VanillaPolicyGradient.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 548
    },
    "id": "lIYdn1woOS1n",
    "outputId": "4ac3019d-8647-4407-c64a-9c255256c16b"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ale_py/roms/utils.py:90: DeprecationWarning: SelectableGroups dict interface is deprecated. Use select.\n",
      "  for external in metadata.entry_points().get(self.group, []):\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torch.nn.functional as F\n",
    "\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "import argparse\n",
    "from distutils.util import strtobool\n",
    "import collections\n",
    "import numpy as np\n",
    "import gym\n",
    "from gym.wrappers import TimeLimit, Monitor\n",
    "from gym.spaces import Discrete, Box, MultiBinary, MultiDiscrete, Space\n",
    "import time\n",
    "import random\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import argparse\n",
    "import datetime\n",
    "from torch.distributions import Categorical, Normal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Buffer(object):\n",
    "    \"\"\"\n",
    "    A buffer for storing trajectories experienced by a agent interacting\n",
    "    with the environment.\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(self, obs_dim, act_dim, size, device, gamma=0.99, lam=0.97):\n",
    "        self.obs_buf = np.zeros([size, obs_dim], dtype=np.float32)\n",
    "        self.act_buf = np.zeros([size, act_dim], dtype=np.float32)\n",
    "        self.rew_buf = np.zeros(size, dtype=np.float32)\n",
    "        self.don_buf = np.zeros(size, dtype=np.float32)\n",
    "        self.ret_buf = np.zeros(size, dtype=np.float32)\n",
    "        self.adv_buf = np.zeros(size, dtype=np.float32)\n",
    "        self.v_buf = np.zeros(size, dtype=np.float32)\n",
    "        self.gamma, self.lam = gamma, lam\n",
    "        self.ptr, self.max_size = 0, size\n",
    "        self.device = device\n",
    "\n",
    "    def add(self, obs, act, rew, don, v):\n",
    "        assert self.ptr < self.max_size      # Buffer has to have room so you can store\n",
    "        self.obs_buf[self.ptr] = obs\n",
    "        self.act_buf[self.ptr] = act\n",
    "        self.rew_buf[self.ptr] = rew\n",
    "        self.don_buf[self.ptr] = don\n",
    "        self.v_buf[self.ptr] = v\n",
    "        self.ptr += 1\n",
    "\n",
    "    def finish_path(self):\n",
    "        previous_v = 0\n",
    "        running_ret = 0\n",
    "        running_adv = 0\n",
    "        for t in reversed(range(len(self.rew_buf))):\n",
    "            # The next two line computes rewards-to-go, to be targets for the value function\n",
    "            running_ret = self.rew_buf[t] + self.gamma*(1-self.don_buf[t])*running_ret\n",
    "            self.ret_buf[t] = running_ret\n",
    "\n",
    "            # The next four lines implement GAE-Lambda advantage calculation\n",
    "            running_del = self.rew_buf[t] + self.gamma*(1-self.don_buf[t])*previous_v - self.v_buf[t]\n",
    "            running_adv = running_del + self.gamma*self.lam*(1-self.don_buf[t])*running_adv\n",
    "            previous_v = self.v_buf[t]\n",
    "            self.adv_buf[t] = running_adv\n",
    "        # The next line implement the advantage normalization trick\n",
    "        self.adv_buf = (self.adv_buf - self.adv_buf.mean()) / self.adv_buf.std()\n",
    "        \n",
    "    def get(self):\n",
    "        assert self.ptr == self.max_size     # Buffer has to be full before you can get\n",
    "        self.ptr = 0\n",
    "        return dict(obs=torch.Tensor(self.obs_buf).to(self.device),\n",
    "                    act=torch.Tensor(self.act_buf).to(self.device),\n",
    "                    ret=torch.Tensor(self.ret_buf).to(self.device),\n",
    "                    adv=torch.Tensor(self.adv_buf).to(self.device),\n",
    "                    v=torch.Tensor(self.v_buf).to(self.device))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "MLLwE9TQ5NUU"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/gswatiseas/gym/gym/envs/registration.py:579: UserWarning: \u001b[33mWARN: The environment Humanoid-v2 is out of date. You should consider upgrading to version v3 with the environment ID `Humanoid-v3`.\u001b[0m\n",
      "  f\"The environment {spec.id} is out of date. You should consider \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "376 17 0.4\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "env = gym.make('Humanoid-v2')\n",
    "seed = 1234\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "torch.manual_seed(seed)\n",
