[1]==4 and pos[0]==2:\n",
+ " return True\n",
+ " return False\n",
+ "\n",
+ " def next_pos(self,a):\n",
+ "\n",
+ " if self.is_windy(self.pos): #if windy\n",
+ " if self.pos[0]<2 and np.random.random()<0.1: #if above cliff\n",
+ " next_pos=self.pos+self.d[2] \n",
+ " elif self.pos[0]>2 and np.random.random()<self.e: #below cliff and windy\n",
+ " next_pos=self.pos+self.d[0] \n",
+ " else: #not above cliff and no wind\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " else:\n",
+ " next_pos=self.pos+self.d[a]\n",
+ " \n",
+ " if next_pos[0]<0:\n",
+ " next_pos[0]=0\n",
+ " if next_pos[0]>self.pos_max_x:\n",
+ " next_pos[0]=self.pos_max_x \n",
+ " if next_pos[1]<0:\n",
+ " next_pos[1]=0\n",
+ " if next_pos[1]>self.pos_max_y:\n",
+ " next_pos[1]=self.pos_max_y \n",
+ " return next_pos\n",
+ "\n",
+ " def reward_terminal(self,pos):\n",
+ " assert pos[0]<=self.pos_max_x\n",
+ " assert pos[1]<=self.pos_max_y\n",
+ " if self.is_cliff(pos):\n",
+ " return -1, True\n",
+ " elif self.is_goal(pos):\n",
+ " return 1, True\n",
+ " elif self.is_tzone(pos):\n",
+ " return 0, True\n",
+ " return -0.01, False\n",
+ " def step(self,a):\n",
+ " a=int(a)\n",
+ " self.pos=self.next_pos(a)\n",
+ " reward,isdone=self.reward_terminal(self.pos)\n",
+ " return self.pos,reward, isdone, None\n",
+ " def render(self,*args,**kwargs):\n",
+ " import pandas as pd\n",
+ " a=np.ndarray([self.pos_max_x+1,self.pos_max_y+1],dtype=object)\n",
+ " a.fill(' ')\n",
+ " a[-1,self.reward_region:]='T'\n",
+ " a[tuple(self.pos)]='X'\n",
+ " print(pd.DataFrame(a))\n",
+ " def close(self):\n",
+ " pass\n",
+ " def seed(self,seed):\n",
+ " np.random.seed(seed)\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0dfLETtaKc31"
+ },
+ "source": [
+ "import easy_cliffwalking"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "mB8y6aefHvmg"
+ },
+ "source": [
+ "## Replay buffer\n",
+ "\n",
+ "Please see *01.dqn.ipynb* for detailed description."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "EhVTDn4mHvmh"
+ },
+ "source": [
+ "class ReplayBuffer:\n",
+ " \"\"\"A simple numpy replay buffer.\"\"\"\n",
+ "\n",
+ " def __init__(self, obs_dim: int, size: int, batch_size: int = 32):\n",
+ " self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.next_obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
+ " self.acts_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.rews_buf = np.zeros([size], dtype=np.float32)\n",
+ " self.done_buf = np.zeros(size, dtype=np.float32)\n",
+ " self.max_size, self.batch_size = size, batch_size\n",
+ " self.ptr, self.size, = 0, 0\n",
+ "\n",
+ " def store(\n",
+ " self, \n",
+ " obs: np.ndarray, \n",
+ " act: np.ndarray, \n",
+ " rew: float, \n",
+ " next_obs: np.ndarray, \n",
+ " done: bool,\n",
+ " ):\n",
+ " #try:\n",
+ " self.obs_buf[self.ptr] = obs\n",
+ " self.next_obs_buf[self.ptr] = next_obs\n",
+ " self.acts_buf[self.ptr] = act\n",
+ " self.rews_buf[self.ptr] = rew\n",
+ " self.done_buf[self.ptr] = done\n",
+ " self.ptr = (self.ptr + 1) % self.max_size\n",
+ " self.size = min(self.size + 1, self.max_size)\n",
+ "\n",
+ " def sample_batch(self) -> Dict[str, np.ndarray]:\n",
+ " idxs = np.random.choice(self.size, size=self.batch_size, replace=False)\n",
+ " return dict(obs=self.obs_buf[idxs],\n",
+ " next_obs=self.next_obs_buf[idxs],\n",
+ " acts=self.acts_buf[idxs],\n",
+ " rews=self.rews_buf[idxs],\n",
+ " done=self.done_buf[idxs])\n",
+ "\n",
+ " def __len__(self) -> int:\n",
+ " return self.size"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xBE-ev78Hvmi"
+ },
+ "source": [
+ "## Network\n",
+ "\n",
+ "The parametrized distribution can be represented by a neural network, as in DQN, but with atom_size x out_dim outputs. A softmax is applied independently for each action dimension of the output to ensure that the distribution for each action is appropriately normalized.\n",
+ "\n",
+ "To estimate q-values, we use inner product of each action's softmax distribution and support which is the set of atoms $\\{z_i = V_{min} + i\\Delta z: 0 \\le i < N\\}, \\Delta z = \\frac{V_{max} - V_{min}}{N-1}$.\n",
+ "\n",
+ "$$\n",
+ "Q(s_t, a_t) = \\sum_i z_i p_i(s_t, a_t), \\\\\n",
+ "\\text{where } p_i \\text{ is the probability of } z_i \\text{ (the output of softmax)}.\n",
+ "$$"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "kpyPL_8MHvmi"
+ },
+ "source": [
+ "class Network(nn.Module):\n",
+ " def __init__(\n",
+ " self, \n",
+ " in_dim: int, \n",
+ " out_dim: int, \n",
+ " atom_size: int, \n",
+ " support: torch.Tensor,\n",
+ " size=300,\n",
+ " ):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(Network, self).__init__()\n",
+ "\n",
+ " self.support = support\n",
+ " self.out_dim = out_dim\n",
+ " self.atom_size = atom_size\n",
+ " \n",
+ " self.layers = nn.Sequential(\n",
+ " nn.Linear(in_dim, size), \n",
+ " nn.ReLU(),\n",
+ " nn.Linear(size, size), \n",
+ " nn.ReLU(), \n",
+ " nn.Linear(size, out_dim * atom_size)\n",
+ " )\n",
+ "\n",
+ " def forward(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Forward method implementation.\"\"\"\n",
+ " \n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " return q\n",
+ " def var_action(self, x: torch.Tensor,alpha=1) -> torch.Tensor:\n",
+ " dist = self.dist(x)\n",
+ " q = torch.sum(dist * self.support, dim=2)\n",
+ " var=self.var(dist,q)\n",
+ " return q-alpha*var \n",
+ " def var(self,dist,q):\n",
+ " cm=(self.support.reshape(51,1)-q)**2\n",
+ " var=torch.sum(dist * cm.T,dim=-1)\n",
+ " return var\n",
+ " def dist(self, x: torch.Tensor) -> torch.Tensor:\n",
+ " \"\"\"Get distribution for atoms.\"\"\"\n",
+ " \n",
+ " q_atoms = self.layers(x).view(-1, self.out_dim, self.atom_size)\n",
+ " dist = F.softmax(q_atoms, dim=-1)\n",
+ " dist = dist.clamp(min=1e-3) # for avoiding nans\n",
+ " \n",
+ " return dist"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "CD2PH24IHvmj"
+ },
+ "source": [
+ "## Categorical DQN Agent\n",
+ "\n",
+ "\n",
+ "Here is a summary of DQNAgent class.\n",
+ "\n",
+ "| Method | Note |\n",
+ "| --- | --- |\n",
+ "|select_action | select an action from the input state. |\n",
+ "|step | take an action and return the response of the env. |\n",
+ "|compute_dqn_loss | return dqn loss. |\n",
+ "|update_model | update the model by gradient descent. |\n",
+ "|target_hard_update| hard update from the local model to the target model.|\n",
+ "|train | train the agent during num_frames. |\n",
+ "|test | test the agent (1 episode). |\n",
+ "|plot | plot the training progresses. |\n",
+ "\n",
+ "All differences from pure DQN are noted with comments *Categorical DQN*."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "xbOiVo-NHvmj"
+ },
+ "source": [
+ "class DQNAgent:\n",
+ " \"\"\"DQN Agent interacting with environment.\n",
+ " \n",
+ " Attribute:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory (ReplayBuffer): replay memory to store transitions\n",
+ " batch_size (int): batch size for sampling\n",
+ " epsilon (float): parameter for epsilon greedy policy\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " target_update (int): period for target model's hard update\n",
+ " gamma (float): discount factor\n",
+ " dqn (Network): model to train and select actions\n",
+ " dqn_target (Network): target model to update\n",
+ " optimizer (torch.optim): optimizer for training dqn\n",
+ " transition (list): transition information including\n",
+ " state, action, reward, next_state, done\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " support (torch.Tensor): support for categorical dqn\n",
+ " \"\"\"\n",
+ "\n",
+ " def __init__(\n",
+ " self, \n",
+ " env: gym.Env,\n",
+ " memory_size: int,\n",
+ " batch_size: int,\n",
+ " target_update: int,\n",
+ " epsilon_decay: float,\n",
+ " max_epsilon: float = 1.0,\n",
+ " min_epsilon: float = 0.10,\n",
+ " gamma: float = 0.95, \n",
+ " # Categorical DQN parameters\n",
+ " v_min: float = -2,\n",
+ " v_max: float = 2,\n",
+ " atom_size: int = 51,\n",
+ " size=300,\n",
+ " ):\n",
+ " \"\"\"Initialization.\n",
+ " \n",
+ " Args:\n",
+ " env (gym.Env): openAI Gym environment\n",
+ " memory_size (int): length of memory\n",
+ " batch_size (int): batch size for sampling\n",
+ " target_update (int): period for target model's hard update\n",
+ " epsilon_decay (float): step size to decrease epsilon\n",
+ " lr (float): learning rate\n",
+ " max_epsilon (float): max value of epsilon\n",
+ " min_epsilon (float): min value of epsilon\n",
+ " gamma (float): discount factor\n",
+ " v_min (float): min value of support\n",
+ " v_max (float): max value of support\n",
+ " atom_size (int): the unit number of support\n",
+ " \"\"\"\n",
+ " obs_dim =2# env.observation_space.shape[0]\n",
+ " action_dim = env.action_space.n\n",
+ " \n",
+ " self.env = env\n",
+ " self.memory = ReplayBuffer(obs_dim, memory_size, batch_size)\n",
+ " self.batch_size = batch_size\n",
+ " self.epsilon = max_epsilon\n",
+ " self.epsilon_decay = epsilon_decay\n",
+ " self.max_epsilon = max_epsilon\n",
+ " self.min_epsilon = min_epsilon\n",
+ " self.target_update = target_update\n",
+ " self.gamma = gamma\n",
+ " \n",
+ " # device: cpu / gpu\n",
+ " self.device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+ " print(self.device)\n",
+ " \n",
+ " # Categorical DQN parameters\n",
+ " self.v_min = v_min\n",
+ " self.v_max = v_max\n",
+ " self.atom_size = atom_size\n",
+ " self.support = torch.linspace(\n",
+ " self.v_min, self.v_max, self.atom_size\n",
+ " ).to(self.device)\n",
+ "\n",
+ " # networks: dqn, dqn_target\n",
+ " self.dqn = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support,size=size,\n",
+ " ).to(self.device)\n",
+ " self.dqn_target = Network(\n",
+ " obs_dim, action_dim, atom_size, self.support,size=size,\n",
+ " ).to(self.device)\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " self.dqn_target.eval()\n",
+ " \n",
+ " # optimizer\n",
+ " self.optimizer = optim.Adam(self.dqn.parameters(),lr=0.0001)\n",
+ "\n",
+ " # transition to store in memory\n",
+ " self.transition = list()\n",
+ " \n",
+ " # mode: train / test\n",
+ " self.is_test = False\n",
+ "\n",
+ " def select_action(self, state: np.ndarray) -> np.ndarray:\n",
+ " \"\"\"Select an action from the input state.\"\"\"\n",
+ " # epsilon greedy policy\n",
+ " if self.epsilon > np.random.random():\n",
+ " selected_action = self.env.action_space.sample()\n",
+ " else:\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy()\n",
+ " \n",
+ " if not self.is_test:\n",
+ " self.transition = [state, selected_action]\n",
+ " \n",
+ " return selected_action\n",
+ " def greedy_action(self,state):\n",
+ " selected_action = self.dqn(\n",
+ " torch.FloatTensor(state).to(self.device)\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ " \n",
+ " def risk_action(self,state,alpha=1):\n",
+ " selected_action = self.dqn.risk_action(\n",
+ " torch.FloatTensor(state).to(self.device),alpha=alpha,\n",
+ " ).argmax()\n",
+ " selected_action = selected_action.detach().cpu().numpy() \n",
+ " return selected_action \n",
+ "\n",
+ "\n",
+ "\n",
+ " def split(self,next_state):\n",
+ " if len(str(next_state))==1:\n",
+ " next_state=torch.tensor([0,int(str(next_state)[0])]).float()#split\n",
+ " else:\n",
+ " next_state=torch.tensor([int(str(next_state)[0]),int(str(next_state)[1])]).float() #split\n",
+ " return next_state\n",
+ "\n",
+ " def step(self, action: np.ndarray) -> Tuple[np.ndarray, np.float64, bool]:\n",
+ " \"\"\"Take an action and return the response of the env.\"\"\"\n",
+ " \n",
+ " next_state, reward, done, _ = self.env.step(int(action))\n",
+ "\n",
+ " if not self.is_test:\n",
+ " self.transition += [reward, next_state, done]\n",
+ " self.memory.store(*self.transition)\n",
+ " \n",
+ " return next_state, reward, done\n",
+ "\n",
+ " def update_model(self) -> torch.Tensor:\n",
+ " \"\"\"Update the model by gradient descent.\"\"\"\n",
+ " samples = self.memory.sample_batch()\n",
+ "\n",
+ " loss = self._compute_dqn_loss(samples)\n",
+ "\n",
+ " self.optimizer.zero_grad()\n",
+ " loss.backward()\n",
+ " self.optimizer.step()\n",
+ "\n",
+ " return loss.item()\n",
+ " \n",
+ " def train(self, num_frames: int, plotting_interval: int = 500):\n",
+ " \"\"\"Train the agent.\"\"\"\n",
+ " self.is_test = False\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " update_cnt = 0\n",
+ " epsilons = []\n",
+ " losses = []\n",
+ " scores = []\n",
+ " score = 0\n",
+ "\n",
+ " for frame_idx in range(1, num_frames + 1):\n",
+ " \n",
+ " action = self.select_action(state)\n",
+ "\n",
+ " next_state, reward, done = self.step(action) \n",
+ " state = next_state\n",
+ " \n",
+ " score += reward\n",
+ "\n",
+ " # if episode ends\n",
+ " if done:\n",
+ " state = self.env.reset()\n",
+ "\n",
+ " scores.append(score)\n",
+ " score = 0\n",
+ " #if reward>=0:\n",
+ " #self.epsilon=max(self.min_epsilon,self.epsilon*0.993)\n",
+ " # if training is ready\n",
+ " if len(self.memory) >= self.batch_size:\n",
+ " loss = self.update_model()\n",
+ " losses.append(loss)\n",
+ " update_cnt += 1\n",
+ " \n",
+ " # linearly decrease epsilon\n",
+ " self.epsilon = max(self.min_epsilon, self.epsilon - (self.max_epsilon - self.min_epsilon) * self.epsilon_decay)\n",
+ " epsilons.append(self.epsilon)\n",
+ " \n",
+ " # if hard update is needed\n",
+ " if update_cnt % self.target_update == 0:\n",
+ " self._target_hard_update()\n",
+ " \n",
+ " # plotting\n",
+ " if frame_idx % plotting_interval == 0:\n",
+ " self._plot(frame_idx, scores, losses, epsilons)\n",
+ " \n",
+ " self.env.close()\n",
+ " \n",
+ " def test(self) -> List[np.ndarray]:\n",
+ " \"\"\"Test the agent.\"\"\"\n",
+ " self.is_test = True\n",
+ " \n",
+ " state = self.env.reset()\n",
+ " done = False\n",
+ " score = 0\n",
+ " \n",
+ " frames = []\n",
+ " while not done:\n",
+ " frames.append(self.env.render(mode=\"rgb_array\"))\n",
+ "\n",
+ " action = self.select_action(state)\n",
+ " next_state, reward, done = self.step(action)\n",
+ "\n",
+ " state = next_state\n",
+ " score += reward\n",
+ " \n",
+ " print(\"score: \", score)\n",
+ " self.env.close()\n",
+ " \n",
+ " return frames\n",
+ "\n",
+ "\n",
+ " def _compute_dqn_loss(self, samples: Dict[str, np.ndarray]) -> torch.Tensor:\n",
+ " \"\"\"Return categorical dqn loss.\"\"\"\n",
+ " device = self.device # for shortening the following lines\n",
+ " state = torch.FloatTensor(samples[\"obs\"]).to(device)\n",
+ " next_state = torch.FloatTensor(samples[\"next_obs\"]).to(device)\n",
+ " action = torch.LongTensor(samples[\"acts\"]).to(device)\n",
+ " reward = torch.FloatTensor(samples[\"rews\"].reshape(-1, 1)).to(device)\n",
+ " done = torch.FloatTensor(samples[\"done\"].reshape(-1, 1)).to(device)\n",
+ " #print(samples)\n",
+ " # Categorical DQN algorithm\n",
+ " delta_z = float(self.v_max - self.v_min) / (self.atom_size - 1)\n",
+ " #print('dz',delta_z.shape)\n",
+ " with torch.no_grad():\n",
+ " next_action = self.dqn_target(next_state).argmax(1)\n",
+ " #print('next_action',next_action)\n",
+ " next_dist = self.dqn_target.dist(next_state)\n",
+ " #print('next dist',next_dist)\n",
+ " next_dist = next_dist[range(self.batch_size), next_action]\n",
+ " #print('final_dist',next_dist)\n",
+ " t_z = reward + (1 - done) * self.gamma * self.support\n",
+ " t_z = t_z.clamp(min=self.v_min, max=self.v_max)\n",
+ " b = (t_z - self.v_min) / delta_z\n",
+ " l = b.floor().long()\n",
+ " u = b.ceil().long()\n",
+ "\n",
+ " offset = (\n",
+ " torch.linspace(\n",
+ " 0, (self.batch_size - 1) * self.atom_size, self.batch_size\n",
+ " ).long()\n",
+ " .unsqueeze(1)\n",
+ " .expand(self.batch_size, self.atom_size)\n",
+ " .to(self.device)\n",
+ " )\n",
+ "\n",
+ " proj_dist = torch.zeros(next_dist.size(), device=self.device)\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (l + offset).view(-1), (next_dist * (u.float() - b)).view(-1)\n",
+ " )\n",
+ " proj_dist.view(-1).index_add_(\n",
+ " 0, (u + offset).view(-1), (next_dist * (b - l.float())).view(-1)\n",
+ " )\n",
+ "\n",
+ " dist = self.dqn.dist(state)\n",
+ " log_p = torch.log(dist[range(self.batch_size), action])\n",
+ "\n",
+ " loss = -(proj_dist * log_p).sum(1).mean()\n",
+ " #print('loss shape',loss.shape)\n",
+ "\n",
+ " return loss\n",
+ "\n",
+ " def _target_hard_update(self):\n",
+ " \"\"\"Hard update: target <- local.\"\"\"\n",
+ " self.dqn_target.load_state_dict(self.dqn.state_dict())\n",
+ " \n",
+ " def _plot(\n",
+ " self, \n",
+ " frame_idx: int, \n",
+ " scores: List[float], \n",
+ " losses: List[float], \n",
+ " epsilons: List[float],\n",
+ " ):\n",
+ " \"\"\"Plot the training progresses.\"\"\"\n",
+ " clear_output(True)\n",
+ " plt.figure(figsize=(20, 5))\n",
+ " plt.subplot(131)\n",
+ " plt.title('frame %s. score: %s' % (frame_idx, np.mean(scores[-10:])))\n",
+ " plt.plot(scores)\n",
+ " plt.subplot(132)\n",
+ " plt.title('loss')\n",
+ " plt.plot(losses)\n",
+ " plt.subplot(133)\n",
+ " plt.title('epsilons')\n",
+ " plt.plot(epsilons)\n",
+ " plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "dzTxEC3cHvmn"
+ },
+ "source": [
+ "## Initialize"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "JB9OrdL-vXOu"
+ },
+ "source": [
+ "Creating reference policy"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wY5md-bWHvml"
+ },
+ "source": [
+ "seed = 777\n",
+ "\n",
+ "def seed_torch(seed):\n",
+ " torch.manual_seed(seed)\n",
+ " torch.cuda.manual_seed(seed)\n",
+ " if torch.backends.cudnn.enabled:\n",
+ " torch.backends.cudnn.benchmark = False\n",
+ " torch.backends.cudnn.deterministic = True\n",
+ "\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "try:\n",
+ " env.seed(seed)\n",
+ "except:\n",
+ " pass\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "aChSfk4uHvmn",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "96d796c7-c7ba-4f32-e33c-2a3c0659c5a8"
+ },
+ "source": [
+ "seed = 777\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "env=cliff_walking(e=0.1,pos_max=[4,9],reward_region=5,windy_y=[1,5])\n",
+ "env.seed(seed)\n",
+ "# parameters\n",
+ "num_frames = 55000\n",
+ "memory_size = 5000\n",
+ "batch_size = 32\n",
+ "target_update = 500\n",
+ "epsilon_decay = 1 / 45000\n",
+ "\n",
+ "# train\n",
+ "agent = DQNAgent(env, memory_size, batch_size, target_update, epsilon_decay,max_epsilon=0.7)\n",
+ "agent.dqn.load_state_dict(torch.load('base_c51.pt',map_location=torch.device('cpu')))\n",
+ "#agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 15
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Dsnqf7uw4UrS",
+ "outputId": "17c99aa7-25b6-4c58-8ff6-cf695436f3d1"
+ },
+ "source": [
+ "seed = 777\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "inv_env=inv_cliff_walking()\n",
+ "inv_env.seed(seed)\n",
+ "# parameters\n",
+ "num_frames = 50000\n",
+ "memory_size = 5000\n",
+ "batch_size = 32\n",
+ "target_update = 500\n",
+ "epsilon_decay = 1 / 45000\n",
+ "\n",
+ "# train\n",
+ "\n",
+ "inv_agent = DQNAgent(inv_env, memory_size, batch_size, target_update, epsilon_decay,max_epsilon=0.6)\n",
+ "inv_agent.dqn.load_state_dict(torch.load('inv_agent.pt'))\n",
+ "#inv_agent.train(num_frames)"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 154
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "zcwOjstJqeEs",
+ "outputId": "4892b8ed-9069-4fbd-e291-9c134b4db82d"
+ },
+ "source": [
+ "seed = 77\n",
+ "np.random.seed(seed)\n",
+ "seed_torch(seed)\n",
+ "m_env=modified_cliff_walking()\n",
+ "m_env.seed(seed)\n",
+ "# parameters\n",
+ "num_frames = 400000\n",
+ "memory_size = 20000\n",
+ "batch_size = 32\n",
+ "target_update = 200\n",
+ "epsilon_decay = 1 / 350000\n",
+ "\n",
+ "# train\n",
+ "m_agent = DQNAgent(m_env, memory_size, batch_size, target_update, epsilon_decay,size=250)\n",
+ "m_agent.dqn.load_state_dict(torch.load('mcw_final2.pt'))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "cuda\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "<All keys matched successfully>"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 16
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "gn22Irb-riUX",
+ "outputId": "a7aebb91-a54f-4cb0-d86c-1a37a3139af1"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(pol.shape[0]):\n",
+ " for j in range(pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol[i,j]=d[agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('Policy at each step')\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:11]=1\n",
+ "grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "ajA_styEqTd5",
+ "outputId": "b7e234a3-b0d3-4764-b6d7-90775bf6d89f"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "inv_pol=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(inv_pol.shape[0]):\n",
+ " for j in range(inv_pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " inv_pol[i,j]=d[inv_agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('Inverted Policy at each step')\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[0,1:inv_env.reward_region+1]=1\n",
+ "grid[0,inv_env.reward_region+1:]=-1\n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, inv_pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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5yfVV9cThosOTmMx27nttnP4/Ka5L8pyq2mn4t+PhGcwb1YUF4y6gF621tVX15iRfTDI/yQdba5eOuSxmUFUfT3JYkj2ranmSd7XWTh5vVWzG85IcleS7w2vKk+T3W2tnjLEmZrZvklOGMyfPS/Kp1ppby8Ho7JPks4O/cbMgycdaa/8y3pLYAm9JcurwS6Nrkvz6mOthBsNw7SVJfmvctbB5rbXzq+q0JBcmWZvk20lOGm9VW4/bAAIAAEAHXAIAAAAAHRAAAAAAQAcEAAAAANABAQAAAAB0QAAAAAAAHRAAAAAAQAcEAAAAANABAQAAAAB04P8H5weJGxMHBE4AAAAASUVORK5CYII=\n",
+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "JWiT4QGj8E9U",
+ "outputId": "e9d6c15f-0c71-4bd5-f4a4-c7b0cfd80637"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol1.shape[0]):\n",
+ " for j in range(pol1.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol1[i,j]=arrow_list[inv_agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ " #pol2[i,j]=arrow_list[inv_agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=4.5).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Inverted Windy CW c51 Policy\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[0,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==0 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==0 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol1[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 499
+ },
+ "id": "qUIA4-lSnyE3",
+ "outputId": "0a2383b5-c070-496c-c51b-f01ac9fa82e9"
+ },
+ "source": [
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "m_pol=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "for i in range(m_pol.shape[0]):\n",
+ " for j in range(m_pol.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " m_pol[i,j]=d[m_agent.greedy_action(np.array([i,j])).item()]\n",
+ "# m_pol[i,j]=d[m_agent.risk_action(np.array([i,j])).item()] \n",
+ "plt.figure(figsize=[21,8])\n",
+ "plt.title('M Agent Policy at each step')\n",
+ "grid=np.zeros([m_env.pos_max_x+1,m_env.pos_max_y+1])\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " if m_env.is_goal([i,j]):\n",
+ " grid[i,j]=-1\n",
+ " if m_env.is_cliff([i,j]):\n",
+ " grid[i,j]=2\n",
+ " if m_env.is_tzone([i,j]):\n",
+ " grid[i,j]=1 \n",
+ "plt.imshow(grid)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='extra bold' if m_env.is_windy([i,j]) else 'normal'\n",
+ " text = plt.text(j, i, m_pol[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": "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\n",