    "env.seed(seed)\n",
    "\n",
    "obs_dim = env.observation_space.shape[0]\n",
    "act_dim = env.action_space.shape[0]\n",
    "act_limit = env.action_space.high[0]\n",
    "\n",
    "print (obs_dim, act_dim, act_limit)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Hyper params\n",
    "n_iters = 30\n",
    "steps_per_iter = 4000\n",
    "phase = 'train'\n",
    "max_step = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "k6w-tmg2421Q"
   },
   "outputs": [],
   "source": [
    "def identity(x):\n",
    "    \"\"\"Return input without any change.\"\"\"\n",
    "    return x\n",
    "\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self, \n",
    "                 input_size, \n",
    "                 output_size, \n",
    "                 output_limit=1.0,\n",
    "                 hidden_sizes=(64,64), \n",
    "                 activation=F.relu, \n",
    "                 output_activation=identity,\n",
    "                 use_output_layer=True,\n",
    "                 use_actor=False,\n",
    "    ):\n",
    "        super(MLP, self).__init__()\n",
    "\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self.output_limit = output_limit\n",
    "        self.hidden_sizes = hidden_sizes\n",
    "        self.activation = activation\n",
    "        self.output_activation = output_activation\n",
    "        self.use_output_layer = use_output_layer\n",
    "        self.use_actor = use_actor\n",
    "\n",
    "        # Set hidden layers\n",
    "        self.hidden_layers = nn.ModuleList()\n",
    "        in_size = self.input_size\n",
    "        for next_size in self.hidden_sizes:\n",
    "            fc = nn.Linear(in_size, next_size)\n",
    "            in_size = next_size\n",
    "            self.hidden_layers.append(fc)\n",
    "\n",
    "        # Set output layers\n",
    "        if self.use_output_layer:\n",
    "            self.output_layer = nn.Linear(in_size, self.output_size)\n",
    "        else:\n",
    "            self.output_layer = identity\n",
    "\n",
    "    def forward(self, x):\n",
    "        for hidden_layer in self.hidden_layers:\n",
    "            x = self.activation(hidden_layer(x))\n",
    "        x = self.output_activation(self.output_layer(x))\n",
    "        # If the network is used as actor network, make sure output is in correct range\n",
    "        x = x * self.output_limit if self.use_actor else x   \n",
    "        return x\n",
    "    \n",
    "class GaussianPolicy(MLP):\n",
    "    def __init__(self, \n",
    "                 input_size, \n",
    "                 output_size, \n",
    "                 output_limit=1.0,\n",
    "                 hidden_sizes=(64,64),\n",
    "                 activation=torch.tanh,\n",
    "    ):\n",
    "        super(GaussianPolicy, self).__init__(\n",
    "            input_size=input_size,\n",
    "            output_size=output_size,\n",
    "            hidden_sizes=hidden_sizes,\n",
    "            activation=activation,\n",
    "        )\n",
    "\n",
    "        self.output_limit = output_limit\n",
    "        self.log_std = np.ones(output_size, dtype=np.float32)\n",
    "        self.log_std = torch.nn.Parameter(torch.Tensor(self.log_std))\n",
    "\n",
    "    def forward(self, x, pi=None, use_pi=True):\n",
    "        mu = super(GaussianPolicy, self).forward(x)\n",
    "        std = torch.exp(self.log_std)\n",
    "\n",
    "        dist = Normal(mu, std)\n",
    "        if use_pi:\n",
    "            pi = dist.sample()\n",
    "        log_pi = dist.log_prob(pi).sum(dim=-1)\n",
    "\n",
    "        # Make sure outputs are in correct range\n",
    "        mu = mu * self.output_limit\n",
    "        pi = pi * self.output_limit\n",
    "        return mu, std, pi, log_pi\n",
    "    \n",
    "def hard_target_update(main, target):\n",
    "    target.load_state_dict(main.state_dict())\n",
    "\n",
    "def soft_target_update(main, target, tau=0.005):\n",
    "    for main_param, target_param in zip(main.parameters(), target.parameters()):\n",
    "        target_param.data.copy_(tau*main_param.data + (1.0-tau)*target_param.data)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Agent(object):\n",
    "   \"\"\"\n",
    "   An implementation of the Vanilla Policy Gradient (VPG) agent\n",
    "   with GAE-Lambda for advantage estimation.\n",
    "   \"\"\"\n",
    "\n",
    "   def __init__(self,\n",
    "                env,\n",
    "                device,\n",
    "                obs_dim,\n",
    "                act_dim,\n",
    "                act_limit,\n",