+ "text/plain": [
+ "<Figure size 1512x576 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 890
+ },
+ "id": "lftNokbtQ0qF",
+ "outputId": "d9452e51-9e2f-4e06-daaf-9c643ce33736"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "grid=np.zeros([m_env.pos_max_x+1,m_env.pos_max_y+1])-1\n",
+ "grid[2,1:4]=1\n",
+ "ax = plt.gca()\n",
+ "plt.title('Modified Windy Cliff-Walking')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='bold'#'extra bold'\n",
+ " if (i==2 and j>0 and j<4):\n",
+ " plt.text(j, i,'C' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ " elif j>0 and j<4:\n",
+ " plt.text(j, i,'W' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==2 and j==0:\n",
+ " plt.text(j, i,'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==2 and j==4:\n",
+ " plt.text(j, i,'G' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ "\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.set_xticks([0,1,2,3,4])\n",
+ "ax.set_yticks([0,1,2,3,4]) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 890
+ },
+ "id": "aP9kyfScS8wU",
+ "outputId": "a74e3798-ef0d-4f78-9950-700d997d704a"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "for i in range(pol1.shape[0]):\n",
+ " for j in range(pol1.shape[1]):\n",
+ " with torch.no_grad():\n",
+ " pol1[i,j]=arrow_list[m_agent.dqn(torch.tensor([i,j]).float().to(\"cuda\")).argmax().cpu().item()]\n",
+ " #pol2[i,j]=arrow_list[inv_agent.dqn.risk_action(torch.tensor([i,j]).float().to(\"cuda\"),alpha=4.5).argmax().cpu().item()]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"Modified Windy CW c51 Policy\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([m_env.pos_max_x+1,m_env.pos_max_y+1])\n",
+ "grid[2,1:4]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==2 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==2 and j==4:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, pol1[i,j],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4])\n",
+ "ax.set_yticks([0,1,2,3,4]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "D5M8tRNw-RDF",
+ "outputId": "2a238085-0cc5-42b6-f1e5-f39410f0bca6"
+ },
+ "source": [
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "grid=np.zeros([e_env.pos_max_x+1,e_env.pos_max_y+1])-1\n",
+ "grid[0,1:6]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "ax = plt.gca()\n",
+ "plt.title('Inverted Windy Cliff-Walking')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " f='bold'#'extra bold'\n",
+ " if i==0 and j>0 and j<6:\n",
+ " plt.text(j, i,'C' ,\n",
+ " ha=\"center\", va=\"center\", color=\"w\",fontweight=f)\n",
+ " elif j>0 and j<6:\n",
+ " plt.text(j, i,'W' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==0 and j==0:\n",
+ " plt.text(j, i,'S' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " elif i==0 and j>5:\n",
+ " plt.text(j, i,'G' ,\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight=f) \n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ "\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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rfDfPSs67Q9L+cQXnnagwex4A0Ns0O5cOhUKhUCjdLapvjvkNBf//lKS3lTl2lEKwNH3uuTnOm5WT+59q+Nl3U3EO0xtzHDcl4/yzFVJulMxzrRAg+KKK87SukDSmhve7MNfzAknHljm2r6R+OX73p3dzPH0to83nFG4M9C1zXGeAcW3q2I15frcKf6hvyDj3b8q9twpPTLxQsP/6dBuRPoffzuj7+RWO2UFd85WvSr/HGZ+xkp/JZP9DM/pxT47+X1TL+6binMCFv8O/SzqxzLFDJf0ko78rJQ3Oef6ifivciPhCqfGqcBPnioL9tybHFLYxpcJ5/yXjvGV/NyXaOTGjnU80aIxOzjiXSxoR4zOSc/xMbfBxu2f8/KfX0O+aPi91es9mZPwM+1Y45vjU/nemtvdT12v3xkqfQYXvwnQ//l+Z/a9XWKvkLOX4vkyOGapwg2Bj6jxLq7hGdGT0s6Pev2OFGwq/yzjXXEnjc/b19Izjd89x3JTUMRv12nfLmuQ9L3U9HKAQhE+fd7OkXXP2+/9lHL9J0pdUIme9wk2Wc1PjLv39PTXW54pCoVAozSmksgEAoKtByX8flXSYu5ecSenuyxRmPM1KbZpgZukZu2nXZ9SdnreTBT6m4tR0WW3/g5mdrJB2p9Btkg5097vd3Usd6+4b3P1ShXzQrxZsGq7smd6VdM56fErSm73MDEB33+Lum2s4R25mdpjCY++FHpK0v7v/wkMu4lL92+zuP1SYrb2mYNMASd/Icfpr9Nr46/RzSe9x9+fLnPf/FNLGdObjHlxq3wa7N6OuUu7nI9Q1F/r9Ge9xtXnmOzLqsvrWKJ2/w/mSDnX3W0rt6O4vK3zu0znbt1cIWJdlZnuo+HO3WdIH3f1bpcaru69z988qLGoshd/BwKx9y/ixwk2IQudV2UbWMesU0k80wj4ZdQvd/aUGnQ+NUcu1piP1emrhi+S75cGCqgEKazFU06ZU/lrzWXc/3N1/UO6anurXy+7+NUnHKny2O41SWDi0JSTrRNyvMMO/0DRJh7v7gshdGqBwXVshaXLynpe6Hm5y9y8qpK4r1FfSGZVOZGa7Sfq3VHXndfgSd3814zB58H2Fp/PWJdXN+v4GADQJgXkAAIqtkvQBd6+UDkUe8hJfmLHpnRWO+6vCYmSFPlxDrufTU6+fVZm0DGZmCjO7Cv1F0snuvinvSd399wozywudUeOCaZslfcjdl1Tcs/G+oq7/Ploi6Rh3X523AXf/i6TzU9XvNrNJWftLkpkdoOLA0jOSPuY5Fshz96cUbtI0033qGjiSpCOTMVdKR+p1OgifVZc+plKbUuSFXxVuWp3s7osq7ZjcCLsgY1PZa0jifBUvZniJu9+W41i5+3cl/W+efTOOXSnpZ6nq480s95oMZra3pLelqm909zVZ+9fBbhl19UqxgniyPs8dFY5Jb+/WtSZJXfLmVPUGSY+UOqaa75GMY/+g8JRLoWrXsGiI5LvtYRWncfmZpKOTa0WznJF8J+fxRYUnhwrluQ6frXAjoNDl7n5rnpN6SNv1pTz7AgDaD4F5AACK/Ze7P1vF/r9XyBlfKM8inOmZ7SOUvTBhJjObLGnPVPWUCoHcYyXtl6r7TKkZXRX8p8Ij2J0GKeRZr9b/uPvjlXdrLDPbX8V55b/s7qtqaO5nkp5O1R1fZv9zM+r+xatYkNLdfyfpV3n3r7dk9nf6ZtMoFY+3QuXyy3d6VOHx/k6HJGskFCmRX369QuqLmH7m7n/Ku7OHPPwzU9VlryFmNlDFN+aWKCzmXI1/UUj7UIv0IrBZ+eLLOVddn5iQpO/X2Jc8shZXrOXzjea6X1XcBEzyyxcGjdeq+PMmVfd0zsEqzi//QDU3uGuQzkF+YKlrYSxm9i6Fm7K7pjZ9U9JHG/x+VDLN3e/Iu3MyGeM3qeo3JOvOZEq2pa95KyR9PXcvg6sUnhwEAPQyBOYBACiWfpy5rOTx6HQQLitlQtr/qnh21ulVnDq9ryvkWS3nA6nXT7t71szBijwswJmeudhRQ1M/quX8DZB+b9ZKurmWhpJZ0L9NVXeUOSR9Q+PvCovUVevaGo6pp9wzWZNg2QEFVZnBsuSmUd4UEwcr5E8v1OhgWZaqriGJ9M2DvSvs/yaFHP2FfuplFsbN4u5/V403dNz9z5IeSFWfZWZ9s/YvZGaDVJxS65EG36TLShNR8yxmNEdyEzC9iGi5m4BHqnLKLCl8BgtvAh5cJvDdkVHX6Cdz0jd7+6nrgttRmdnZCteObQuqN0s6y93/rVxavEjqcR0equKbDoUmKiyEXOgmd1+ftXMpyYSKKdUcAwBoDwTmAQDoap67L67luNTrYZUOSB7vTqeceJeZpf/IK5IEC05KVU/Lkcf1yNTrBzP3yi99vgOrPH6DwiPwrSD93sx09/SNk2rkem+S9D/jU9W3l8tnX8bv9Fqu2maoJvdz3mCZlH8ma0fOPjXSBtU2Qz99DelrZukZuYUOzajLlTqhjsdJxbPmd1N4MqeSU1R8Y+GabvQjj6wZ1c0OHqI21VxrOlKvp2btlNwEfKigaoCK09WUarNUn0oys75m9hYz+6SZXWtmd5jZvWb2mJn9OV2U/V05tppz1oMFlyrcCO5XsGmtwpooP4zdpxJqmXSQvg5L5f89l3UdTs+6z+vOGo8DAPRg/SrvAgBAr5KekZZXetZlxcB84npJHyx43VdhQbfLKhz3AXWdpSaFxRhLMrPRknZPVb8r+YO/VumbCCOrPP6JGgPQdZXM8E3/gf36br436aDjMDPrn5E2KCtlSe40KIXcfbOZPSHpsFqOr4MHJG1S13y7R5iZZcye7Ei9LhdEyZv7Oas+dmB+UY2pobJmbg+T9HKJ/dPjZrPCehG1qGm8JW5ReMKjMLf8eZIqpZBIL/q6QjU+oVKFrKcJtm/wOdEY96p44eMOSVdm7NuRel3pWnNU6ti7C3cws/4qDthnzeLPlNx8/6LCd/9OeY4pI+r4TVJo/UThxlqhxZKOrSKfe6O9kneB3ZRS1+FSDsioy0qTlMeTCk9RpheBBwC0MQLzAAB0VXHB1xLSgbi837F/kPScui5KeLoqB+bPSL1eqxAgK2dMRt1O6n5goNCIKvdfWsdzd8cIFf8xPFzZOam7YwdJL6bqshbM7U6u2SfVpMC8u683sxnqmud9pELw4onU7nnyy3eaoa4Bi4PNbIi7/+PpgDLBsnTe+0ar1zVEKl7YtVB63DzbjSc8npa0ReHGYFXc/VUzu07SVwuq32lm40s9wWNmb1BIxVNoSjefUMkj63dDYD4CMztX2WtplHOHu6cXK++UdRPwyPRNQDMboa7B05dV/kZUnqdzslJm3e/u6bz3RZL0L5dJ2q7SvjnlnQRQL39Q8ToeTygE5WsJhDdKrOtw+t88m5L0YFVLbqw/J2mvWo4HAPRMpLIBAKCrWma61izJK/qTVPW+ZnZIqWPMbHcVp125OUdO02qD5rWodqbXmob0onox3hspO8d1VmCwO3mvm50zu2KKiSRYtn9BVdlgWbIIbmEah/4qDg5lBcvuyxMsq7NY15D0uKn5954EMtdW3LG069T15zZJ55TZPz1b3tXYRV87ZQUO63ljEqXtLGlSlaVkmpZkLYVHUtUj1HWRV6k4ZdYDFa4Jj6jr2i8Hm1n6utKRcVzFJ3PM7EKF9C/1CspL5YPGjZC+7j4raXKLBeWleNfh9M377n7/Nvv7GwAQGYF5AACab0pGXXpGfKHTVZwruWwam0S9Z3/XQ+ygaSnNfG/SKYmk7uWJb2aOeSnfArDVBsukyuls0q9L9aVdpMdNd3/vNR/v7ksk/V+q+kwzG5De18y2k/SRVPUf3b3WNGLVyHoSZXyyEDF6njzXmvTrqeUaTG4CFgb8+6k4GJ1us1Rf/sHM3iLpWxmb1kn6maTzk3b3VPg+2kZSH3e3wlLuHJGkr9Nj1fVpmd5mYOp1dxca39jN4wEAPQyBeQAAmszd50manqr+oJkVzT43M5N0Wqr6KXd/KL1vhqz8yt9K/+Hf3ZLvp245We/NzfV+b9x9YcZ5smYqp2doVqM7x9bDQyoOLhxpZoX/7kynh8izSF+lFBNZKSfaOTCfHjfd/b139/j0IrCjFNbCSDs141wxZstL0uPKXuy15BNKaGl5FoCt67WmRMqsNaqcV/y7GXVTJI1x94+4+zXuPs3d57n7KnffkF6Xw8yynriK7UQVX98vSBav7Y2xhfQM96wb7dWo59MUAIAeoDd+eQIA0IrSM963l3R8xn4dKl7A9fqc51ieUcdM0aCZ782qjLru5A2OnXO4iyRPePpG0XB1TTHRkdo+NUfTD6vrbMSDzGyo9I9g2eGp/VcrBGLbVXrc1Px7T274dSug5O73qXjx2XTKGqk4z/gSSbd359x5uftaZadMeluM8/dm7n5RDTcyT6/Q7EPqmnZGCotN95EkMxspab+CbeuUb4HWck/nHKIwm73Q9HKLmJvZnpLemKr+lbuf4e5Z1/9Smv597e53SHqPpHTqvLMl3WBmvW0Nu5Wp19tmPSlUhVhp9QAALYLAPAAAreGXCnm2C2Wls0nXbZF0Q85zpBcdlaRxOY9td8tUPJM21nuTtQDuPt1ob0I3jq2XkjNZSwTLKi7QmuSUnlFQ1U/S5OT/qw6WtYH0uBmb9ZRNTnuphoVfM1yVev0WM/vH7zpJ57F/ap8fRl4H4I6MutO7GUxDEyRpZ7JuAk5K/j+dMuvBnGPtIRXfBOy8cdWRsX+lJ3PekVH39Rz9SHtdDcfUnbv/QdLRKp4t/hFJv+hln6XnUq9NXb/fckvWXhnd7R4BAHoUAvMAALQAd18n6eep6reb2ZjOF0lg4MTUPne5+99znuYZSStSdYebWT0Ccj1aMss7Pdt3bzOLsTBk1gzeg2ppKJmtmF78sBnK5X7OCpblXaiv1EzWDhVr5zQ2UvG46afXApLVqmm8ZbhRxTP5zyvx/1K4sfiDOp07r59I2pqqGyXppMj9QH2Uu9Z0pOqn5mkwuQlYOLO+MM98us1SfSi0W+r1K+5e8WZkhsNqOKYh3P0BhSdN0k+bHS/pV2aWvlHarmZk1B1aY1u1HgcA6MEIzAMA0DrS6Wz6SPpYweuTVTwrOG8aG7n7Vkl/TFUPVZj51lNlzX6s9UbD3Rl176+xrdzcfamkBanq99WYr/edan6OeSksnpjO29+ZYqKWnM+dpqZevzX130LtHph/OKPuhBrbqss4d/f1Kr4mnWpmQ5InJdI553/l7s/X49x5ufuzkm7N2HSpmW0fsy+oi3J55ut6rUlmgqdTZq1U8U3dtJGp1+kb5HmdXONxDeHuMxVutKYnBxwt6a6Cpwza2SMqftruwzW2lV4UGwDQCxCYBwCgRSQz0J5OVRcu9Hp6attyZadlKCcrl/OXqmyjlWQtnDq0xray3pt/jZQz97ep16MlHVtDO2fVoS/d5u6bJD2Qqt5e0htU4yzWxIOSCmfX/1MS8E3PJF0h6Ykq2u2JHlFxgO/UatPZmNnOkt5bt16FdDaFgartFAJVZ0pKp7i4po7nrcZXVHxTb4yKU/F0i5kNMrPz69kmisxQcb7zycnTTvsW1K1X9uzmUrKezslKmTUtueldzrrU6+HV3ng1syNVvydb6sbd5yikFFuY2jRZ0h/NrOl58RvJ3VdK+n2q+i1mVtXTDWa2hyJMBAAAtB4C8wAAtJb0bNO9zezNZraXXnuUvtONVaQA6XSzQkqbQpPN7HNVttMq1qo4wFZTHt7kxsjUjLYur6W9Kn0/o+471eTqNbO3Szqufl3qtqyZrCerOFiWZzFGSf+YkV2YAqKvpH9RbcGyHi3Jrz0lVT1a0herbOo7kgbWo0+S5O7zJN2Vqj5f0jmpumeU/ZRKw7n7U5K+mbHpw2b23zU+rdJFcs2+Xy02y7ndlLkJ+Fl1TZn1UJXflw+q63fLP0l6X8Z+eZ7MSc8oH6zX1seoKEkLc13e/WNLPvOTJT2V2nSwpKmRUsI1U9YNxu/nTeeT3Py/VnW8DgMAeg4C8wAAtJafKORdLnSGimfLS1WksemULHz31YxN3zazdOAsNzM73Mx+VuvxtUqCr3NS1e/sRmDtK4xmrakAACAASURBVCp+LP3TZvY1M7OsAyoxs/3N7AYzG15qH3f/q4oDPHtLuj7Pz5IEAf+nlv41UFbA6hMqDpZtytivnPRM1k/mPHc7ukZdnyCQpH8zs/fkOdjMPq3GpE/4Xur1G1R8w+w6d09/1mL6hrJTm3xK0s/NbMdaGk1myX9Z0p/VgjOc21TW5z19XZhaTYPJui/pm4DpNRJKnTvtvoy6i82sf6UDzWywpF8ofB+0rCQl1REqflLpAEnTzSydZ7+d3ClpZqru9Qq59oeVOzB5wumnko5qUN8AAC2OwDwAAC3E3Zeo+LHok9U1pY0kzXT3SnltS53jZyoO6vdTmOF1i5nlWjzUzMaY2WfM7GGFGYtZswljeDD1eh9JPzSzcdU2lMya/1rGpv8n6R4zyzXL0cxGmNnHzexuhUDFqaqc+/58SRtTdR+WdIeZ7VrmXMdLmi5p56Qqndu9WR6V9HKqLp1mqJqcz6WOyUpd1CsC8+7+jIpnfveT9Esz+9dSCzub2TZmdrmkKzqbUvHY647fSppXZvtG1XBjsZ6S2dMnSJqVsflESfPM7OuFC3CXY2bjzOwrCutFXKzipzjQOFmf9xjXmuXKHj9pMyQtStW9WdKtSSquTGZ2sMK1/Zikak2OczVNsl5Kh0KarUJ7S7ovSdfSdtx9i8IEivRN0rdJetLMzk/f6DOz4WZ2msL4OSWpXiXpyUb3FwDQWmLkTAUAANW5XtK7C15vm5T0Pt1xrqQ9Vfw4/fslvd/M/qIQlHha0kuStiqkBxghaX+FmaB7qevs52b5scLPU+gMSWeY2TJJy1T8B/Nj7v7xEu19XdIESR9M1XcozPz7m8Lsy9kKOb43Krw3wxXStBwkaaKqXITW3Z9MZtqmU+ccK+kZM/utwszLvyukQnidQuqaAwr2Xawwu/Kz1Zy7Edx9s5ndL+ldZXabWkPTDyg8VVLq/V2m8LvpLS6W9B51nZ09UNJlkj5jZrdImquwSOUoSQcqBKQLcz9fpZBnvuqbWVnc3c3sGoU0OVl+4e7L63Gu7nD3lUnu7jtVvE7BUEn/LunfzWyOQvD3eYXxtUrSIEk7KdwIPFzSfmqN62Fv9JjCTcBS64tsUHGwOI9pkr5QZvvUPE99uPsWM/uaihd4f4+khcln9GGFQP9QSWMV/g1wiLqOqU9KuiF/9+NLPlNvl/QrdV1PZJxCcP7tSV76tuLuT5jZJxXS0hX+znZWuL5+z8yWKvx7arikHVX8HXauwlMZEwrq0k9QAgDaDIF5AABaz+0Kf7yNKLF9o6RupY1x901mdrTCH4xnZuwyKSktz90fNbMpyk73MyopaavKtOdm9hGFGb9fVnGwbW81KK2Au/9nMoMyvSDvIIVg6gllDl8n6XiFYE+ruFelA/MbVN1ijJIkd19rZjMV8hdnyRUsaxfu/qqZvUvSPep6k0YKC5p+pkIT90n6V9V3AVgpBCG/ruyZ481a9LWIu69IgvMXS7pA2Td89lXXtRHy+pvCDRI0UHIT8D51vaFd6OEaUmZJlW8C5n4yx92vN7N3SPpQatMQSR9LSjkXu/v/mFlLB+Ylyd1fNrN3S7pFr832l6RdJE0zs3e6ezr1S4/n7teZmSsE59OZCUzhRl5Wvn2X9Fl3v9nMPpXa1tJPSQAAuo9UNgAAtJgkgFAu8H67u6+ow3lecfd/lvRRSfO72dxSSVd3t0/dcK6k/1KY2d9t7r7V3b+iEFSoKWVQgdWSfqjitC6lzv1lSZ9TWBg1r+clvdXdH6u4Z1zlAlcPJwuY1qJcWopekcamUDL7/G2S7qjy0Fslvasbv4dyfVqp7OvYE+6eTj/VVO7+qrtfqLDA550qXmeiWk8rpKbaz91/3d3+IZdyn/uptTTo7mskPV7jObOcIelHVR7ziqRPJd9HPYa7v6Jwo/iXqU0jFdLCHR6/V43n7j+Q9CZJf8p5yHyFa/B/J6/Ta9GsrlffAACticA8AACtqVyqmrrmZnb3GxVmgH9EITf02pyHzpH03wrB612TwFZTuPtGd/+spN0lXagQcHxK4cmDWmZKdrZ7l7u/QWE28a0KqWvymC/pB5I+IGkXdz8rCVTkPe8VCimDfqLyM+aWKixiuZ+7P5q3/YhmqnT/a8n5nOfYXheYl0Jw3t2PU8iPfr/KB5cfkXSiu5/o7tXcAKpW1o2ilpktn+buT7j7exVSSXxNYQHXPKkkOhehvlrS4e6+t7tfkyy2jTjKfe4bca15wd3nVtNQ8j31cYX0ZA9U2H21pGsVru3pxZR7hGQdhw9KmpLaNEzS75OUN20nuUF+iMLN0qsVriMvStqs8O+rOQqLtX9A0t7uXriu0M5dW8v9bw4AQA9lvehJXwAAkIOZ9VPIQz1OIZ3ODgqBp7UKeaqflvSku+eaAd5OzMwU0oXsofDejFCY6LBWIT3OPElz3b1kqpwazjlQ0lsU8g7vrPC7eFFhUdk/u3tdnhJAe0kWDD5E0miFWZjrJC2UNMPdF0fqw4Pqmrt9raTRPenaYWZDFD7z4xXSUHSm5lmZlCWSHnf3vDc0AUmSme2ksAjsaIV1SjYqXNvnKowpbuz0Ima2l0L6q0L/7O7ptQkAAG2EwDwAAACAujKz16s4DdT33f28ZvQHAFqZmX1W0ndT1Qe4+6xm9AcAEAepbAAAAADU2ycz6pq5DgUAtCQzG6DihbqXK6S9AQC0MQLzAAAAAOrGzMZI+liq+h53/2sz+gMALe67CmvkFPoRqeoAoP0RmAcAAABQT/8laWCq7j+b0REAiMHM3mtm7zezvlUcM8DMrpF0fmrTZknfr2sHAQAticA8AAAAgG4zs13N7CeS3p/a9JC7/7oZfQKASPaTdIukhWb232b2rmSB3y7MrI+Zvd7MvqCwYPy5GW1d5O4LG9tdAEArYPFXAAAAAFUzs99IGp283FlSURBK0hZJb3b3R6J1DAAiM7MvSrokY9MqSSskbZC0naSRkgaXaepuSe8ijQ0A9A79mt0BAAAAAD3SvpLGVdjnYoLyAHqx7ZOSx48lnUdQHgB6D1LZAAAAAKg3l/Rtd/9qszsCABE8LmlOjcfOkHScu/+zu2+qY58AAC2OVDYAAAAAqmZmC9V1xvw6Sc9Kmi7pOnef2Yx+AUCzmNleko6QdKikvRSukcMlbaNww3KlQmqbeZLuk3Svuz/anN4CAJqNwDwAAAAAAAAAABGRygYAAAAAAAAAgIgIzAMAAAAAAAAAEBGBeQAAAAAAAAAAIiIwDwAAAAAAAABARATmAQAAAAAAAACIiMA8AAAAAAAAAAAREZgHAAAAAAAAACAiAvMAAAAAAAAAAEREYB4AAAAAAAAAgIgIzAMAAAAAAAAAEBGBeQAAAAAAAAAAIiIwDwAAAAAAAABARP2a3YF2Y2bDJB1ZUPWcpE1N6g4AAAAAAAAAINsASbsVvJ7m7qtjnJjAfP0dKen2ZncCAAAAAAAAAFCV4yTdEeNEpLIBAAAAAAAAACAiAvMAAAAAAAAAAEREKpv6e67wxZVXXqlJkyY1qy9oUwsXLtTHPvaxZncDAAAAQAu64YYbtPvuuze7G2gzWX+HMtbQCIw1xLJy5Uodd9xx6ernsvZtBALz9ddloddJkyZp8uTJzeoL2tTIkSOb3QUAAAAALeqNb3yjJk6c2OxuoM1k/R3KWEMjMNYQy7Jly7KqN2VVNgKpbAAAAAAAAAAAiIjAPAAAAAAAAAAAERGYBwAAAAAAAAAgIgLzAAAAAAAAAABERGAeAAAAAAAAAICICMwDAAAAAAAAABARgXkAAAAAAAAAACIiMA8AAAAAAAAAQEQE5gEAAAAAAAAAiIjAPAAAAAAAAAAAERGYBwAAAAAAAAAgIgLzAAAAAAA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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 404