    "                steps=0,\n",
    "                gamma=0.99,\n",
    "                lam=0.97,\n",
    "                hidden_sizes=(64,64),\n",
    "                sample_size=2048,\n",
    "                policy_lr=1e-3,\n",
    "                vf_lr=1e-3,\n",
    "                train_vf_iters=80,\n",
    "                eval_mode=False,\n",
    "                policy_losses=list(),\n",
    "                vf_losses=list(),\n",
    "                kls=list(),\n",
    "                logger=dict(),\n",
    "   ):\n",
    "\n",
    "      self.env = env\n",
    "      self.device = device\n",
    "      self.obs_dim = obs_dim\n",
    "      self.act_dim = act_dim\n",
    "      self.act_limit = act_limit\n",
    "      self.steps = steps \n",
    "      self.gamma = gamma\n",
    "      self.lam = lam\n",
    "      self.hidden_sizes = hidden_sizes\n",
    "      self.sample_size = sample_size\n",
    "      self.policy_lr = policy_lr\n",
    "      self.vf_lr = vf_lr\n",
    "      self.train_vf_iters = train_vf_iters\n",
    "      self.eval_mode = eval_mode\n",
    "      self.policy_losses = policy_losses\n",
    "      self.vf_losses = vf_losses\n",
    "      self.kls = kls\n",
    "      self.logger = logger\n",
    "\n",
    "      # Main network\n",
    "      self.policy = GaussianPolicy(self.obs_dim, self.act_dim, self.act_limit).to(self.device)\n",
    "      self.vf = MLP(self.obs_dim, 1, activation=torch.tanh).to(self.device)\n",
    "      \n",
    "      # Create optimizers\n",
    "      self.policy_optimizer = optim.Adam(self.policy.parameters(), lr=self.policy_lr)\n",
    "      self.vf_optimizer = optim.Adam(self.vf.parameters(), lr=self.vf_lr)\n",
    "      \n",
    "      # Experience buffer\n",
    "      self.buffer = Buffer(self.obs_dim, self.act_dim, self.sample_size, self.device, self.gamma, self.lam)\n",
    "\n",
    "   def train_model(self):\n",
    "      batch = self.buffer.get()\n",
    "      obs = batch['obs']\n",
    "      act = batch['act'].detach()\n",
    "      ret = batch['ret']\n",
    "      adv = batch['adv']\n",
    "      \n",
    "      if 0: # Check shape of experiences\n",
    "         print(\"obs\", obs.shape)\n",
    "         print(\"act\", act.shape)\n",
    "         print(\"ret\", ret.shape)\n",
    "         print(\"adv\", adv.shape)\n",
    "\n",
    "      # Update value network parameter\n",
    "      for _ in range(self.train_vf_iters):\n",
    "         # Prediction V(s)\n",
    "         v = self.vf(obs).squeeze(1)\n",
    "         \n",
    "         # Value loss\n",
    "         vf_loss = F.mse_loss(v, ret)\n",
    "\n",
    "         self.vf_optimizer.zero_grad()\n",
    "         vf_loss.backward()\n",
    "         self.vf_optimizer.step()\n",
    "      \n",
    "      # Prediction logπ(s)\n",
    "      _, _, _, log_pi_old = self.policy(obs, act, use_pi=False)\n",
    "      log_pi_old = log_pi_old.detach()\n",
    "      _, _, _, log_pi = self.policy(obs, act, use_pi=False)\n",
    "      \n",
    "      # Policy loss\n",
    "      policy_loss = -(log_pi*adv).mean()\n",
    "\n",
    "      # Update policy network parameter\n",
    "      self.policy_optimizer.zero_grad()\n",
    "      policy_loss.backward()\n",
    "      self.policy_optimizer.step()\n",
    "      \n",
    "      # A sample estimate for KL-divergence, easy to compute\n",
    "      approx_kl = (log_pi_old - log_pi).mean()     \n",
    "\n",
    "      # Save losses\n",
    "      self.policy_losses.append(policy_loss.item())\n",
    "      self.vf_losses.append(vf_loss.item())\n",
    "      self.kls.append(approx_kl.item())\n",
    "\n",
    "   def run(self, max_step):\n",
    "      step_number = 0\n",
    "      total_reward = 0.\n",
    "\n",
    "      obs = self.env.reset()\n",
    "      done = False\n",
    "\n",
    "      # Keep interacting until agent reaches a terminal state.\n",
    "      while not (done or step_number == max_step):\n",
    "         #env.render()    \n",
    "         \n",
    "         if self.eval_mode:\n",
    "            action, _, _, _ = self.policy(torch.Tensor(obs).to(self.device))\n",
    "            action = action.detach().cpu().numpy()\n",
    "            next_obs, reward, done, _ = self.env.step(action)\n",
    "         else:\n",
    "            self.steps += 1\n",
    "            \n",
    "            # Collect experience (s, a, r, s') using some policy\n",
    "            _, _, action, log_pi = self.policy(torch.Tensor(obs).to(self.device))\n",
    "            action = action.detach().cpu().numpy()\n",
    "            next_obs, reward, done, _ = self.env.step(action)\n",
    "\n",
    "            # Add experience to buffer\n",
    "            v = self.vf(torch.Tensor(obs).to(self.device))\n",
    "            self.buffer.add(obs, action, reward, done, v)\n",
    "            \n",
    "            # Start training when the number of experience is equal to sample size\n",
    "            if self.steps == self.sample_size:\n",
    "               self.buffer.finish_path()\n",
    "               self.train_model()\n",
    "               self.steps = 0\n",
    "\n",
    "         total_reward += reward\n",
    "         step_number += 1\n",
    "         obs = next_obs\n",
    "      \n",
    "      # Save logs\n",
    "      self.logger['LossPi'] = round(np.mean(self.policy_losses), 5)\n",
    "      self.logger['LossV'] = round(np.mean(self.vf_losses), 5)\n",
    "      self.logger['KL'] = round(np.mean(self.kls), 5)\n",
    "      return step_number, total_reward"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "x4KRqZ5I46Ti"
   },
   "outputs": [],
   "source": [
    "agent = Agent(env, device, obs_dim, act_dim, act_limit, sample_size=4096)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Lod0C2yG4dYa",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3373: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/opt/conda/lib/python3.7/site-packages/numpy/core/_methods.py:170: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n",
      "Iterations: 1\n",
      "Steps: 4031\n",
      "Episodes: 177\n",
      "EpisodeReturn: 164.14\n",
      "AverageReturn: 110.86\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 133.37\n",
      "EvalAverageReturn: 113.97\n",
      "OtherLogs: {'LossPi': 0.00205, 'LossV': 3782.10718, 'KL': 0.0}\n",
      "Time: 7\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 133.37\n",
      "EvalAverageReturn: 113.97\n",
      "Time: 7\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 2\n",
      "Steps: 8050\n",
      "Episodes: 359\n",
      "EpisodeReturn: 213.73\n",
      "AverageReturn: 109.23\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 125.9\n",
      "EvalAverageReturn: 99.89\n",
      "OtherLogs: {'LossPi': -0.0011, 'LossV': 3420.04797, 'KL': 0.0}\n",
      "Time: 15\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 125.9\n",
      "EvalAverageReturn: 99.89\n",
      "Time: 15\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 3\n",
      "Steps: 12076\n",
      "Episodes: 525\n",
      "EpisodeReturn: 150.35\n",
      "AverageReturn: 112.23\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 144.92\n",
      "EvalAverageReturn: 94.94\n",
      "OtherLogs: {'LossPi': -0.00476, 'LossV': 3376.22306, 'KL': 0.0}\n",
      "Time: 22\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 144.92\n",
      "EvalAverageReturn: 94.94\n",
      "Time: 22\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 4\n",
      "Steps: 16103\n",
      "Episodes: 697\n",
      "EpisodeReturn: 152.65\n",
      "AverageReturn: 112.74\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 87.38\n",
      "EvalAverageReturn: 107.35\n",
      "OtherLogs: {'LossPi': -0.00408, 'LossV': 3192.27704, 'KL': 0.0}\n",
      "Time: 30\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 87.38\n",
      "EvalAverageReturn: 107.35\n",
      "Time: 30\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 5\n",
      "Steps: 20111\n",
      "Episodes: 869\n",
      "EpisodeReturn: 165.58\n",
      "AverageReturn: 112.99\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 98.15\n",
      "EvalAverageReturn: 99.89\n",
      "OtherLogs: {'LossPi': -0.00548, 'LossV': 3044.2981, 'KL': 0.0}\n",
      "Time: 37\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 98.15\n",
      "EvalAverageReturn: 99.89\n",
      "Time: 37\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 6\n",
      "Steps: 24140\n",
      "Episodes: 1039\n",
      "EpisodeReturn: 158.22\n",
      "AverageReturn: 113.45\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 209.57\n",
      "EvalAverageReturn: 105.83\n",
      "OtherLogs: {'LossPi': -0.0076, 'LossV': 2919.66882, 'KL': 0.0}\n",
      "Time: 45\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 209.57\n",
      "EvalAverageReturn: 105.83\n",
      "Time: 45\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 7\n",
      "Steps: 28159\n",
      "Episodes: 1213\n",
      "EpisodeReturn: 151.87\n",
      "AverageReturn: 113.34\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 170.9\n",
      "EvalAverageReturn: 106.45\n",