+ },
+ "id": "310wPf8aQPK3",
+ "outputId": "d40d9957-52e9-4b5b-a740-ab347d76ad5f"
+ },
+ "source": [
+ "plt.figure(figsize=[20,5])\n",
+ "pos=torch.tensor([1,3])\n",
+ "p=agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "print('Argmax:',pol[tuple(pos)],'for pos:',str(pos.numpy()))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [0.64135283 0.71163344 0.4731686 0.61014515]\n",
+ "Var Q: [0.13993353 0.15828 0.3481025 0.1514151 ]\n",
+ "Entropy: [1.8461998 1.8577257 2.103247 1.8548269]\n",
+ "Argmax: R for pos: [1 3]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 387
+ },
+ "id": "4SOonCqGHdPb",
+ "outputId": "05b2891c-7005-4136-e5e4-2bca23464cf3"
+ },
+ "source": [
+ "plt.figure(figsize=[20,5])\n",
+ "pos=torch.tensor([1,2])\n",
+ "p=inv_agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=inv_agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(inv_agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(agent.support.cpu().numpy(),p[i])\n",
+ "#print('Argmax:',pol[tuple(pos)],'for pos:',str(pos.numpy()))"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Mean Q: [-0.99764574 0.28456345 0.28275877 -0.0408875 ]\n",
+ "Var Q: [0.1234032 0.30820864 0.26390046 0.43405277]\n",
+ "Entropy: [1.0289338 2.2166333 2.1583898 2.40856 ]\n"
+ ],
+ "name": "stdout"
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1440x360 with 4 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "jEJ-blyFtVIJ"
+ },
+ "source": [
+ "plt.figure(figsize=[20,5])\n",
+ "pos=torch.tensor([3,5])\n",
+ "p=m_agent.dqn.dist(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "m=m_agent.dqn(pos.float().to('cuda')).squeeze().cpu().detach().numpy()\n",
+ "y=(m_agent.support.cpu().numpy().reshape(51,1)-m)**2\n",
+ "ent=-(p*np.log(p)).sum(axis=1)\n",
+ "variance=np.sum(p*y.T,axis=1)\n",
+ "print('Mean Q:',m)\n",
+ "print('Var Q:',variance)\n",
+ "print('Entropy:', ent)\n",
+ "for i in range(4):\n",
+ "\n",
+ " plt.subplot(1,4,i+1)\n",
+ " plt.title(str(d[i]))\n",
+ " plt.plot(m_agent.support.cpu().numpy(),p[i])\n",
+ "#print('Argmax:',pol[tuple(pos)],'for pos:',str(pos.numpy()))"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BhUvf59xosyo",
+ "outputId": "819e595c-837e-40d6-c862-507a5025e62d"
+ },
+ "source": [
+ "#expert policy for WCW\n",
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "reference=torch.ones([4,9],dtype=torch.int64)\n",
+ "reference[1:,0:6]=0\n",
+ "reference[:,6:]=2\n",
+ "onehot=torch.zeros([4,9,4])\n",
+ "onehot=onehot.scatter_(-1,reference.unsqueeze(-1),1)\n",
+ "display_reference=reference.numpy().astype(object)\n",
+ "display_reference[reference==0]='U'\n",
+ "display_reference[reference==1]='R'\n",
+ "display_reference[reference==2]='D'\n",
+ "display_reference"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['R', 'R', 'R', 'R', 'R', 'R', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D'],\n",
+ " ['U', 'U', 'U', 'U', 'U', 'U', 'D', 'D', 'D']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 24
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "ByoW9PR-GTbV",
+ "outputId": "ec6f042c-7a33-4b6c-ce2d-348f93058357"
+ },
+ "source": [
+ " d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC Expert Policy - Windy CW\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, arrow_list[reference[i,j]],\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Ox8rhFthyHYj",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "outputId": "ddc044c3-18bb-451c-c515-659aa31541ea"
+ },
+ "source": [
+ "#original labels, deviation from expert policy\n",
+ "dev=(pol!=display_reference).astype(float)\n",
+ "def highlight_cell(x,y, ax=None, **kwargs):\n",
+ " rect = plt.Rectangle((x-.5, y-.5), 1,1, fill=False, **kwargs)\n",
+ " ax = ax or plt.gca()\n",
+ " ax.add_patch(rect)\n",
+ " return rect\n",
+ "d={0:'Up',\n",
+ " 1:'Right',\n",
+ " 2:'Down',\n",
+ " 3:'Left'}\n",
+ "pol1=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "pol2=np.ndarray([env.pos_max_x+1,env.pos_max_y+1],dtype=object)\n",
+ "arrow_reference=reference.numpy().astype(object)\n",
+ "arrow_list=[ r\"$\\uparrow$\",r\"$\\rightarrow$\",r\"$\\downarrow$\", r\"$\\leftarrow$\"]\n",
+ "arrow_reference[reference==0]=r\"$\\uparrow$\"\n",
+ "arrow_reference[reference==1]=r\"$\\rightarrow$\"\n",
+ "arrow_reference[reference==2]=r\"$\\downarrow$\"\n",
+ "arrow_reference[reference==3]=r\"$\\leftarrow$\"\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC Original Labels - Windy CW\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(dev[i,j].item()),\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ymt3cV392KHG",
+ "outputId": "126a7565-258e-4f7f-c53e-c50a7d6af2f2"
+ },
+ "source": [
+ "#expert policy for inverted environment\n",
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "inv_reference=torch.full([4,9],1,dtype=torch.int64)\n",
+ "inv_reference[:3,0:6]=2\n",
+ "\n",
+ "inv_reference[:,-3:]=0\n",
+ "inv_display_reference=inv_reference.numpy().astype(object)\n",
+ "inv_display_reference[inv_reference==0]='U'\n",
+ "inv_display_reference[inv_reference==1]='R'\n",
+ "inv_display_reference[inv_reference==2]='D'\n",
+ "inv_display_reference[inv_reference==3]='L'\n",
+ "inv_display_reference"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['D', 'D', 'D', 'D', 'D', 'D', 'U', 'U', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'R', 'R', 'U', 'U', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 27
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "0iqkzdtH6tlY"
+ },
+ "source": [
+ "class SafetyClassifier(nn.Module):\n",
+ " def __init__(\n",
+ " self, support, action_dim = 4, size = 51, t = 0.2, alpha = 2,state_dim=0):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(SafetyClassifier, self).__init__()\n",
+ " self.action_dim=action_dim\n",
+ " self.size=size\n",
+ " self.support=support.reshape(1,1,size)\n",
+ " self.fwd=nn.Sequential(\n",
+ " nn.Linear(state_dim+self.action_dim*self.size,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,1),\n",
+ " nn.Sigmoid()\n",
+ " )\n",
+ " self.ce_loss=nn.CrossEntropyLoss(reduction='none')\n",
+ " self.t=t\n",
+ " self.alpha=alpha\n",
+ " def cat_CE_loss(self,pred,true):\n",
+ " return self.ce_loss(pred,true)\n",
+ " #return (-torch.log(prob) * true).sum(dim=-1)\n",
+ " def normalise(self,x):\n",
+ " #expect x of N by 4\n",
+ " a=(x-x.min(dim=-1)[0].reshape(-1,1))\n",
+ " a=a/torch.std(x,dim=-1).reshape(-1,1)\n",
+ " return (a**self.alpha) \n",
+ "\n",
+ " def get_error(self,dist,labels):\n",
+ "\n",
+ " mean = torch.sum(dist * self.support, dim=2)\n",
+ " \n",
+ " normalised=self.normalise(mean)\n",
+ " \n",
+ " loss=self.cat_CE_loss(normalised,labels)\n",
+ " \n",
+ " return loss#(loss>self.t).float()\n",
+ " def test(self,dist,labels):\n",
+ " mean = torch.sum(dist * self.support, dim=2)\n",
+ " \n",
+ " normalised=self.normalise(mean)\n",
+ " return normalised\n",
+ " def compute_target(self,dist,labels):\n",
+ " error=self.get_error(dist,labels)\n",
+ " return (error>self.t).float()\n",
+ " def forward(self,dist,state=None): #x is N x action_dim x size\n",
+ " dist=dist.reshape(-1,self.action_dim*self.size)\n",
+ "\n",
+ " if state is not None:\n",
+ " dist=torch.cat([dist,state],axis=1)\n",
+ " out=self.fwd(dist)\n",
+ " return out\n",
+ "\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "gsypifr7A-Ni",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "99d452d9-bd64-4ae8-a22b-5478dbae9b10"
+ },
+ "source": [
+ "#obtaining alternate labels for WCW\n",
+ "l=nn.CrossEntropyLoss(reduction='none')\n",
+ "classifier=SafetyClassifier(agent.support,alpha=2.5,t=0.2).to('cuda')\n",
+ "x1=torch.arange(0,4).reshape(1,4).expand(9,4).T.flatten().unsqueeze(1)\n",
+ "x2=torch.arange(0,9).repeat(4).unsqueeze(1)\n",
+ "input=torch.cat([x1,x2],dim=1).to('cuda').float()\n",
+ "dist=agent.dqn.dist(input)\n",
+ "labels=reference[x1,x2].squeeze().to('cuda')\n",
+ "target=classifier.get_error(dist,labels).reshape(4,9)\n",
+ "binary=(target>0.2).float()\n",
+ "binary[-1,:]=0\n",
+ "binary"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "tensor([[0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+ " [1., 1., 1., 1., 1., 1., 0., 0., 0.],\n",
+ " [0., 1., 1., 1., 1., 1., 0., 0., 0.],\n",
+ " [0., 0., 0., 0., 0., 0., 0., 0., 0.]], device='cuda:0')"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 48
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "8SWaTBVkdqd7"
+ },
+ "source": [
+ "#alternate labels for inverted WCW\n",
+ "l=nn.CrossEntropyLoss(reduction='none')\n",
+ "classifier=SafetyClassifier(inv_agent.support,alpha=2.5,t=0.2).to('cuda')\n",
+ "x1=torch.arange(0,4).reshape(1,4).expand(9,4).T.flatten().unsqueeze(1)\n",
+ "x2=torch.arange(0,9).repeat(4).unsqueeze(1)\n",
+ "input=torch.cat([x1,x2],dim=1).to('cuda').float()\n",
+ "dist=inv_agent.dqn.dist(input)\n",
+ "labels=inv_reference[x1,x2].squeeze().to('cuda')\n",
+ "inv_target=classifier.get_error(dist,labels).reshape(4,9)\n",
+ "inv_binary=(inv_target>0.2).float()\n",
+ "inv_binary[0,:]=0\n",
+ "inv_binary"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "jkW86AmuHkZC",
+ "outputId": "6f121635-b35b-40c2-d859-7cb22fce2e64"
+ },
+ "source": [
+ "arrow_reference=reference.numpy().astype(object)\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC Alternate Labels - Windy CW\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(binary[i,j].item()),\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "KrSVCBsErWej"
+ },
+ "source": [
+ "#training SC on top 2 rows\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SafetyClassifier(agent.support,alpha=2.5,t=0.2).to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,2,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=reference[x,y].squeeze().to('cuda')\n",
+ " dist=agent.dqn.dist(pos)\n",
+ " pred=classifier(dist)\n",
+ " target=classifier.compute_target(dist,labels)\n",
+ " loss=bce(pred.squeeze(),target)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "MVKlPZ8UKZDO",
+ "outputId": "bd613121-752d-4b55-add8-fdadb6b1812f"
+ },
+ "source": [
+ "classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " classified[i,j]=np.round(classifier(dist).cpu().detach().numpy()[0][0],3)\n",