      "OtherLogs: {'LossPi': -0.00743, 'LossV': 2784.99566, 'KL': 0.0}\n",
      "Time: 52\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 170.9\n",
      "EvalAverageReturn: 106.45\n",
      "Time: 52\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 8\n",
      "Steps: 32161\n",
      "Episodes: 1385\n",
      "EpisodeReturn: 81.16\n",
      "AverageReturn: 113.41\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 113.52\n",
      "EvalAverageReturn: 110.3\n",
      "OtherLogs: {'LossPi': -0.00651, 'LossV': 2634.31993, 'KL': 0.0}\n",
      "Time: 60\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 113.52\n",
      "EvalAverageReturn: 110.3\n",
      "Time: 60\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 9\n",
      "Steps: 36179\n",
      "Episodes: 1554\n",
      "EpisodeReturn: 129.42\n",
      "AverageReturn: 113.66\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 108.38\n",
      "EvalAverageReturn: 144.04\n",
      "OtherLogs: {'LossPi': -0.00735, 'LossV': 2517.12941, 'KL': 0.0}\n",
      "Time: 68\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 108.38\n",
      "EvalAverageReturn: 144.04\n",
      "Time: 68\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 10\n",
      "Steps: 40184\n",
      "Episodes: 1716\n",
      "EpisodeReturn: 104.47\n",
      "AverageReturn: 114.32\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 99.56\n",
      "EvalAverageReturn: 107.82\n",
      "OtherLogs: {'LossPi': -0.00742, 'LossV': 2425.15951, 'KL': 0.0}\n",
      "Time: 75\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 99.56\n",
      "EvalAverageReturn: 107.82\n",
      "Time: 75\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 11\n",
      "Steps: 44213\n",
      "Episodes: 1893\n",
      "EpisodeReturn: 162.36\n",
      "AverageReturn: 113.99\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 127.05\n",
      "EvalAverageReturn: 128.98\n",
      "OtherLogs: {'LossPi': -0.00681, 'LossV': 2295.27183, 'KL': 0.0}\n",
      "Time: 83\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 127.05\n",
      "EvalAverageReturn: 128.98\n",
      "Time: 83\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 12\n",
      "Steps: 48235\n",
      "Episodes: 2068\n",
      "EpisodeReturn: 190.05\n",
      "AverageReturn: 113.83\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 97.31\n",
      "EvalAverageReturn: 155.94\n",
      "OtherLogs: {'LossPi': -0.00574, 'LossV': 2183.20775, 'KL': 0.0}\n",
      "Time: 90\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 97.31\n",
      "EvalAverageReturn: 155.94\n",
      "Time: 90\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 13\n",
      "Steps: 52239\n",
      "Episodes: 2235\n",
      "EpisodeReturn: 89.25\n",
      "AverageReturn: 114.09\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 147.47\n",
      "EvalAverageReturn: 123.23\n",
      "OtherLogs: {'LossPi': -0.00673, 'LossV': 2139.31901, 'KL': 0.0}\n",
      "Time: 98\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 147.47\n",
      "EvalAverageReturn: 123.23\n",
      "Time: 98\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 14\n",
      "Steps: 56257\n",
      "Episodes: 2397\n",
      "EpisodeReturn: 187.7\n",
      "AverageReturn: 114.54\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 91.92\n",
      "EvalAverageReturn: 104.92\n",
      "OtherLogs: {'LossPi': -0.007, 'LossV': 2055.0099, 'KL': 0.0}\n",
      "Time: 106\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 91.92\n",
      "EvalAverageReturn: 104.92\n",
      "Time: 106\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 15\n",
      "Steps: 60283\n",
      "Episodes: 2563\n",
      "EpisodeReturn: 149.51\n",
      "AverageReturn: 114.86\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 107.55\n",
      "EvalAverageReturn: 113.82\n",
      "OtherLogs: {'LossPi': -0.00763, 'LossV': 1978.2995, 'KL': 0.0}\n",
      "Time: 113\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 107.55\n",
      "EvalAverageReturn: 113.82\n",
      "Time: 113\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 16\n",
      "Steps: 64299\n",
      "Episodes: 2725\n",
      "EpisodeReturn: 131.75\n",
      "AverageReturn: 115.23\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 171.63\n",
      "EvalAverageReturn: 108.02\n",
      "OtherLogs: {'LossPi': -0.00752, 'LossV': 1914.90808, 'KL': 0.0}\n",
      "Time: 121\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 171.63\n",
      "EvalAverageReturn: 108.02\n",
      "Time: 121\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 17\n",
      "Steps: 68300\n",
      "Episodes: 2892\n",
      "EpisodeReturn: 121.64\n",
      "AverageReturn: 115.32\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 93.22\n",
      "EvalAverageReturn: 124.21\n",
      "OtherLogs: {'LossPi': -0.00789, 'LossV': 1846.43698, 'KL': 0.0}\n",
      "Time: 128\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 93.22\n",
      "EvalAverageReturn: 124.21\n",
      "Time: 128\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 18\n",
      "Steps: 72309\n",
      "Episodes: 3059\n",
      "EpisodeReturn: 92.3\n",
      "AverageReturn: 115.41\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 137.0\n",
      "EvalAverageReturn: 123.76\n",
      "OtherLogs: {'LossPi': -0.00749, 'LossV': 1779.24295, 'KL': 0.0}\n",
      "Time: 135\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 137.0\n",
      "EvalAverageReturn: 123.76\n",
      "Time: 135\n",
      "---------------------------------------\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------------\n",
      "Iterations: 19\n",
      "Steps: 76329\n",
      "Episodes: 3218\n",
      "EpisodeReturn: 125.98\n",
      "AverageReturn: 115.84\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 265.82\n",
      "EvalAverageReturn: 130.95\n",
      "OtherLogs: {'LossPi': -0.0076, 'LossV': 1720.15748, 'KL': 0.0}\n",
      "Time: 143\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 265.82\n",
      "EvalAverageReturn: 130.95\n",
      "Time: 143\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 20\n",
      "Steps: 80346\n",
      "Episodes: 3386\n",
      "EpisodeReturn: 101.83\n",
      "AverageReturn: 115.88\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 118.95\n",
      "EvalAverageReturn: 110.07\n",
      "OtherLogs: {'LossPi': -0.00804, 'LossV': 1665.26853, 'KL': 0.0}\n",
      "Time: 150\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 118.95\n",
      "EvalAverageReturn: 110.07\n",
      "Time: 150\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 21\n",
      "Steps: 84358\n",
      "Episodes: 3537\n",
      "EpisodeReturn: 130.73\n",
      "AverageReturn: 116.5\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 87.72\n",
      "EvalAverageReturn: 115.77\n",
      "OtherLogs: {'LossPi': -0.00878, 'LossV': 1614.90319, 'KL': 0.0}\n",
      "Time: 158\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 87.72\n",
      "EvalAverageReturn: 115.77\n",
      "Time: 158\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 22\n",
      "Steps: 88383\n",
      "Episodes: 3696\n",
      "EpisodeReturn: 154.03\n",
      "AverageReturn: 116.82\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 98.84\n",
      "EvalAverageReturn: 124.5\n",
      "OtherLogs: {'LossPi': -0.00898, 'LossV': 1576.82583, 'KL': 0.0}\n",
      "Time: 166\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 98.84\n",
      "EvalAverageReturn: 124.5\n",
      "Time: 166\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 23\n",
      "Steps: 92384\n",
      "Episodes: 3849\n",
      "EpisodeReturn: 82.65\n",
      "AverageReturn: 117.27\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 138.42\n",
      "EvalAverageReturn: 138.11\n",
      "OtherLogs: {'LossPi': -0.00893, 'LossV': 1538.43461, 'KL': 0.0}\n",
      "Time: 173\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 138.42\n",
      "EvalAverageReturn: 138.11\n",
      "Time: 173\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 24\n",
      "Steps: 96388\n",
      "Episodes: 4002\n",
      "EpisodeReturn: 142.6\n",
      "AverageReturn: 117.68\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 205.8\n",
      "EvalAverageReturn: 156.93\n",
      "OtherLogs: {'LossPi': -0.00915, 'LossV': 1499.54274, 'KL': 0.0}\n",
      "Time: 181\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 205.8\n",
      "EvalAverageReturn: 156.93\n",
      "Time: 181\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 25\n",
      "Steps: 100423\n",
      "Episodes: 4160\n",
      "EpisodeReturn: 169.61\n",
      "AverageReturn: 117.97\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 268.81\n",
      "EvalAverageReturn: 120.36\n",
      "OtherLogs: {'LossPi': -0.01009, 'LossV': 1430.25508, 'KL': 0.0}\n",
      "Time: 188\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 268.81\n",
      "EvalAverageReturn: 120.36\n",
      "Time: 188\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 26\n",
      "Steps: 104433\n",
      "Episodes: 4305\n",
      "EpisodeReturn: 118.96\n",
      "AverageReturn: 118.57\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 97.93\n",