+ "classified[3,1:]=0 \n",
+ "\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC trained on top 2 rows\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(grid,cmap='binary', vmin=0, vmax=1)\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold')\n",
+ " elif i==3 and j>5:\n",
+ " text = plt.text(j, i, 'G',\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(classified[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=\"black\",fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "6cV6nG2KVlzV"
+ },
+ "source": [
+ "#training on full env with alt labels\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SafetyClassifier(agent.support,alpha=2.5,t=0.2).to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=reference[x,y].squeeze().to('cuda')\n",
+ " dist=agent.dqn.dist(pos)\n",
+ " pred=classifier(dist)\n",
+ " target=classifier.compute_target(dist,labels)\n",
+ " loss=bce(pred.squeeze(),target)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "Yw_fdU049cQz"
+ },
+ "source": [
+ "#training on full env with original labels\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SafetyClassifier(agent.support).to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=torch.tensor(dev)[x,y].squeeze().to('cuda').float()\n",
+ " dist=agent.dqn.dist(pos)\n",
+ " pred=classifier(dist)\n",
+ " #target=classifier.compute_target(dist,labels)\n",
+ " loss=bce(pred.squeeze(),labels)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "2oTeC4R0uNy7"
+ },
+ "source": [
+ "#observing training results, SC prediction on training set\n",
+ "classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " classified[i,j]=np.round(classifier(dist).cpu().detach().numpy()[0][0],3)\n",
+ "\n",
+ "classified[3,1:]=0 \n",
+ "#classified=(classified>0.4).astype(int)"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "9Q_l7HWcdWcT",
+ "outputId": "6bb5a3a9-5e5b-4296-c975-6a4fb160e428"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC trained on Windy CW - Alternate labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if classified[i,j]>0.5 else 'white'\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==3 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(binary[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "XnNxkL6E7XNa",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "outputId": "caeb5cd3-df25-4585-d4dd-6fb63ab680a0"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC trained on Windy CW - Original labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if classified[i,j]>0.5 else 'white'\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==3 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(dev[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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AAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEQ5u9QLs78cQTo6qqZq9BG9uzZ0+zVwCAQamU0uwVAACAg5SfmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASDS02QvAMcccE6eeempMnTo1hg0bFi+99FKsXbs2Vq1aFR0dHc1ejzbydtaOPPLIvVlbs2aNrFF3skYWWQOA/vEaShZZI4usDUKlFFPHiYhZEVHenqqqypAhQ8x+ZsGCBeWhhx4q3dm2bVv56le/Wg4//PCm79rK0zlvZv8zf/78A2bt7/7u78phhx3W9F3N4B5ZM1kja/UZgHbT7OfVwTBeQ03WyJrJGlmr+8wqWT1y1ic6WCYU8wecESNGlG9961v7e67Yr+eff77MnTu36Xu36rTAE1bLzrBhw8o3v/nNPmXt9NNPb/reZvCNrJmskbX6Dj179tlny2233VauvPLKcsYZZ5SxY8fW/PlNmzat2SvSJmStfpr9vNrK4zXUZI2smayRtYaNYn6wTijme5xDDjmkLF++fJ8nh9dff72sW7euPPLII+Wll17a5+OvvvpqmT17dtP3b8VpgSeslpyqqsp3v/vdfmXtAx/4QNP3N4NnZM1kjazVf9jXvffeWxYsWFCmTJlywD8/ZSkDIWuN0ezn1VYdr6Ema2TNZI2sNXQU84N1QjHf41x55ZX7PCl87WtfK1OnTt37mEMOOaT8zu/8TtmwYUPN45555pkyfvz4pn8NrTYt8ITVknPFFVfsk7Xrr7++TJ48ueafzwsuuGC/WRs3blzTvwYzOEbWTNbIWv2HfX35y1/u9Z+fspSBkLXGaPbzaquO11CTNbJmskbWGjoHdzEfEVVETI+I346IRRHx+Yi4NCI+FRGnR8SIZu/Yw+6K+W5m4sSJZevWrTVPBosXL+728UceeWT52c9+VvP4a665pulfR6tNCzxhtdxMmDBhn6xdeeWV3T5+ypQp+2Tt6quvbvrXYVp/ZM1kjaw1ZthXT2XpmDFjao6VpQyErDVGs59XW3G8hpqskTWTNbLW8Dn4ivmIODQiLoqIWyLihQP8Ae2KiO9GxNxm772fr0Mx380sWbKk5kng7rvvPuA1Z511Vs01W7duLRMnTmz619JK0wJPWC0311577T5ZO9A1Z5555j5ZmzBhQtO/FtPaI2sma2StMcO+3i5Lx44dW+bNm1cuv/zysmzZsrJhw4ayYsWKmj8/ZSkDIWuN0ezn1VYcr6Ema2TNZI2sNXwOrmI+Iq6LiI5+/mF9IyLGNftr6PS1KOb3M4ccckh5/vnna54EzjzzzF5de88999Rct2jRoqZ/Pa00LfCE1VJTVdU+WZs3b16vru2atc9+9rNN/3pM646smayRtcYN+/rpT39annjiifLGG2/s8zFlKfUka43R7OfVVhuvoSZrZM1kjaylTFoxPyRaw2kRMWw/59+IiGcj4uGIeDwitu7nMX8YEXdWVTWmcesxULNnz45JkybtPV63bl3cfffdvbr25ptvrjmeP39+PVejzQwkazfddFPN8QUXXFDP1WgzskYWWSPTjBkzYubMmTFkSKv8awLtStbI4DWULLJGFllrL634XdDLEXF9RHwoIg4tpby7lPJrpZRfiYjDIuKMiPj3LtecGhFLU7ekTz74wQ/WHN911129vvbOO++sOZ47d26MGjWqLnvRfj70oQ/VHHfNT0+6PnbevHmyRrdkjSyyBgD94zWULLJGFllrL61UzG+IiE9HxJRSyp+WUv5vKeWVzg8opbxRSrk73iznb+xy/YerqjojZVP67MQTT6w5vu+++3p97XPPPRfr16/fezx8+PCYOXNm3XajvXQzzwj5AAAgAElEQVTN2qpVq3p9razRF7JGFlkDgP7xGkoWWSOLrLWXVinmr4qIY0spN5VSdhzowaWUNyJiUUQ81OVDn27EcgzccccdV3P85JNP9un6p556qub4+OOPH/BOtKeu2ehr1ro+XtbojqyRRdYAoH+8hpJF1sgia+2lJYr5Usr3Sym7+njNGxHxN11On1u/raiXESNGxFFHHVVz7uc//3mf7tH18e9973sHvBftpxFZO/bYYwe8F+1H1sgiawDQP15DySJrZJG19tMSxfwAdH2v+cOqqvLmSC1m4sSJNb/UadeuXbF58+Y+3WPTpk01x51/0QW8TdbIImtkkTUA6B+voWSRNbLIWvsZ7MX8S/s59870LejRmDFjao63b9/e53u89tprPd4TImSNPLJGFlkDgP7xGkoWWSOLrLWfwV7MT93PuS3pW9Cjrv+Q79y5s8/36HqNJw72px5Z27Gj9tdcyBr7I2tkkTUA6B+voWSRNbLIWvsZ7MX86V2ON/b1veppvBEjRtQc79rV9/+LOjo6erwnRDQmayNHjhzQTrQnWSOLrAFA/3gNJYuskUXW2s9gL+Y/1eX4/zZlC3rU9W/whg0b1ud7DB8+vOa46xMJRDQma/35G2jan6yRRdYAoH+8hpJF1sgia+1naLMX6K+qqj4YEXO6nF5a588xKSIO7+NlM+q5Qzt49dVXa47789PuXa/pek+IqE/Wuv5tsayxP7JGFlkDgP7xGkoWWSOLrLWfQVnMV1U1ISL+vsvp5aWUB+r8qRZFxFV1vudBp+s/5KNGjerzPUaPHt3jPSFC1sgja2SRNQDoH6+hZJE1ssha+xl0b2VTVdWQiPhWRBzZ6fTWiLikORtxIC+++GLs2bNn7/GwYcNi0qRJfbrH1Km1v+d38+bNddmN9iJrZJE1ssgaAPSP11CyyBpZZK39DLpiPiL+Z0T8Vpdzf1xK+XkzluHAdu7cGc8880zNuaOOOqpP9+j6+LVr1w54L9pPI7K2Zs2aAe9F+5E1ssgaAPSP11CyyBpZZK39DKpivqqqSyLiz7uc/ptSyi0N+pTXR8QJfZz5DdplUOtapM+cObNP1x933HE1x5446E7XbPQ1a8cff3yP94O3yRpZZA0A+sdrKFlkjSyy1l4GTTFfVdXHI+J/dzm9NCIWN+pzllI2l1Ke6MtExLpG7TOYPfbYYzXHv/7rv97ra4844oiYPn363uNdu3bFk08+WbfdaC9dszZ79uxeXytr9IWskUXWAKB/vIaSRdbIImvtZVAU81VVnRcR34iIqtPp2yLi06WU0pyt6Ivvf//7Nce/+Zu/2etrzznnnJrju+++O1577bW67EX7+d73vldzfNZZZ/X62q5ZW7FihazRLVkji6wBQP94DSWLrJFF1tpLyxfzVVWdERHLImJop9N3RsTHSilvNGcr+mrVqlXxwgsv7D2eMWNGzJs3r1fXXnTRRTXHt99+ez1Xo80MJGsXX3xxzbGs0RNZI4usAUD/eA0li6yRRdbaS0sX81VVnRYR/xwRIzqdXhURC0opu5qzFf1RSolvfOMbNee+8IUvHPC6M888M+bMmbP3eNu2bbFs2bK670f7KKXE0qVLa85dddVVB7xuf1m79dZb670ebUTWyCJrANA/XkPJImtkkbU2U0ppyYmI90fELyOidJpHIuKdzd7tAHvP6rxzVVVlyJAhZsiQcvjhh5dt27aVzhYvXtzt44888sjys5/9rObxf/VXf9X0r6PVpss/IyaiHHbYYftk7corr+z28VOmTNkna3/5l3/Z9K/DtP7ImskaWWvM0DcrVqyo+fObNm1as1eiTcla/zX7ebUVx2uoyRpZM1kjaw2fWSWrR876RH1aKuLYiHi+yx/KkxFxeLN368Xuivke5nOf+1zp6vrrry9Tp07d+5hDDjmkLFiwoGzYsKHmcc8++2w59NBDm/41tNq0wBNWS87ixYv3ydp1111XJk+eXPPP5/z58/ebtXe+851N/xrM4BhZM1kja/Uf+kZZShZZ679mP6+26ngNNVkjayZrZK2hk1bMV2++dreOqqqmRcS9EXFkp9PrI+L0Usqm5mzVe1VVzYqI/+h0HFVV9XDFwaWqqli+fHmcd955Ned3794dGzdujK1bt8b06dPj0EMPrfn49u3b49xzz41Vq1Zlrjso7Nmzp9krtKSqquL222+P888/v+Z8b7J29tlnyxq9JmtkkbX6a7Xvg1vFypUrY8eOHfucX716dVx22WV7j9/1rnfFt771rf3eY8qUKTFz5syG7Uh7kLX68++e++c1lCyyRhZZa6gTSilPpHymrL8B6M1ExOSI+GnU/i3FsxExvdm79eFr8BPzB5iRI0eW73znO6W3XnjhhXLGGWc0fe9WnWj83xQO2hk+fHj59re/3aeszZ07t+l7m8E3smayRtbqO+zftGnTBvxnu3DhwmZ/GQwCslZ/zX5ebeXxGmqyRtZM1shawybtJ+Zb5pe/VlU1ISLujIgZnU6/EBFnl1LWN2crGqGjoyN+//d/Pz760Y/Go48+2u3jXn311bj++uvjhBNOiHvuuSdxQ9pFR0dHfPzjH48Pf/jDB8zaddddFzNnzpQ1+kXWyCJrANA/XkPJImtkkbXBryXeyqaqqrER8W8RcUqn0y9HxBmllMeas1X/eCubvpsxY0acdtppMWXKlBg2bFi8/PLLsWbNmli5cmV0dHQ0e72W561seu/trE2dOnVv1p566ilZo+5kjSyyNjCt8H1wKzr66KNj48aNA7rHwoULY+nSpfVZiLYla/Xn3z17z2soWWSNLLJWN2lvZdMqxfyKiJjX5fT/iIj7+nG7h0spLw14qX5SzJNNMQ8A/dMK3wcD1JN/9wSAAUsr5odmfJJemLefc3/Zz3udERF393sTAAAAAABooJZ5j3kAAAAAADgYKOYBAAAAACBRS7yVTSnFG+EBAAAAAHBQ8BPzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiYY2e4F2V0qJUkqz1wAAoIuqqpq9AgAAcJDyE/MAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAIkU8wAAAAAAkEgxDwAAAAAAiRTzAAAAAACQSDEPAAAAAACJFPMAAAAAAJBIMQ8AAAAAAImGNnsBDk6HHXZYnHzyyTF16tQYP358jB49Onbu3Bnbt2+PLVu2xKZNm+LZZ5+NjRs3Riml2esCAAAAANSNYp40EydOjE996lNx0UUXxXHHHdera7Zv3x5r166N1atXx3333RcrV66MJ554osGbAgAAAAA0jreyIcXChQvj6aefjiVLlvS6lI+IGDVqVJx00knxyU9+Mv7+7/8+fvzjHzdwSwabFStWRCklbebOndvsL5kmkTWyyBpZZI0sskYWWSOLrJFF1tqfYp6G++IXvxhLly6N8ePHN3sVAAAAAICmU8zTUAsXLozPf/7zzV4DAAAAAKBltOR7zFdVNSwijouIoyNiakSMjYh3RMS2iNgSEY9HxFOllDeatSMHNnr06Lj22msP+Lg33ngjXnjhhdixY0eMHTs2xo8fH0OHtmQ0AQAAAAAGrGXaz6qqPhIRZ0XEb8SbpfyBdttaVdV3IuIrpZQ1jd6Pvvu93/u9OOKII7r9+K233hpf/epX4/7774/XX3997/mqquK9731vnHzyyTFnzpz47d/+7Zg8eXLGygAAAAAADddKb2XzvyPijyPihOjdXxi8MyI+GxGPV1V1dVVVVSOXo+9+67d+q9uP3XjjjfG7v/u7ce+999aU8hERpZRYu3ZtfPvb347PfvazMWXKlDj99NPjn/7pn6Kjo6PRawMAAAAANFbmb/ftaSLi2YgoXWZHRKyNiAci4qGI2BARe/bzuBIRNzX7a3jr65jVzX4H3Tz99NOlOyeffHK/7jlhwoSmf11m8M2KFSu6zWIppUybNq3pO5r2GFkzWSNrJmtkzWSNrJmskTWTNbJmskbW6j6zSlKP3DJvZfOWX0TE9yPiRxFxX0SsL6Xs6fyAqqoOjYiPRMT/iIgjO33oU1VV3VtKuTlrWXp2+OGHd/ux8uZfYvTZL3/5y/6uAwAAAADQElrprWw+GBFHllI+U0r5VillXddSPiKilPJSKeXrEfH+iHiky4e/WFVVK31NB7WRI0d2+7GFCxcmbgIAAAAA0DpapsQupTxe+vBj1KWUlyLiE/Hmf2Lwtsnx5i+PpQX09NPtl1xySSxdujROOumkxI0AAAAAAJqvZYr5/iilPBURD3c5fXwzdmFfTz31VI8fX7hwYTzyyCPx7LPPxi233BKXXnppzJkzJ8aMGZO0IQAAAABAvlZ7j/n+WBcRv9bpeGKzFqHWD37wgzjjjDMO+LipU6fGhRdeGBdeeGFEROzevTseffTRWLFiRSxfvjzuu+++Rq8KAAAAAJBmUP/E/FtGdDl+uSlbsI+vf/3r8dprr/X5uqFDh8Ypp5wSV1xxRaxatSqefvrp+KM/+qM45JBDGrAlAAAAAECuQV3MV1VVRcQpXU53fWsbmmTLli1x2WWXDfg+73nPe+LGG2+MRx55JGbMmFGHzQAAAAAAmmdQF/MR8amImNLpeE1EPNCkXdiPG264Ia6++uq63Ov9739/3H///XHMMcfU5X4AAAAAAM0waIv5qqoWRsT1nU7tiYj/WkopTVqJblxzzTXxwQ9+MP7zP/9zwPc67LDD4jvf+U4dtgIAAAAAaI6W/eWvVVW9NyKO6nTqHRFxaEScEBHzI2Jmp4/tiojPlFL+rc47TIqIw/t4mfda2Y877rgj7rzzzliwYEFcfPHFMW/evBg+fHi/7nXqqafGueeeG//6r/9a5y0BAAAAABqvZYv5iFgUEf/tAI8pEfH/IuJzpZTVDdrhqgbc96C0e/fuWLZsWSxbtixGjRoVc+fOjTlz5sTs2bPjlFNOiZEjR/b6XhdccIFiHgAAAAAYlFq5mO+NZRHxdw0q5Wmg7du3xx133BF33HFHREQMHTo0PvCBD8R5550Xf/AHfxBTpkzp8frTTjstY00AAAAAgLobtO8x/5YLI+Leqqp+VFXVe5q9DP23e/fuuPfee2Px4sUxffr0uPHGG3t8/KRJk5I2AwAAAACor5Yt5ksp/72UUr09ETEqIt4dEedFxE0RsaPTw0+PiAerqvq1Oq9xfbz5nvZ9mfl13uGgs2vXrvjTP/3T2LBhQ7ePGTFiRN5CAAAAAAB1NGjeyqaUsiMinn1rvl9V1bXx5lvZnPjWQ8ZHxPKqqk4opbxcp8+5OSI29+Waqqrq8anbwtChQ2P37t39unb37t3x8MMPx9FHH73fj//yl78cwGYAAAAAAM3Tsj8xfyCllJ9GxNkR8fNOp6dGxOXN2YiubrnllvjiF78YEyZM6Nf148eP7/Zjzz//fH/XAgAAAABoqkFbzEdElFJejIirupz+ZBNWYT/GjRsXn//852P9+vXxla98JU466aReX3vaaafF3Llzu/34fffdV48VAQAAAADSDepi/i3fjYjS6XhKVVXTmrUM+xo3blxccskl8cgjj8Tjjz8eS5YsifPPPz8OP/zwfR579NFHx+c+97m48847Y+jQ7t9p6Qc/+EEjVwYAAAAAaJhB8x7z3SmlvFxV1S8j4rBOp4+IiI1NWokevO9974v3ve99ccUVV0RExOuvvx4vvvhidHR0xIQJE2LcuHEHvMdjjz0Wd911V6NXBQAAAABoiEFfzHfj9WYvQO+84x3viMmTJ/f68bt3745LL720gRsBAAAAADTWoH8rm6qqxkZE198u6jeDtqlFixbFD3/4w2avAQAAAADQb4O+mI+ID0VE1en4hYh4rkm70MmyZcviJz/5SV3u9cwzz8S5554bX//61+tyPwAAAACAZhnUxXxVVSMj4poup79XStnTjH2odeONN8b73//+OOaYY+JP/uRP4pvf/GY8/fTTvb5+165dcc8998RFF10Uxx13nF/4CgAAAAC0hZZ4j/mqqv4mIpaVUh7swzUTIuKWiHhvp9NvRMSX67weA7R+/fq44YYb4oYbboiIiFGjRsUxxxwT06dPj3e9610xZsyYGD16dOzevTu2bdsWL7/8cqxduzZ+8pOfxOuv+3UBAAAAAEB7qUopzd4hqqp6LCJ+JSIeiDfL9h9GxBOllNe7PK6KiGMj4qMRcUlETOxyq/9VSrm88Rt3r6qqWRHxH83cAQAAAACAPjuhlPJExidqiZ+Y7+TUtyYiYldVVZsi4uWI2BURYyPi3W/97/58IyKubPiGAAAAAAAwAK1WzHc2LCKm9+Jx2yJicUTcUFrhx/8BAAAAAKAHrVLMfywizo+Is+PNn5gfd4DHl4j4SUR8MyK+UUp5obHrAQAAAABAfbTEe8x3VlXVkIj4LxHxnog4Kt4s6d8REa9ExNaI2BARj5RStjVrx554j3kAAAAAgEHpoH2P+Sil7ImItW8NAAAAAAC0lSHNXgAAAAAAAA4minkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAAAAEikmAcAAAAAgESKeQAAAAAASKSYBwAAAACARIp5AAAAAABIpJgHAAAAAIBEinkAAAD+f3t3Hm3ZWdYJ+PeSyQAxgTgAYQoEwuxESDeIhMEJUAYDS+xuiGA7tApog4CKCCrKYAt0M7WwKGyFRmxbIAiaME/dIJNpQhDCGEIYIgkJJCQkb/+xT5Fzd92qe8+te/epe+t51qpV9X1n75OXxV5n+J1vvx8AABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDC/+Q5fdgEAAAAAACxssmxXML/5brLsAgAAAAAAWNhk2a5gHgAAAAAAJiSYBwAAAACACVV3L7uGHaWqjk5yj7mpzyW5YknlbBe3TPKaufEDkpy7pFrY2VxrTMW1xlRca0zFtcZUXGtMxbXGVFxrTMW1tjGHZ2X7mrd198VT/IcPneI/cjCZ/R/32mXXsZ1U1Xjq3O7+yDJqYWdzrTEV1xpTca0xFdcaU3GtMRXXGlNxrTEV19p++eAy/qNa2QAAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAEzo0GUXAEm+nOSpozFsBdcaU3GtMRXXGlNxrTEV1xpTca0xFdcaU3GtbTPV3cuuAQAAAAAADhpa2QAAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQOXXYBUFW3THKXJDdOcniSryY5J8m7u/vyZdYGsB5VVUlunuSOGV7LjknyzQyvZx9P8j6vZ2yGqjo8yW0yXG/HJTkqyWFJvpbkwiT/nOSj3X3VsmoEgO2kqk5M8n0ZPsNdO8llSb6Y5F+SfLi7v7nE8gDWpaqOSPIDSW6b5HpJjszwHeFLST6Q5BPd3curkNWU/09Ylqp6YJInJ/nBvRxyaZJdSZ7a3V+Zqi52lqo6LsMPPyfP/r5zhiBrt890982XUBrbXFVdL8kDk/xEknsl+a59HH5lktcneU53v22C8thBqurUJPdJcrcMofxaCysuTvLKJM/t7nO2uDwOQlX1yiQ/O5r2fgpsG1V1VJJfT/ILSY7fx6FXJHlvkr/p7udOURvbT1XtSvKITXo676cspKp+KMlvJDk1yRH7OPTzSV6a4TvCv05RG2sTzDO52a94L03y79Z5ypeTnNrdb9+6qthJqupuSf5zhjD+Rmsc7oMPC6uq52f4Inf4Bk7/iyS/3t1f29yq2Kmq6rwMq+MXdWWSp2f4gdsHPjZFVf1Ukteu8pD3U9atqn4/yVP24yle3t2nbU41HGyq6v5JXpLkexc47YvdfYMtKoltbpOD+Y9396036bnYwarqWhk+6z8+i7Uq/2KS07r7jVtSGAvRyoZJzV44XpXkAaOHrkry2Qyr/I5PcvTcY9+d5A1VdZ/ufs8khbLdnZTkQcsugh3t5Kweyl+V5AsZPuwcluRmWfl6liQPT3Kbqrp3d1+6pVWyk12ea943r5Xhjo2bJqm5Yw7LEHzdJMmjpi6Qnaeqjk7ywmXXAbBRVfUbSf40K98vk+F99fwkX8nQ/uGG2ffdkLBVTl92AWwbL86wWGzsG0nOzdCW69gkt8jK17zvTfKaqnpgd79hy6tknwTzTO3x2TOUf1GSP+ju85Nvh/cPSPKcDCFDMvT6++uqukN3XzxVsexIlya57rKLYEe5KMkrMrSqeUd3X7L7gao6JMndkzxt9vdud8nQquvU6cpkmzs/wzX29iTvSfKp7r56/oBZe6VTk/xehj65uz2yqt7Z3S+bqlh2rGflmrs3vp7kOkusBWAhVfWoJP9lNP2GJM9L8pZxL/mqulGGdoUPzPDZDfbmmUn+cgPn3SrJC0Zzu/a7Gna8WavLcSh/dobM7R+7+1tzx353kl9J8ju5ZnHZ4UleXlUndvdXJyiZvdDKhslU1bFJPpWV/b2f1N1/spfjj0vyzgwb3O32tO7en9teOQhU1WOT/FmSS5K8P8n7MvSGfF+GOzLeMne4W+9ZWFX9U4bVB3+Y5BXdfdkaxx+S4UP3L44euld3v2WVU+DbqupOSc5abzuaWUB/Zlbu4fKFJDceh/mwXlV1SpI3Z1hxdXWSJ2YIInbzfsq6rdLK5nFJPrzAU5zf3WdvalHsaFV1QpKzknzHbOrKJI/o7leu8/zrCa/YbFX19CRPmpv6YHfvbQ8++LaqOivJHeam/inJKd399X2cc68k/5CVi7R/u7v/eGuqZD0E80ymqp6R5Lfmpt6e4YVjrxdhVd07Q7iw2yVJju/uC7emSnaCqrplhk1PzlllRekpEcyzn6rqfknO6O4rFjjnkCT/J8MGxLu9orvXu98GrFtV3TbJR7LyttUf6e53LKkktrGqOjJDoHXL2dRzk/xdvJ+yQasE8/fs7rcupxoOBlX15iT3nJt6aHe/eln1wKxTwGey8i7Hx3T385ZUEttEVd0iQ6uaeXfp7vet49wXJfmluan3dPddN7M+FrPI5gCwYbM3nZ8fTf/+Wqv/uvtNSeZDhKOSPHSTy2OH6e5zu/tsK0PZKt39+kVC+dk5V2Xl6tIk+fHNqwqu0d0fzXDH0LzbLtsuPkwAABBsSURBVKMWdoQ/yDWh/GeT/O4SawFYSFU9ICtD+VcL5TkA3CcrQ/krM7THhLWcOBqft55QfuZ/jcYnbEI97AfBPFO5a4ZNXHf7ZJK3rvPcl47GD9yMggCWYLxa+diquvZSKuFgMF5JYxM7FlZVJyV57NzUr9q4Gthmxq0En7qUKmClR4zGp3f3V5ZSCdvN9Ufjzy1w7mdH42P2sxb2k2CeqdxvND5jvb1yk5wxGp9SVTYbA7aj1XqTHj15FRwsvmM0vmgpVbBtVdVhGRZIHDKbenV3n77EkgAWMtu3bP4OxQ9190eWVQ8kSVV9Z5IHjaZ3LaEUtqeLR+MjFzh3fKwfg5ZMMM9Uvn80fvd6T+zu85N8em7q8CS324SaAKZ23Cpz9sxg01VVJTlpND1ubQNreVKSO87+fVGSRy+xFoCN+Ilc8+NisnJvDFiWh2ZlQPqlJH+/pFrYfj40Gt92gcWrdxmN37sJ9bAfBPNMZdzX9uwFzx8fr08usB3dfTT+zKK96mGdHpnkRnPjc+KDNwuoqtsl+Z25qSd09wXLqgdgg8Y/Un949z+q6geq6nlV9eGq+mpVfaOqPl1VZ1TV42ar7WErnDYa/1V3f2sZhbD9dPd5WbnY9YisY/FEVR2Rle0Jkz1bRzOxQ5ddADtfVR2Z5Kaj6UV6YK12/HizC4Dt4JGjsZUxbLqqekSSF8xNXZ3k1xZoIcdBrqquleGL2uGzqXck+fPlVcTBYBYY3CLJsRk2Qbwwyfnd/Y2lFsZ2Nw7mP1lV103y3Oz5uSxJbjb7c58kT6uq5yR5SndfubVlcrCoqhOS3G00/bJl1MK29oQkb8s1C66fVlXnd/fLVzu4qo5J8j+ycpHr67r7dVtbJmsRzDOF70pSc+MrM9yqtYjPj8bfs18VAUysqu6b5EdG07uWUArbXFXdOit/8D4syfWS3CHJA7Ky3dsVSX6xu980XYXsAI9O8m9m/959Dflhh630/Ayh/HhvjG9V1fuTvCHJC7r7y5NXxnZ3wmh8dZK3J/mBdZx7ZIaWXidV1YO7+5LNLo6D0njT1w9091lLqYRtq7vfWVW/luH9szLku7uq6leT/G2SjyW5LEMed3KSn8vKTWPPSPKwSYtmVYJ5pnDd0fgbG/hy9/U1nhPggFVV10/y4tH033W31iJsxH9K8pg1jukkb0zypO7+8BrHwrdV1fFJ/nBu6o+7+5xl1cNBY2/7Rx2aIVA4OckTqurZSZ7a3VdNVhnb1uzun6NG08/LNaF8Jzk9wx2M5yW5zuyx/5CV7eDuk2Exxc9sYbkcBGZ7AD18NL1rCaWwA3T3C6vqYxle124/mz4pe94pNO+TSZ6Z5M+7++otLpF10GOeKYxD9Ms38ByXrfGcAAek2ZfCv0xy47npi2MTRbbWq5P8kVCeDfjvGcKpZNib4OlLrAXmHZnkyUnOnLUigbUcnZV3bifJD87+vjDJPbr7p7v7Rd19ene/qrufmKFt6itG5z24qsaBKizqXll51+MV2fNag3Xr7jdnCOKfnWStH60/OzvuFUL5A4dgnimMb0ndyEaH3xyNj1z1KIADz7OS/ORo7pe6e9G9NmARD03yzqp6+6yXKaypqh6VYWVoMqwk/UUbVLOFOsPmdb+T5Ecz/IB97QzfHY5L8lMZ7jYbL+o5Jcn/rKpDJquU7WpvP+BcleR+3f2O1R7s7kszrJr/x9FDvz1b8QwbNW5jc3p3X7iUStgRquqXk5yb5HFJ1npfvGmGfag+XVWr7bHBEgjmmcL4w/Thqx61b0es8ZwAB5yqenSS3xxNP7O7X7WMetgZuvux3V27/2QIsm6S5P4ZNuycv8vs7kneV1V3XkKpbCNVdcMMq6h2e8neQivYBP+Y5Dbdfbfufnp3n9ndn+/uy7r7m919/mwF8y8nuVWSd43Ov1+Gtl6wL3v7zviS7v6/+zpxtpr0VzL0pN/txCT32KTaOMjM7vR58Gh61xJKYQeoqsOq6m+SvDDJDWfT/5rkaUnukmH/qcMztOX66ST/O8MP4snQa/6lVfWsSYtmVYJ5pnDpaDxeQb8e4xXy4+cEOKBU1c8lec5oeleSJ05fDTvZLMg6r7tf392/kOROST40d8gxSf6uqo5ZToVsE8/PcK0kyQVJfmuJtbDDdfe7u/tf1nnseRnu5HjP6KHfraprb3px7CR7+8745+s5ubs/meTM0bRgno16SK5pFZckX8ywsTVsxAuzct+L9ya5fXc/pbvf190XdfeV3f2F7n5ddz84yQOz8gfLx1XVz09ZNHsSzDOF8Qeia2/gFsDrjMaCeeCAVVX3T/LyrOxr+rdJfmEDm1/DQrr7ExnaQsy3SzouyeOXUxEHuqp6SJIHzU09prsvWlY9MNbdl2fYMPFbc9Pfk+THllMR20F3X5Y9ey5fkuSDCzzN20Zjd6CxUaeNxn/V3d9a7UDYl6o6Jcmj5qa+lOT+3X3Bvs7r7tcm+dXR9LOqSqvoJRLMM4Wv5JpbZpLksAwfpBdx3Gj8pf2qCGCLVNU9M2y8eejc9BlJHtbda23IA5uiu7+S5Cmj6dOWUArbw/ytzK/v7r9eWiWwF7MfHV87mhbMs5bx98ZPLLjp4cdG40W/x0Kq6hYZ2gvO27WEUtgZHj0aP6e7v7zOc3clmb9j7djs2WKJCQnm2XKzlQqfHU3fdLVj92F8/Dkbrwhga1TVyRlCg/mWXe9O8iAbKLIE870kk+RGVXWzZRXDAW2+zdH9qqrX+pPkLaPnuNkqx33/hP8bODi8aTQ+cSlVsJ18dDT+2oLnj4+/3n7UwsHr4Vl5J+0HuvusZRXD9jXrPnGv0fTr1nv+7IfJ14+mf2R/62LjBPNMZRyk327B82+7xvMBLFVV3SlDn8jrzk1/MMl9u/vry6mKg9msFcm/jqZvsIxaADbJ50bj715KFWwnZ4/GRyx4/nh/tG/sRy0chGZB6sNH0y9bRi3sCNdLcvRo7lMLPsf4+HGHCiYkmGcqHxqN77reE6vqhkluPjd1Zfb8gAWwNFV1YoZ2NfOrqD6a5Me7++LlVAWrunLZBQDsh/Fr2GFLqYLt5AOj8fcueP64dc2F+1ELB6d7JDl+bnxFklcsqRa2v9V+XFx0r4Lxe+khG6yFTXDo2ofApjg9yRPmxvepqlrnJojj3pFv6W6bvwIHhFlrkDOz8ovbp5L86AK9/mDTVdVRSa4/mv7iMmrhgPeALB5wfl+SZ8+Nv5jk34+O+cT+FAWrGN/1432Wtbw+ydW5ZlHi8VV1/e4e31G2Nz80Go97zsNaHjEav26B6w/GVvtx8EZZbNX8eIW899IlEswzlXdn2AT2u2bjWyQ5JXv2J13No0bj12xeWQAbN7uj501Jbjw3/fkk9+7uzy+nKvi2+2VlP9MvJ/nCkmrhANbdb1v0nKoar866vLvP3KSSYG9+eDQet7aBFbr7S1X1rqzcePPBSV6y1rlVdWiSB42m37p51bHTVdV1kpw6mt61hFLYIbr7iqr6QpIbzk3fK8lLF3iae4/G5+53YWyYVjZMYrbBxK7R9FNm/db2qqrunZUfoi5J8tebWx3A4qrq+hna19xybvrLGVbKL9rnDzZVVR2Z5Kmj6dNn78cA205VHZPkZ0bT481gYTUvHo0fX1Xr6TX/H7PyLo2vJfmHTauKg8HPZOX+UxckeeOSamHnGL/3PXb2Q+KaquoeSf7tGs/HhATzTOkZSeZb0NwjK9vbrFBVx2XPlQzP7e6vbEFtAOs2axHyxiS3n5u+KMmPdfdHl1MVO1FVPbOqTlrwnOsneW2SW89NX5XkzzazNoCJPTvJMXPjKzJsug5reWWSs+bGt07y4qraax5SVScneeZo+gX2DmJBp43Gf9Xdi/YDh7G/HI3vkOQF+3pNS5KqOiF77m/w8STv2cTaWFCtr8U3bI6qelKSp4+mX5jkD7v7/Nkx10ry00mem+Smc8edn+T23X3RFLWyvVXV3ZIcucpD6+mJu9v53W2jYfZQVW/J0I5r3u9lYx9q3t/dX93votiRqupDGV633pvkVUnenOQj3X3l6LhKcmKShyR5dK5pHbfbs7v78VtfMQeLqjolK1sSfqa7b76cathOquqJSc7o7vev8/hDMyzw+c3RQ8/r7sdsdn3sTLM7sc/IyhZvZyZ54vy1WFVHZ2il+tSsXOn8L0nu3N2XTFAuO8BsH6pPZeU1d8fu/n9LKokdpKrenOSeo+l3Jvn9JG+b/wGoqo7N8CPRk5McPTrnId39N1tXKWsRzDOpWej+miT3Hz10VZLPJLk4w47lx4wevyxDe4h3bXmR7AhV9ekkN9vPp3l5d5+2/9Ww01TVZr553rO737qJz8cOMhfMz7siw14GF83+fVSSm8z+Xs3LkzxSGxs2k2Cejaqqt2a4c/bdGVpUvinJOeNVpLOA9L5JfivJ94+e5twkJ3f3apvgwaqq6glJ/mSVhy5Icl6S62RoUXj46PELM3xeO2t8IuxNVT05ydPmpt7f3XdeVj3sLFV1gwzvo8ev8vClGX4UuizJsRn2eFytjfSfdvfjtqxI1sXmr0yqu6+uqockeVmSn5176JAMLxaruTDJqUJ5AEgyBAarfQgf+1qSJyZ5UVuJARx47jr7kyTfrKrzMizSuSpDkHDzrN569YIkPymUZ1Hd/Yyq+kaSP01y2NxDN8jKXvLzPpbkp7r741tdHzvOI0bjXcsogp2puy+Y9Yv/i+x5J/d1k9xxH6dfmWH1/LhdF0ugxzyT6+7Lu/thGXYn/9A+Dv16khckuZ3VpAAcpB6WYT+WMzME7WvpJP+c5PFJTujuFwrlgW3giAwrlX8wyUkZFuys9l3175N8n5CUjeru/5rkThnaw125j0M/leQxSe7kemNRVfXDGV7Tdrsie/b2hv3S3Z9Lcu8kD03y1iRr3R17cYZW0nfs7mf4jnBg0MqGpZttQHFykuMyrAK8KMlHk7yruy9fZm0AcKCYtYO7VZITMuzB8p0ZVvxdkuGD9qeTfKC71xPgAyxFVf1okgcnuXuS22S4c3ZfLs2wyet/6+63b3F5HESq6jsz3LVxqwx9ly/NsP/UB7r7Y8usDWBRVXVUkjtn+HH7mCTfkWFhz4UZFu6crbXlgUcwDwAAwOSq6tpJbpehbc0NM9x+f60MC3W+muTsJGd191XLqhEAYKsI5gEAAAAAYEJ6zAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABMSzAMAAAAAwIQE8wAAAAAAMCHBPAAAAAAATEgwDwAAAAAAExLMAwAAAADAhATzAAAAAAAwIcE8AAAAAABMSDAPAAAAAAATEswDAAAAAMCEBPMAAAAAADAhwTwAAAAAAExIMA8AAAAAABP6/6XoI0WQZI4RAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "HfoMKFQkGOlN"
+ },
+ "source": [
+ "#testing trained SC on inverted environment\n",
+ "inv_classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=inv_agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " inv_classified[i,j]=np.round(classifier(dist).cpu().detach().numpy()[0][0],2)\n",
+ "inv_classified[0,1:]=0\n"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "SionF_AjdiEv"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC tested on Inv Windy CW - Alternate labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(inv_classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if inv_classified[i,j]>0.5 else 'white'\n",
+ " if i==0 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==0 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(inv_binary[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 721
+ },
+ "id": "gru8bJT7GT9_",
+ "outputId": "4446006f-901b-4cdc-a783-ed31d4e5e06e"
+ },
+ "source": [
+ "inv_dev=(inv_pol!=inv_display_reference).astype(float)\n",
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SC tested on Inv Windy CW - Original labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(inv_classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if inv_classified[i,j]>0.5 else 'white'\n",
+ " if i==0 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==0 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(inv_dev[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "N4tuDVa005VP"
+ },
+ "source": [
+ "#repeating DSC on Modified WCW\n",
+ "m_classified=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1])\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " dist=m_agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " m_classified[i,j]=np.round(classifier(dist).cpu().detach().numpy()[0][0],3)\n",
+ "m_classified[2,1:]=0\n",
+ "#m_classified[3,6:]=0.2"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "Dwa0xDA0Ww-S",
+ "outputId": "1fd428f7-0289-49fb-9761-3ac953f25b81"
+ },
+ "source": [
+ "#Modified WCW expert policy\n",
+ "d={0:'U',\n",
+ " 1:'R',\n",
+ " 2:'D',\n",
+ " 3:'L'}\n",
+ "m_reference=torch.full([5,5],1)\n",
+ "m_reference[1:3,:-2]=0\n",
+ "m_reference[3,:-2]=2\n",
+ "m_reference[3:,-1]=0\n",
+ "m_reference[0:2,-1]=2\n",
+ "m_display_pol=torch.clone(m_reference).numpy().astype('object')\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " m_display_pol[i,j]=d[m_reference[i,j].item()]\n",
+ "m_display_pol"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['R', 'R', 'R', 'R', 'D'],\n",
+ " ['U', 'U', 'U', 'R', 'D'],\n",
+ " ['U', 'U', 'U', 'R', 'R'],\n",
+ " ['D', 'D', 'D', 'R', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 37
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "If0r2MD_XciP",
+ "outputId": "ff17b23a-4d21-48c4-e453-fcabab73bb08"
+ },
+ "source": [
+ "m_pol #policy from the agent"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "array([['R', 'R', 'R', 'R', 'D'],\n",
+ " ['U', 'R', 'R', 'R', 'D'],\n",
+ " ['U', 'L', 'R', 'R', 'U'],\n",
+ " ['D', 'R', 'R', 'R', 'U'],\n",
+ " ['R', 'R', 'R', 'R', 'U']], dtype=object)"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 40
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "ijAaAXUeYKsc"
+ },
+ "source": [
+ "m_dev=(m_pol!=m_display_pol).astype(float) #original labels (deviations from expert policy) for the modified WCW environment, "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 890
+ },
+ "id": "RyYEdKnf0_VM",
+ "outputId": "1c0479b9-0ee4-4352-f587-301357663ca9"
+ },
+ "source": [
+ "plt.figure(figsize=[4,3],dpi=300)\n",
+ "plt.imshow(m_classified,cmap='gray')\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " f='bold' \n",
+ "\n",
+ " if m_env.is_cliff([i,j]):\n",
+ " text='T'\n",
+ " elif m_env.is_goal([i,j]):\n",
+ " text='T'\n",
+ " elif i==2 and j==0:\n",
+ " text='S'\n",
+ " else: \n",
+ " f='regular'\n",
+ " text=str(int(m_dev[i,j]))#+'/'+str(inv_display_reference[i,j])\n",