      "EvalAverageReturn: 116.03\n",
      "OtherLogs: {'LossPi': -0.01031, 'LossV': 1408.79565, 'KL': 0.0}\n",
      "Time: 196\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 97.93\n",
      "EvalAverageReturn: 116.03\n",
      "Time: 196\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 27\n",
      "Steps: 108436\n",
      "Episodes: 4452\n",
      "EpisodeReturn: 104.99\n",
      "AverageReturn: 119.09\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 137.27\n",
      "EvalAverageReturn: 115.86\n",
      "OtherLogs: {'LossPi': -0.01017, 'LossV': 1382.55479, 'KL': 0.0}\n",
      "Time: 203\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 137.27\n",
      "EvalAverageReturn: 115.86\n",
      "Time: 203\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 28\n",
      "Steps: 112452\n",
      "Episodes: 4601\n",
      "EpisodeReturn: 86.3\n",
      "AverageReturn: 119.53\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 117.28\n",
      "EvalAverageReturn: 151.03\n",
      "OtherLogs: {'LossPi': -0.01085, 'LossV': 1350.26324, 'KL': 0.0}\n",
      "Time: 211\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 117.28\n",
      "EvalAverageReturn: 151.03\n",
      "Time: 211\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 29\n",
      "Steps: 116463\n",
      "Episodes: 4743\n",
      "EpisodeReturn: 202.25\n",
      "AverageReturn: 120.11\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 151.64\n",
      "EvalAverageReturn: 119.56\n",
      "OtherLogs: {'LossPi': -0.01108, 'LossV': 1330.65011, 'KL': 0.0}\n",
      "Time: 218\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 151.64\n",
      "EvalAverageReturn: 119.56\n",
      "Time: 218\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "Iterations: 30\n",
      "Steps: 120471\n",
      "Episodes: 4887\n",
      "EpisodeReturn: 98.46\n",
      "AverageReturn: 120.59\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 81.11\n",
      "EvalAverageReturn: 146.37\n",
      "OtherLogs: {'LossPi': -0.0115, 'LossV': 1306.31141, 'KL': 0.0}\n",
      "Time: 226\n",
      "---------------------------------------\n",
      "---------------------------------------\n",
      "EvalEpisodes: 10\n",
      "EvalEpisodeReturn: 81.11\n",
      "EvalAverageReturn: 146.37\n",
      "Time: 226\n",
      "---------------------------------------\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "\n",
    "total_num_steps = 0\n",
    "train_sum_returns = 0.\n",
    "train_num_episodes = 0\n",
    "eval_mode = 0\n",
    "\n",
    "train_avg_ret = []\n",
    "train_ep_ret = []\n",
    "eval_avg_ret = []\n",
    "eval_ep_ret = []\n",
    "\n",
    "# dir_name = 'runs/' + '/' \\\n",
    "#                            + 'ddpg' \\\n",
    "#                            + '_s_' + str(seed) \\\n",
    "#                            + '_t_' + datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n",
    "# writer = SummaryWriter(log_dir=dir_name)\n",
    "\n",
    "for i in range(n_iters):\n",
    "        # Perform the training phase, during which the agent learns\n",
    "        if phase == 'train':\n",
    "            train_step_count = 0\n",
    "\n",
    "            while train_step_count <= steps_per_iter:\n",
    "                eval_mode = False\n",
    "                \n",
    "                # Run one episode\n",
    "                train_step_length, train_episode_return = agent.run(max_step)\n",
    "                \n",
    "                total_num_steps += train_step_length\n",
    "                train_step_count += train_step_length\n",
    "                train_sum_returns += train_episode_return\n",
    "                train_num_episodes += 1\n",
    "\n",
    "                train_average_return = train_sum_returns / train_num_episodes if train_num_episodes > 0 else 0.0\n",
    "                \n",
    "                train_avg_ret.append(train_average_return)\n",
    "                train_ep_ret.append(train_episode_return)\n",
    "                \n",
    "#                 writer.add_scalar('Train/AverageReturns', train_average_return, total_num_steps)\n",
    "#                 writer.add_scalar('Train/EpisodeReturns', train_episode_return, total_num_steps)\n",
    "\n",
    "\n",
    "        # Perform the evaluation phase -- no learning\n",
    "        eval_sum_returns = 0.\n",
    "        eval_num_episodes = 0\n",
    "        eval_mode = True\n",
    "\n",
    "        for _ in range(10):\n",
    "            # Run one episode\n",