+ " if m_classified[i,j]>0.5:\n",
+ " color='black'\n",
+ " else:\n",
+ " color='w'\n",
+ " text = plt.text(j, i, text,\n",
+ " ha=\"center\", va=\"center\", color=color,fontweight=f)\n",
+ "plt.title('DSC tested on Modified Windy CW')\n",
+ "plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "PqXWfr7GACmp"
+ },
+ "source": [
+ "**Now we test a state SC, ie a SSC**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "I3RO5Gojdoti"
+ },
+ "source": [
+ "class SSC(nn.Module):\n",
+ " def __init__(\n",
+ " self):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(SSC, self).__init__()\n",
+ " self.fwd=nn.Sequential(\n",
+ " nn.Linear(2,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,1),\n",
+ " nn.Sigmoid()\n",
+ " )\n",
+ " def forward(self,x): #x is N x action_dim x size\n",
+ " \n",
+ " return self.fwd(x)\n",
+ "\n",
+ "class SSC(nn.Module):\n",
+ " def __init__(\n",
+ " self):\n",
+ " \"\"\"Initialization.\"\"\"\n",
+ " super(SSC, self).__init__()\n",
+ " self.fwd=nn.Sequential(\n",
+ " nn.Linear(2,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,300),\n",
+ " nn.ReLU(),\n",
+ " nn.Linear(300,1),\n",
+ " nn.Sigmoid()\n",
+ " )\n",
+ " def forward(self,x): #x is N x action_dim x size\n",
+ " \n",
+ " return self.fwd(x) "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "oZ4JNkGPhXtr"
+ },
+ "source": [
+ "#training on full environment with original labels\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SSC().to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=torch.tensor(dev)[x,y].squeeze().to('cuda').float()\n",
+ " pred=classifier(pos)\n",
+ " #target=classifier.compute_target(dist,labels)\n",
+ " loss=bce(pred.squeeze(),labels)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "qmoodWSIhoxU"
+ },
+ "source": [
+ "#training results\n",
+ "s_classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " s_classified[i,j]=np.round(classifier(torch.tensor([i,j]).to('cuda').float()).item(),3)\n",
+ "classified[3,1:]=0 "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "GROJhybch__o",
+ "outputId": "34f258e6-b430-4b3a-fab2-c90900fa6ca4"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SSC trained on Windy CW - Original labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(s_classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if s_classified[i,j]>0.5 else 'white'\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==3 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(dev[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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2eN7VzOx0hfdsZzV/v01hzYozzOy3iT/HTZnZxmZ2nkKwYx+FEVqtbKwwi+YOM/tCNvKuinqsZGZnKvwdW+VZN4UR/eea2UnZzKJULszZVnSk7kj0eFSSPKzPcWnN9qUkvbLDY0qJ03FkM0QulXSCwuixZiZLeq2kK8zsv7s43zYKN0i+qfwUa2OmSjpQ0t/N7LMdnOr/FEYp1jq0g+PUOkBhdGqtH3R5zGbyfrvP8f6M4Kv8c5PpNM98fExpgqSxUVjPJ/7Ofkwh2FQJd39W0k9znnpbtmZRaWa2vJn9XmHB7NepccR/z2R9i9MVblzvqtZpb7eWdLKkC8xstRT1K8rMdlboH31Rod/VzHIK/bbbzWyfkueYZmbXKgSZvyZpDzWuT5VnJYXv6yvN7PRsRsRQG7T+vplNNrPjFL5j91Drz9i2kn4i6WIz26iLcy5jZt+UdIfCzYZWv+lSuCm/s8L31fXZjLmeM7NVzOxUhXWB/k3SMgV3XU3SmyT9WNJxPaoeMLQIzAOo9WDOtrcmOvdchVF/taYojK7oOTPbQmHEc+xHKc4/HpnZWpIul7RLh4fYRmFEU6dWkfR7Mzuyi2N0zcy+qtAJLXMRvqGkS7NAX9nzvUjSlZLep84u2HeV9Dcz267keadJ+qvCaOgy3iLpj1namL4xs5crtNc9Oth9OYWUCL/J0mZ0U49pCsHdvUvuepDCCMtU8oJ3IwX3jV93UZP/L3TMJvnlFyrMNkjCzDZWmL1Q9EbCmM+Z2Vc6ON92Cu9BmYvxKZK+amZxao2WPCx+eUK0eYfsxkCnPhw9flzhBm6lzGwl5f8G9eu3e2A+Ny1eN1EC83nfsb/06tO6/VxhxG2t5VR+kV6Z2XIKv7MHVFCvsudeTSEYV/Y3fkQhaLlK5ZXqgJm9SWHGUpn+1TKS/mBme5bYZwWFfmw39pV0daeDJcaRgenvZynDfinp30vu+mpJF2V9gbLnXEvhJsCnVO5aYcxLJV1uZm/sYN/Css/wqBJdewNYghzzAGpdrjCSodbrzOyj7t7TRVjd3c3sCjVeRH7LzK723ud6zruAmu3uzRZ5G0R3K+RxlkIewPWi52coTI1sJe/mTJ5Jkn6rMKJ4zExJ5yjk0HxY0jSFXPNFLzBvV6j/DEkPKKQZWaRw8bO+Qsd+D9WP3jCFQNRN7n5OwfNUxsw+JKl2hOpDkv4o6W/Z/y8t6YUKndwto93XUAgevanE+bZQCOrmXQBfrZA+5naFUYFTFVJE7aQwJb42oLyWwijsbd39ngLnfYHCdN+X5jx9n6Q/KLxvjyqMTttG0n419dxFISVMX5jZ5goXG3kj5P8p6TSFUUyPK/xttlf+iPoDFd7TskH1MVMkna76YOt1Cn/bmQo3KFdVyHW6v8LIulrvNLPT3f33HZ6/jIsV0hjU3vwZabeTma2q+vzy8xT+jWPyRv5+qc1hX6HG9+KKHgTYmllB4XM9Fuhxhd/Lvyh8785XyA/7KoXPenzhfaSZneXuVxU5WXbh/2c1vv9SyGN+hkJKgacUcj+PKHw3jt38OkLh+6eM4xVmh9QO2vmwGvsEbZnZlgoj/Wqd4u4Lyh6rgFcrjCystViN7SyVWxT+9qvXbHu5ma3g7k802ymbjbNrzSZX/b/h4miXkXYVyYJB8ajMWX2aSZCUma2gsP5DrNL1giTJ3Web2XUKeeBrvaaD8/1Y4fdnzP0K/aqbFNY+WVFhhlyp0d3tZDc//6j83/gHFH63blbo262qsJ7Nvgp9PCnkoD65yjp1aGuF0etj34VPKdzouFihf/usQt94D4VZTbWmSPqpmW3p7vM6OPd8hb7fDIV+xTyFfuxUhb7QFgptIl7zYVOFm/67ZjMwJoJ+9vc/qTASfMwTCr+pf1MY1b+ywvfmAWq8jlpPYYbI1u4+t8jJspnFVyp/ZuvNCt/ztyj0/6RwbfBKSW9U/bo6KyjcPNrJ3a9Tb/yP8r8D/qHQ37lNYS2dhQp9spUVZj9spdBv7WrgCjCh9TuXDoVCGZyicCHZLJfcmZJe3ePz/3uTc89XyBM4vYfn/lnOec/q93vSxb/n4Jx/z4ZdHG80OlZtPsaHFeUZjfY1SUvlbD9RIbj1AUnrFqzH8gpBvIVRfeZIWqbgMUZy/jYjBfY7Kme/p7QkF+SRapIvO/sbfEz5+VoL5aFWGIF3S147lbR5m33XknRKzr5XSZrU4b/9aYUA4OQW9f1OzesXZ/vUHuOkBJ+FpSRdn1P/RyS9q8V+q2RtNO876eMFzz2rSXtxhQvT3Vrsu7rCugDxuWf0+m9WU4erc86/RZt99otef3b0/BTV5/Zf2O6zm3224np8sUD9R6N9Rgv+u5t9zl3hAnvbFvtuqJCTNj7Gnwqe2xRGrcb73y9p7xb7baCQomns9Xm/5Qe3OfdZ0evnS1qxg3bzvZxzb9WjNvqFnHPdlOoz0qROv82p0xvb7LN19Pqbc15zW/Sajdsc82059Tihn3+bhO/BLjn/dpe0eo/Od3zOuf7SZp+TotfX9quelPRxSS9osX9Df6OL77wv5tR/kcLAg9w6ZN9Vh6r++zz+3il6/vjcRxXYZyRnv9rv6pMlrd1m/0dyjnFkwTpvqDAI4nsKN9WavlfRfjspBIHj836qRHvr6H2uoJ3Pis57UsH9TlR/+vtHtWkjJ0haucm+kxSC+E/lHOPEguefpHBjKN7/MrVZY0Ah6P1tNa7lNFPSClW3EYWbDvG55qhFvyPafzmF/t/ZCjOTet4eKZRhKqSyAfA8d39IzfPC7S3pEjO718xOMLMPmNnLKs55/FNJeaN3l1PonN1rZleZ2TFmtl/FUz9fnLPtmgqPP2zGRtHOlrSLu5/i7p73Qg/iNEWSdLi77+TuP3H3e4uc1N3nu/uXJe2l+qnjqyvcHEhtaYWLhje4+zHeZARv9jc4ViGIFHt/wXN9U2HEVa0j3H1vd5/Rakd3f9Dd362Q377W9gqjwJsys00kfS7a/Kykt7n71z3kDc875wJ3P1xhFK6U3aBpda4e+aTqR3BLYWT/a9z9F812cvfH3P0QSV/NefrrnaQh0pKR1H+T9Ep3v6DF+R9SGJF4c/TUZmYWp3XplQtztrVLzzASPR6tfeBhRODlNZumKgQqyhxTSpuOY+x9O1vhJt61zV7oYTTy7grfjbV2N7P1C5zr3Wr8Gz+o8D17Vovz/kvSngo5fKXieWFrxb//y6nk92qWiuM90eZL3D1ux1UZxN/uyj83mbJ55uNjShMnjU1eu7g7+17thbw2l1eHVsb6VQsUFgz+rrs/0+zFzfobZZnZepI+H20e+43/WrM6ZP2aHyqM7B2bDdPJ906Vxr6rv+Du73H3B5q90N1HtWSh8lrvK3iu+xUGDH3U3S9q9V5F571cYUbRn6KnPpZ4HZmUBqm/P9ZGjnH393qTke/uvtjdv62QjjGeyXCwmRVJ4/kpSbtF276vMNCt5Qw6d5/r7p9UY3vcSI2p4qqwjxrXjXpLq35HrazPf7q7v0nhBgyAEgjMA4h9Ua3z9q6jsNDajxVGoT6RBcuPNbMDsyl7HcmCt29V/kKw0pKFEI9QSD1xj5nNNrNzzezzZrZzF/mf4+mKUvG0LhPZ+9391k529M6mCo/t+xeF0dh1den0eF36tLv/teBrv6mQ+qXW69vtlAWB48758e7+jYLnlSS5+1EKo2prtcvZeZgaU0V8zd1PL3jO/1VYXDK5LAXPR3KeOtjdbyxyDHf/vBovoJdS+fykY+ZKOtDd48U28869UNJncp5q22YqkhfEG2mzT/x8XkqRwvmys0DFq6LNTynM9khplsIMi7bBsOy9jW+CTVII2Lfz8Zxt73L3Owqc9zlJ75J0V4Hz5PmzQgqGWmUXgX2HGnMJ92rRV2kwf7v7/rlp8fxECcznDdyIf3urlHfstc2sk3VgjnT3OHVRL31QS1K/jPm2u5+a9+KYu1+i+pR+/Xaquxda18PdL5X0u2jzC7MBCe32XeRhfY7Sst+RgxRmGIwZS7EzdAawvz/q7oXarLufLSmvPX2s1X5mtqykT0ebz3H3jzQbyNTk/CeqcYHpT/Rg3aY4d/4/3b2jlHDu/mT7VwGoRWAeQJ2ss/hGhSntRSytECz/qELn9gEzGzWz93YSJHf3KxU6pk1HuUTWUMif/RWFHJJzzOxnZtZuBGYsL2d3ofyBE9iFWYe1X+J8pttkHeGUZiqMfikkG1H1m2jzuma2RptdP6H6C+fH1T6g3szR0eNtzGyDvBdmn+GDo833K+RwLeOTalzcOYUDFPLs1zq36AigGh9RSENU64NZ4L+s77r73SVe/2c15gvPy53cC5eqcaTYrtniaQ2y/PK1+UmfkJQ3urzMyN/t1Jhf/jJ3b7deRtW+XDK48Gs1tpmW71u24Ou20ebTStz4UxYo+lTR10f7ukJKjlpbmVmcL76VOJD/kMIaFL0ycL/d7n6bGm8ObJMtVNsgyy8fj77s9obW2mocsX1H0dGqQ2DVnG0dBwcLyDv2ZJVf8PJOlehTdCtre3GQ81E19hPa+b5CerZ+W6z8m9mt5M2c6/lvrLvPUeNN/1Sz4cabqvv7LYPqOb4uKf7u3Df7nm3mvZJWq3m8WOFauRNHq35mx1oqvxB9OytEjx+p+PgAWiAwD6BBNq1vX4Xp6G1H6UVMIdfizyT9w8ze2cH5L1EI7nxHzUfPN7OiQmfoMjM728yKTiXOm37by4u4YfCzPp8/Htk5RWGRyJROcPfFJfe5Omdbu3Yap5v5rbdYSLCNy9UYuBpp8tod1Bjg+EXZUWLZdPKywfAq5I1QjgOPbbn7nQoLtNaapsYgahE/KXnu5xRyltcqmyKhI+4+tphdrdXVuJDxmF1VPxX60iapjq5W/UjB7VpcZI/kbEs96neBpF+V2cHdH1Pjd1S79+0NOdtKtZfMGep81PiJWpKWYkyhUfNmtr0aPxMn9PgmyqD+dsdtdLIaF8Qd8zLV32CYkQXt6rj7farvk61rZi9scsyRAnUaZnntopc3bJodu2xqlxPLjKatwOYKQb5avy474jXrB51UVaW6cEH2e11GJ32yqsS/ETsmOu94U2V//0p3v6nMDtmgtVNy6vC6FrvF/fYL3L2j2Wzufo/CAtC1Rjo5VgtxIP4lzW4mA6gegXkAubLckacodNr3VkhF8XjJw6wv6RdmdmLZ0fPu/rC7f0JhQbuPKyy4VzYAupeka8xs/wKvzRsBmvLiaDyq9CLfzCab2avN7CNm9iMzO9PMLjSza8zs+rgotIlYkRzOVepkmmfeRWPTzm82mj0e0X5ZB+eV9PwF9L+izds0eXneRWKh6e0V7teNOAXKAjWOUCvqtwWO386dWXCtrLjNpLxYKpMveyR6PJr3omzmSG3KtKlq/reMj9msTr10ZYfB5bLvW/x5m68wY6KU7GbImWX3y/adp8YRpAea2eoFdo/z3i6W9KNO6lHCoP52V/65yRSdbRIfU5pYgfnUmrW53NlFLaR+j/J+48/t8Fj9nEE5pnSfzN1nq/FmZEe/sWa2jpm9xcz+y8x+Y2Z/MrPLzey6Jv3Yg6NDpO7D9kWf+/uF0jDmyOvD5t5Iya55d4g2d9xvz8RB/Wb99k7F6QGXk/TrbCYkgB4b1gVGAFTEw0J9Z0s6O8uVubXCVMvtFDoFL9aSBauaOVjSspL+rYPzz5F0rKRjszv3OykEcLbNzh+P9IktL+l3ZrZvm7QrT6kxXcLKZes7gcx29/urOJCZraWQluVtkjpeoyCT+j2LR/EUkTeas9VFYF7A8j/N7PAOzj0mHmW5Wu6rGqdzPyvphg7PGY/67qlsEcoXRZuvazKCu4h45LhUfsR8J+1FamwzqQPz8eK/I5K+l/PakehxqyDJRZJeG+1bt/5Blioobv95o/h7LdX7Fn/ebuiivXbzeTtO0odqHk9VmIn29WY7mNkqavyNP6/TEYIl5M3eGYTf7jJ55uPt7T43teuNjCh/VkXeuUZbHHfY5LWLXn5vNmtzZUaeu8LaTSm9JGdb08Wt27hNYZbr0u1e2EPdfFcvV/O4VFsxswMV1uPZVd0NfByE766eGZD+fqe/jTcp9IFr42fNUh69XI2fg/ea2SU78vYAACAASURBVH4dnltqvBHRrN/eqfMU0sjWpufZU9JMM/ulpN8rzIIstMgxgHIIzAMorCalwvOdmiz9wA4Ko7beImmzJru/1cwudfe8YE7R88+T9MesjJ1/HYXp4XtKerPy83lOkvRLM9uixWjVR0VgvoyGafadMLMPKiyIWjYPazOpp122XcAzR16ntlWu8rxF7NouTFbStCbb49z3dxdZ/LKJfyrk3O5kMbxOTFPjaMUZXRzvNoURwLUX3WUvjDppL1Jjm0nZf7tM0iLVr3Gwq5lZbcoFM5um+iDPfLW+AC4y8nc71QdLpHBhGOe977Wq3remn/Msb3/cnrrJ2Xxbpzu6+81mdpFCkGnMh8zsGy3SbBysxrQdvVz0dUzee9P33253v8PM7lX99/fWZrZyli5QUqn88s2ea/jcmNl0Nd6UvC1LKdZTZnaupOkld3u/u19TcVUey9nWy3aRd+znVG6m6fw+LJoY//Yv6rSduPuzZnaPpE27r1bHev5dXSv7rJ0iabcOzxsb2tQhA9Tf7+h31d0Xmtldqm/fzdaHyuu3r6f8xco71azf3hF3f8rMPqIQgK/tO6+kcNPpMElPmtkVCqPrr5J0SZa2D0CXSGUDoCvu/qS7X+juX3T3zRUC5Lc0efl/Vr04p7vf5+6/dveDFS4GP6v8kVIrZs81k7cgWrejOYZZ2bRGDczsMwppDqrqpEsFL6aqkmjkSKWd7yaa5cGNgw0d527OAnqd5sXvRKWLQmYpgOJ2X3aK77gbaZStJxBPcZ6m+kVepcb88pe1CaBfpfo1RLbLZjnUGsnZrx/pOFK8byuqsV/eTa70bvOsxwtQbiTp9S1e/6Ho8d2SzumyDkUM8m933FYnqf5mhxRmIdZ+z97u7k3XB8gWjp5Vs2ntnLV0RgrUpVe2UMiZX6bEgyKqkNcuyt4wKCNvEcgHS8546bpf1YH4d7Lb741+r++Q7Dc2Gxh0kaoLyktDOmhywPr7Vf6uNrvZ189+e8fc/VRJ71JjaqcxyyrMdPycwlo2D2fph44wsypvOgATDoF5AJVy9/MURjn+MefpNSTt08NzL3D3YxTS3eQF4A4ys2ad3rwRFKkXEh1PuhqxamavVn5ahLFFFg9TCC68UOHCcVlJk9zdaks3dRhH8gLMqawQPW7WWS+q2/3LiOtexfnj/fPOMYyKpOWIH4+2OqC7L1R9wH+KQpq0VsdsVpdhUHV77batnyYpnmEW55CXJJnZbmpcLPHHHSyM3YlB/u2u/HOTiUfNtztms7oMs7x2sYGZVZ3+YUxemys7Mjf1TCBJitd/6nah5oVd7j+enKTGtIBSSEf0NYVZvNsqpNxcUdLUnD7sl1NVtl8GsL9f5e9qsz5gP/vtXXH3Xyn8nh+v9oNpJimk7TlG0p1m9tOC69EAiBCYB1C5bITl2yQ9nPP0a3O2VX3+6xU6erHl1bgYz5i8lAvbVVYpxP43Z9tJktZ193e6+w/c/SJ3v9Pd57r7U3EKBTOrfLTIgMqbAfKG+KKlyzLS5Nxxpzwe0VxWt/uXkXdBUXX9U84A6KciC1nGj4sswtc0LUeT/PKPq/P8x4Ou6vbaVVvPZjvEC7fu1WRUXBywf0bSz7o5fwl5v91b5My+6Ifkn5smj10TK7+8FIKjeWmXetWvyzvuePiuikcAd3uzucoR0QPLzPaS9Lpo8xxJr3f3bdz9c+5+urtf5+6z3f2JJjMsJ0I/dtD6+1X+rjbrA+b12z9ccb99wy7+HS1ls9H/XWH22f4K687coJCeq5kXKKx/cqOZlV1/CZjwCMwD6Al3f1yh4xWLR9X1yq8lPVTi/KM529Yysy0qqxEkSWb2QjWOLjvL3Q+pzb1bQNk0IuNV3g2uVP/2+P3oOP9plkM75QjzSnMMZ7mg46BDp/lsx5srVJ92RpJ2yf4mykahblnz3AIVW6C11cjf7RVGztW6uGRqiPHkcYU1DGp1k2+4ilzFP1b9CNrJkj5Y+4JsMb94QbvTW6VjqdilahxpPFmNKWOSc/dZqk87I0kvNbNVpee/U3aOnh8tcOj4c/P8vzVLrxGP4r3F3fP6Q5Vz9w07CDCN9qAe8yRdl/PUnlWfKxshuk3OU+NhlkL8O7mCmU3NfWUxKVJ4DIK3R4+fk7S3u/+55HGGuh87oP39Kn9Xm/0b+tlvr0x2k+Q0d/+ou4+lXdtdYabHVWrss0hhhsg52bpDAAoiMA+gl67O2daracR1stEWeYGh3PO7+y2SZuY8FefNRfd2z9l2dAfH2bjbiowTs3O2bZDo3PEiv+ub2dIdHmtTpVv4VQoXRvGIyc27ON6L1dhvyrv4GjpZ2pkros2rKOSHlhrzy1/eJr/8mCtUH/h9uZmN3bwZyXn9eAh0dST7zYrbUzc3spstxF6Yu89WWAiu1vuilHDvV2Ne5BSLvkp6PgB7cc5Tg/LbHbdZ05JA+jaqD/T80wssvOnuM1WfQ30tMxv7bhspUIeJ4qycbe8yszh9S7cOUmO+6wUaH3/3e6LHpvqbrIVlgbhe5vEfJHE/9k/unnfN086w92MHsb8fL4xdSHbDasNoc9xHHtPPfnvPuPt8d/+Lux/l7jsq/Ju+psaBG2tJ+kzyCgLjGIF5AL2Ut8BOyhyaZc9/Qs62gwZkSvwwiVMhPO3u13RwnFdWUZlxIF54U5J2SXTuOE3EFC0Jxpb18i7rUoq7P6nGHL9bm1mnNwfyUhXkpdEYVq3yZY9E20eLHDBLe1Z7A7U2z3x8zGZ1GCZxe3pZF+21qs9bvAjs2spGyGd1+2D0/G3unvp9yvvt3svM1k9cjzyVf24yzWabxMdsVoeJ4GQ13pxdVdK/VXWC7DPw/pynfpN9vw26vGDyjh0eq9P9xpXsxs4a0eZLOjjOZIWZYcNsEPv7nf42vlSNN+Ca9QGvUeNo8lT99mTc/V53/5ykPdSY5uaAPlQJGLcIzAPopTVztuWNIhiU8/9EjQv7rKT8RYsGXd4NiJSjlVuJZy10mg7krd1WZJy4UY2jcnZJNE30ypxtb+7wWPt3U5EOXR49Xl6dpzJ4S4HjD7NW+bI7yZM9ZjQ+ZjYybado+2MKOU6HWfx5W175Iw5bygI+lSy07u6XqzElyFhO+b3UGHj5YRXnLen3alyodrIabyr0Q7LPTZNjesnjDo1sZsHZOU8dY2ZVpHqSpE+ocWaLSzq2ouP32lVqvHnxjg6P9c4u6zJe5PW9OunHvlHhO36YDWJ/P069VlReHzavjyx3f1SNQfvNhjU9qrtfosYZSpuYWZyOEEATBOYB9NJuOdvuTHHirDOQt9Br0/O7+xzlL1J0mJntUVXdJMnMXmpm8cJRVcpbkGhQLgDimx+rjOWqLsrMdlXiEdj9kqW4ODPavKykjyc4/VVqvJB6d9l0Nlke6r0rq1Vx5+VsO7TsQcxsIzUG9B/W+FjcrypXS3oy2razma0pqfZi80nlj8JsJm/kb15++YvcPS+f6TD5Y862D3RwnL0VppJX5bjo8WvM7EVq/Cw9KennFZ63kCzV0pdynnqTmXXy92vKzNY3swOLvt7d75V0R7R5q+xz8+po+2iJqjTkmc8W5t0k2n5DFiSaqL6gxpGra0v6n24PnOXP/nLOU79193FxE9HdH5MU50V/tZmVGqFsZpuoPzff+yHuw0qdpen8j24rMg4MYn//lWZWKl1TNkvi3dHmZyX9pcVuZ+RsO7LMeceZ23K2VXUDFBh6BOYBPM/M9s4CUFUcaxPlj3A4p8U+H+oif3Xs45LiFDQPqX3A6KtqTH9hkn5nZnk3Gkozs4MkXSZp3SqO10TewpeDkssyzqG7jBoXwGsqu+ny40prNPiOUeMsiCPMLO/mU2WygNdJ0ebpKn9x8S1JVef1LeI0Nba3N5nZG0oe53tqzKP9I3d/puOajTPuvkjhe6vWypIOV31++StK/l0uV33b3lb5o72HPh2Hu/9NjTd79jezeBR0U9lv6LcqrZj0f6q/QWcK30mvj17365IL+lXpBOUHto8zs05HANfJvjf+Jmmrkrvm5Zk/XPVBizvdPR7135S7/1P1321rSDqswLknlCxAnjd6/b1m1kmua0nhBo2k89V4A3Guxl/ANW9NiB8WHe2arTnxI/XnNz65bF2L+CZ1qcE7ZvZ+5aedGjaD2t//bsnXf0aN12xntFkT5Dg1Lg77LjPrdNbpoFs7epy3bg6AJgjMA6i1l6R/mNmJZtbxwnFmNl0hIBZ36h9SuJBp5ruSZprZ4WbW8ehuMztA0lE5T/2m3YjLLCfo29U4ymNFSX/Mbh509N1pZi80s7MUAp29Hr1+c862N/b4nEXl5eL8bzOLczc2MLNlJP1OHS7eNF65+52STow2T5V0hpnFKT8KMbPJZvY2M/vvNi/9gaQ40Pp5M3tTwfN8TH2a4p4FiL+X89TJRacUZ8GbvaLNT0s6vsvqjUd5Qb6PRI9HyxzQ3Rco5GMdM1lLUqW0O/cwygsY/MLM2t5YzVLYnKzGUdNdyX4XfxZtfrMaryP6kcZG0vMzi96jxnR1UxX+fl/odNFPM1vbzE6SdK4ac0sXUfnnJhOPmo+P2ezcE83n1JiOSZK+YGb/08EMsG0V3q8No6dc0gfc/f5OKtlHZ6vxhuBLJZ3VLuVP9rf7haTX9qhug+rS6PGImRXqY5vZnho/qY66Naj9/dea2VeKvDC7IfuFnKdavofZDZxvxodT6H/uW6iWTepjZpUvsG5mR3U62CebrRXfcJgxkQavAN0iMA8gNkXSwZJmmNmVZvYRM4vvgucys2XN7FCFC6CX5Lzk0+4er9weW1shncxsM/tl1gEpdNFkZhuZ2U8l/VbhYrzWI8oP1jdw9+sUFgdbFD01VSHwcKOZHZh1GtvVaZKZjZjZLxWm+RUKZnYrm7oeTys8xMw+UWFu1U5dLelf0bZXSTrVzJpOBzaz7SRdrCU3GB7vTfUG1n9IuinatqakUTP7epYupi0ze1l2QXKHwkjYlou5uvsdCjNJak2R9Hsz+1SzxSmz74NvS/rO2KEkLSxSx4p9W9L10bbVFP5uTRcBNLOVs++TvAuyI8Zh8KUKeUG++CZjJ/ms433iYz6s/JuNw+gUNQZpp0u62MziG0TPy0bwnqMlayFUvfDk8WpMCVLr79mI/75x93sUfmPjhd9N0tEKAw/ea2YrFjmemb0iC4DMlHRQF1UbzdmW4nPznMJv5oSW3VjaR9LdOU9/QtLNZvbmdsFCM1vPzL6j0IfJm136n+7++64rnJi7PyfpEDXegN9N0m1mdpiZ1d2QMrNVstmfN2vJYrpzlZ/OYhj9Nmfbb1qluTKzZczsiwopTsauH4a9HzuI/f2x69DPm9lPml0TZddvh0s6VY2Lvp7k7kW+W7+hxnQ3y0s6zcx+XOSGe1aXTc3sc2Z2s8IN4sKzDkrYT9KV2bX/x63g4unZ4KALFAaw1fpF1RUEhlk8LRsAau2Qle+Z2SyFfNO3KgRJHlEItK0oaQOF4N5uakwfM+a37l4m9+yyCgtQvUPSM2Z2vUIH7+7s3HMlLS1pFUmbKeQk3q7JsZ6R9D53f6Toyd39HDPbW2FRuRWip7dUGMXxtJldmtXrIYW/y9PZ6zdQmO6+i6TVi563YicodArHTFbIq/ptM7tXIXjxXLTPD929p6Me3f05M/tyVr9ab5I0y8z+oLCg0sMKHdj1Jb1B4T2uTZnxEYXRoROCu883s30U/ja1Cxu/QGGa7eFmdoXCCKV7FVJPTFVIN7KWpK0V8nQWutEW+W+F96c2z+dSCqOBPp69ZzMUUiitLmkbhdEzq9a8/vsKua836OD8HXP3RVkqi6tVH7haXdKvzewoSacr3Kh4QuFvu4NCICf+7Esh+Jk3Cn8iuEbSfDWf8fOUwu9EWRdJOqLF86PZiOih5+5uZu9TWDhu5Zqn1pF0dvZbeKakuxRudK0taVeFtDK1I8K/qMbRet3Ua5aZnavmN5crH8HXCXe/xkLqn3PVmGd/fYWR/z8ysysV0ijNUfj9XqDQf1lP4Td+Z1WUbs7dHzCz2xT6Ks2MdnDodsH867JRmxOeu99rIVf1+ZJeGD29iULwba6ZnaPwW/aAwnfdmgo3xl6jxj7I84eX9Dl3P6ZH1e85d7/RzD6iMPik9t+4lsJv93FmNkeh/72KwsyR+Kb8oQqznWrbedzHHBYnS/qs6mcnLa+Q9vJahYUw71C4/lhDoe/0JtUvHHuLwmyFVr9949qA9ve/qCXXRu+X9FYzO10hTdkchd/dzSQdkNUn9i+FG3ptufuzZvZWhd+a2s+FKawf814zu0bhJsRdCv32SVkdVleYufJyNc7O6aWxa//vmNntCgNbblL4nRxLzbOywkyG1yikH4z9U+XTBQETGoF5AEVtqM47Bj+X9L4uzv0ChaB7s8B7K09Keru7x4tntuXuf86mLP9C+QvJLi3pdVkp63dqndanCt9XGOUXL3JkCsGH9XL2qXLBwKbc/UQz210hbVCt5RTSEbynzSH+291PMbMJE5iXng+ObafQfuI2OVUhQLdrD877TDb9+gI1zoZZV+0Xor1E0qfUnwVg5e4zsmDdOWpMRbGZiufMP1XSOydKkDiWXWReonDhnOfKLBd9WZcpBHByZ19ogqXjcPeZFhYcP1+Ni6dtnZVWvqFwU7mywHzmOOUH5ucpzL4ZCO5+nZm9TCH9V156iSkKC6/Gi68W8RdJv+lgvwvVPDB/Vzbav5Tse22OmqfXmVCfm3Zqfj9PVBgdGltZ5dOuPaow8OP0buvXb+7+YzNzheB8PKveFG5SrNmwY7gxcbi7/8bMPho9N5QjwrM+0VsUUtrEaTu3VX6wstZ9Ct+lB1dfu8EygP39b0l6hZasg7ZiwXpIYdDLbmXWUnH3x7JR5aeoMS3iZC0JhA+iF2el6ezSHPdKerO7x+swAGiBVDYAap0i6ZdqXKymUzMl7evuB2dTZds5QqGT2zIPfAmnStq8k6D8mCyNxyslvVfSnV3W5zmFUXw7uPtbyyz01omsU/R69f4GQKcOUWPe4naelvRRd//PHtRnXMgCOLtI+i81pmwoa4YKBpnc/WGFWTFlP0+nStozW0i2b9z9GoXPcjytuIgFkr4k6S0F0nENu1bBvtFODujujys/B3SRcw6lLC3MbgqjKot6TtIX3L1XIzD/LOkfOdt/PmgX4e4+x933Upi5c0O3h1O4ubinu+/u7jM6OEbln5tMq3QKE+5z0467z3X3NyuMhu3kfRyzSNJPJG02DEH5Me7+E4Ug4d8L7jJT4XMxlm97lej5oZ2xkaW9fL0aFzht50pJO7r7rMorNbgGpr+fDax4p8KCxWVcJmlXd5/ZwTkfUxiYcrga10Ep619qXHOqCg9WcIyzFNp2mX4LABGYB1DD3S9z93cpjL56rUJO1gsUpvMWNVshuL+XpBeXCYq7+3fdfWeFUdvvVBi1c7OKB+qfUwhi/JfCxdIB7p6XU7QUD05UmLa3l8IMgKId8fkK080/JWldd9/L3a/utk5Fuft97r6HwlTIYySdJ2mWws2XZ1PVI4+7L3T39yv8TS9r8/J5Cp3oLd39uJ5XbsC5+yJ3/6JCWpgjJV2hYu/nMwp/6y9L2s7dt3D3U0qc92F331chqHGpQsCqmaskHZB9DgciaOfuM919d0l7KFxAxIs8x2YqrHnxQnc/2tssHj1BtAr2dZInu92+D3YYCB333P1ahbRQn1F+QHzMMwoLrm/n7oUWtOuwPq7GRSKlPi762o67n+7uWyvMJDpe4feviKcVvsO+JGkTd9/F3c/roiqjav592YvPzbPKX3gRktz9VIXZhHtJ+pVCipZ2FiukdfhPSRu7+wfd/aHe1bI/spvY2yvcGDxe4d88W6FNPaGQ0vIUSQdKepG7/7lm93jW5aM9r3AfufulCqk8v6H2g5quUZjF+ip3v7fXdRskg9bfd/dn3f1QhRnPF6j1deZ1Cmlndu4kKF9zTnf37yrMPj9MoS9VZKDHYoXf3W9IGpG0kbt/u9N6tKjfnpK2ULhePVshvVAR8xSui3d19316PegMGFY2QWdjAyjBzEwhv+2mCvn2VlTIvewK01SfUAhU3+ju3Y4EyDv/Ugp5HF+oMI12BYWpo09n539cIZfjLR4W+UrCzNaVtLlCcHRlhdQ2Tyvk2X5MIZByK8G8YsxsTYVFoaYr/D0XKlwMzlDIldvXGwmDzsyWU0j3tLZCHtOVFdrjEwp5M2+X9E93jxd46+ac6yhcwE9XGCm3QCHwdfV46Jyb2VSF0YHrKeTzXE4hQPOQpJvdvVUwFEjKzF6icOE8XWHxwHkKuVyvzGYd9Pr8Kyn81tcufD7q7q/p9bmrlC1kuaXCb/c0hX/PIi357Z4p6aYqvysx2LJ+7osU0jasp9DPfIFCOsRHFNrEDU7O/qbMbFM13kB8n7vH+cWHkplNVkiRsqXCAvNTFPpfd0m6xt2rGJE8FAatv58tRLujwrXm8grXlQ9kdel2tnSr8y6l0GbWUfgtWkVLboA9rPB5ur1fMzWzBWA3UbiZsLJCH/kZhb/PHIXc83dwnQt0j8A8AAAAgJay/NHHRpvf5u6d5FwHMETM7HCFGWa1XuLuN/ejPgAAjBcE5gEAAAA0lY0onqEwonjMg5LWZ2Q5MLFls89uVxhZO+ZhSWsymhYAgNbIMQ8AAACglQNVH5SXpOMJygNQGCm/YbTtZwTlAQBojxHzAAAAAHKZ2QqSblR94O1JSRsO4+KXwERkZnsr5NU/w92fK7jPVEnflXRo9NSzkjZ191mVVhIAgCHEiHkAAAAADcxsO0nnq3E07HcJygNDZUtJf5A0y8yONbM9s0U665jZJDN7qZkdIelONQblJekogvI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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "CM_yaAy2iLJL"
+ },
+ "source": [
+ "#prediction on test set, modified WCW environment\n",
+ "s_m_classified=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1])\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " s_m_classified[i,j]=np.round(classifier(torch.tensor([i,j]).to('cuda').float()).item(),3)\n",
+ "s_m_classified[2,1:]=0"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 890
+ },
+ "id": "pxhLyrz9iU_P",
+ "outputId": "49e6de2e-ac98-4f49-8ab9-4949621e2a3d"
+ },
+ "source": [
+ "plt.figure(figsize=[4,3],dpi=300)\n",
+ "plt.imshow(s_m_classified,cmap='gray')\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " f='bold' \n",
+ "\n",
+ " if m_env.is_cliff([i,j]):\n",
+ " text='T'\n",
+ " elif m_env.is_goal([i,j]):\n",
+ " text='T'\n",
+ " elif i==2 and j==0:\n",
+ " text='S'\n",
+ " else: \n",
+ " f='regular'\n",
+ " text=str(int(m_dev[i,j]))#+'/'+str(inv_display_reference[i,j])\n",
+ " if s_m_classified[i,j]>0.5:\n",
+ " color='black'\n",
+ " else:\n",
+ " color='w'\n",
+ " text = plt.text(j, i, text,\n",
+ " ha=\"center\", va=\"center\", color=color,fontweight=f)\n",
+ "plt.title('SSC tested on Modified Windy CW')\n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "e0yiCz8KAUmO"
+ },
+ "source": [
+ "**Testing SDSC, ie combining state and return distribution as input to SC**"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "J0S26Y3cC7w5"
+ },
+ "source": [
+ "#training on full env with sdsc\n",
+ "bce=nn.BCELoss()\n",
+ "classifier=SafetyClassifier(agent.support,state_dim=2).to('cuda')\n",
+ "optimiser=torch.optim.Adam(classifier.parameters(),lr=0.001)\n",
+ "#sample from env\n",
+ "#get labels\n",
+ "classifier.train()\n",
+ "for i in range(500):\n",
+ " optimiser.zero_grad()\n",
+ " x=torch.randint(0,3,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " y=torch.randint(0,9,[batch_size]).unsqueeze(-1).to('cuda')\n",
+ " pos=torch.cat([x,y],dim=-1).float()\n",
+ " labels=torch.tensor(dev)[x,y].squeeze().to('cuda').float()\n",
+ " dist=agent.dqn.dist(pos)\n",
+ " pred=classifier(dist,state=pos)\n",
+ " #target=classifier.compute_target(dist,labels)\n",
+ " loss=bce(pred.squeeze(),labels)\n",
+ " loss.backward()\n",
+ " optimiser.step()"
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "dswW-wiWE_7a"
+ },
+ "source": [
+ "#training results\n",
+ "sd_classified=np.ndarray([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "for i in range(4):\n",
+ " for j in range(9):\n",
+ " dist=agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " sd_classified[i,j]=np.round(classifier(dist,torch.tensor([i,j]).to('cuda').float().reshape(1,2)).item(),3)\n",
+ "sd_classified[3,1:]=0 "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 727
+ },
+ "id": "facZ9jsmFOE_",
+ "outputId": "cb9b572b-6ec4-4e6c-e32b-c5cbdd8a461c"
+ },
+ "source": [
+ "ax=plt.figure(figsize=[6,3],dpi=300)\n",
+ "ax = plt.gca()\n",
+ "title=r\"SDSC trained on Windy CW - Original labels\"\n",
+ "plt.title(title)\n",
+ "grid=np.zeros([env.pos_max_x+1,env.pos_max_y+1])\n",
+ "grid[3,1:6]=1\n",
+ "#grid[3,1:11]=1\n",
+ "#grid[3,env.reward_region+1:]=-1\n",
+ "plt.imshow(sd_classified,cmap='gray')\n",
+ "for i in range(grid.shape[0]):\n",
+ " for j in range(grid.shape[1]):\n",
+ " #highlight_cell(j,i, color=\"black\", linewidth=1)\n",
+ " c = 'black' if sd_classified[i,j]>0.5 else 'white'\n",
+ " if i==3 and j==0:\n",
+ " text = plt.text(j, i, 'S',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold')\n",
+ " elif i==3 and j>0:\n",
+ " text = plt.text(j, i, 'T',\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='bold') \n",
+ " else:\n",
+ " text = plt.text(j, i, int(np.round(dev[i,j].item(),1)),\n",
+ " ha=\"center\", va=\"center\", color=c,fontweight='regular')\n",
+ "ax.set_xticks([0,1,2,3,4,5,6,7,8])\n",
+ "ax.set_yticks([0,1,2,3]) \n",
+ "#plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1800x900 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "wM4lbK3aGKMH"
+ },
+ "source": [
+ "#prediction on test set of Modified WCW\n",
+ "sd_m_classified=np.ndarray([m_env.pos_max_x+1,m_env.pos_max_y+1])\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " dist=m_agent.dqn.dist(torch.tensor([i,j]).to('cuda').float())\n",
+ " sd_m_classified[i,j]=np.round(classifier(dist,torch.tensor([i,j]).to('cuda').float().reshape(1,2)).item(),3)\n",
+ "sd_m_classified[2,1:]=0 "
+ ],
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 890
+ },
+ "id": "-MA5zRxuGqjy",
+ "outputId": "f84d66df-aef2-4b87-b678-31eef4501f5a"
+ },
+ "source": [
+ "plt.figure(figsize=[4,3],dpi=300)\n",
+ "plt.imshow(sd_m_classified,cmap='gray')\n",
+ "\n",
+ "for i in range(5):\n",
+ " for j in range(5):\n",
+ " f='bold' \n",
+ "\n",
+ " if m_env.is_cliff([i,j]):\n",
+ " text='T'\n",
+ " elif m_env.is_goal([i,j]):\n",
+ " text='T'\n",
+ " elif i==2 and j==0:\n",
+ " text='S'\n",
+ " else: \n",
+ " f='regular'\n",
+ " text=str(int(m_dev[i,j]))#+'/'+str(inv_display_reference[i,j])\n",
+ " if sd_m_classified[i,j]>0.5:\n",
+ " color='black'\n",
+ " else:\n",
+ " color='w'\n",
+ " text = plt.text(j, i, text,\n",
+ " ha=\"center\", va=\"center\", color=color,fontweight=f)\n",
+ "plt.title('SDSC tested on Modified Windy CW')\n",
+ "plt.colorbar()\n",
+ "plt.show()"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "display_data",
+ "data": {
+ "image/png": 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\n",
+ "text/plain": [
+ "<Figure size 1200x900 with 2 Axes>"
+ ]
+ },
+ "metadata": {
+ "tags": [],
+ "needs_background": "light"
+ }
+ }
+ ]
+ }
+ ]
+}
\ No newline at end of file
--
GitLab