    "            eval_step_length, eval_episode_return = agent.run(max_step)\n",
    "\n",
    "            eval_sum_returns += eval_episode_return\n",
    "            eval_num_episodes += 1\n",
    "\n",
    "        eval_average_return = eval_sum_returns / eval_num_episodes if eval_num_episodes > 0 else 0.0\n",
    "        eval_avg_ret.append(eval_average_return)\n",
    "        eval_ep_ret.append(eval_episode_return)\n",
    "\n",
    "\n",
    "        if phase == 'train':\n",
    "            print('---------------------------------------')\n",
    "            print('Iterations:', i + 1)\n",
    "            print('Steps:', total_num_steps)\n",
    "            print('Episodes:', train_num_episodes)\n",
    "            print('EpisodeReturn:', round(train_episode_return, 2))\n",
    "            print('AverageReturn:', round(train_average_return, 2))\n",
    "            print('EvalEpisodes:', eval_num_episodes)\n",
    "            print('EvalEpisodeReturn:', round(eval_episode_return, 2))\n",
    "            print('EvalAverageReturn:', round(eval_average_return, 2))\n",
    "            print('OtherLogs:', agent.logger)\n",
    "            print('Time:', int(time.time() - start_time))\n",
    "            print('---------------------------------------')\n",
    "\n",
    "        \n",
    "        print('---------------------------------------')\n",
    "        print('EvalEpisodes:', eval_num_episodes)\n",
    "        print('EvalEpisodeReturn:', round(eval_episode_return, 2))\n",
    "        print('EvalAverageReturn:', round(eval_average_return, 2))\n",
    "        print('Time:', int(time.time() - start_time))\n",
    "        print('---------------------------------------')\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8a8051710>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()\n",
    "plt.plot(train_avg_ret)\n",
    "\n",
    "plt.show()\n",
    "plt.plot(train_ep_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "VEMSnKf9lsuB"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fqYvVwLCzHs6jFt4/MY9Iwle+2OQTwfcAW4BXROQssAE4LCKdJCL2jWn33QBcLHSRmuIzktxMEhF8RewkzLWDr8PrIhpXjOtu1oqjd3iaTS311DtyH/3cVgbNToEMRmMGRgFAPhH84MQ8nV7XqsNPzCDnZ1BKHVVKtSulupVS3SRE/Ual1BDwFHC3iDhFZAuwHThg6oo1RWEkuJBqxy6mF0iqBj7rFI0ulaxUeoeCWTlIZqLNY73h2EopGkikafL5XA5OzhXdRdIgmzLJrwMvADtExC8i9y13X6XUceBx4ATwNPCAUsr6djTNqowEQ6nLzja3s2gpGmODqaspu8vTdq9udqpEFiIxzo7N5bzBapAIMqzPwdfahca62oy3J+YV5P659OdQZFAoq147KaXuWeX27iW/Pwg8WNiyNKUkFI0xORdJdY76PA5evjBRlOcanJinw+vEWZNd9axhV6Aj+Mqib2SGWFxlNeQjE21uJ1PzEcLReElSGZkwJjmJZHbB7PA6OXFpOqdzRmNxhqZKUwMPupNVw+KlqJEO8bmdRbMryKUGHhYvj/Xgj8ri2OAUALu68o/gAcZmrXvfl6uBN+j0uhidCRGJxbM+53AwRDSuStLFClrgNSyKZypF43EWza5gcHKe9TmUhzlr7DTX1+oIvsI4MDBOm9vBlraGvB6fanaycHRfYJkuVoOORhdK5db1bRjt6QheUzKM/LaRoilWo0k8rriYYwQP+ec6Ndaxf2CcfVtalk1vrEbKrsDCjdbVBD6fZqfBycQeVKly8FrgNQSSXi++tCqaxHFzv1wjwRCRmMo5emn3urRlcAXhn5hjcHKefd0teZ/DqD23qhY+FleMz4Yy1sAbdOQh8P7x3KrICkULvIaR5FCD1uRQA1+Roqd8o5cOj57NWkm8eDbhbLJvS2ve5yhWkJEtY7Mh4mr5EklYFPhcBn8MTs7T5nbiqi2NRZcWeA0j0yFaG5wpd8dimT0ZTU65enC0exNdjcX0qNeYx4GBcbyumpwtCtKpc9hpcNgtS9GsVgMPiYCo1i4M5RB8lLJEErTAa7i8yQnAnbQrMLsOOd85lB1eF7G4srSiQpM9+wfGubm7BXuWQ7aXw8rZrNkIvM0mOXslDU6WZtCHgRZ4zWVNTpCYOVmMZqfByXlaGhw5t67ryU6VQyAY4kxgln1b8s+/G1hpV7DoQ7NyQ16H15m1XUE8rhKTnEqUfwct8BqSAr8kUkn40Zgs8BO5V9CAns1aSSzm380QeOvsCozN3TaPY8X7deQwm3V0JkQ4FtcRvKZ0xOKKsZlQKko2KFYEn4/At6eqFXQEX+4cGBinrtbO1esbCz5XMT2RViMQDOF21qx6tdnhdWXtKOnPchaxmWiBX+OMzSSqBdJTNJCwKzAz/6mUwj8xl9eH2yhV081O5c+BgXFu3NxE7SrjGLOhze1kYi6SU6eoWaxWA2/Q2ehiNhwjuBBZ9b6pIoMS+MAbaIFf44wssSkwSNgVmFe5Mj4bZiGS3+Wpo8ZGa4NDR/BlztR8hJND0+zrzr88Mh2j2amYA+CXY3Rm5Rp4g84cri6NLtZS1cCDFvg1z0iqyWlJisZku4JcbYKX0u51pRqyNOXJoXPjKGVO/h3AlyzXtaIWPtsIvj0HMzz/xBxN9bU0OHP3x88XLfBrnEWbgss/zGbbFQzmWSJp0OF16gi+zNk/ME6tXbhhU5Mp57NyNmvWKZocmp1KXSIJWuDXPCPL1Pua3Um42OSUX/6x3ePUOfgy58DAONdtaDKtS3NR4EubolmIxJheiGadg4fsZrPmW0VWCFrg1zgjwQUa62qv+FKabVcwODmPx1mDty6/y9OOpDWr7mYtT+bCUY76p0xLz4B1hmPG8xkd3StR76jB46pZtdkpUWQwX9INVtACv+YZmb6yBh7MtyswWrTzdRds97qIq0TVj6b8eOn8JNG4MlXgGxx2XLW2kjc7ZdPFmk42tfATcxHmIzEdwWtKy9IuVgOz7QryrYE36EjNZtUCX47sHxjHJnDT5mbTzml0VJc6gs+2i9Wg0+ta1Y/GGFVZyhp40AK/5gkEr2xyAvPtCvKtgTdoz8OaVVM6DgyMsWddIx5X5vml+eKzwI/G6GLNJYJfrdmp1IM+DLTAr2GUUkmBz/xBNsuuYHohQnAhWtCHOzWbVZdKlh2haIyXzk+amp4xsDKCb80iBw/Q2bi622mhRQb5ogV+DTM1HyEciy8bqZgVwS82eOT/4W5zOxHRKZpy5NjgFKFovKoEvqXBkXU3bqfhdrrCOgstMsgXLfBrmMVZrJlzjWbZFRRaAw9Qa7fR2uDUzU5lyP6BhMHYzQVMcFoOn9tRtAHwyxEIZtfFamB8f1baaC20yCBftMCvYZZrcjJoM8muILXBVGAFQaIWfm1H8GMzIf782ydYiMSsXkqKAwPjbG9309KQXUojF4o5AH45AjPZNTkZZNPs5J+YK3n+HbIQeBF5RERGRORY2rE/E5EjIvKyiHxfRNal3fZxEekTkV4RuaNYC9cUjmFTsFIO3owv1+DkPM4aW1Z1xSuR6GZd2xH8Qz/u5/88N8DPz4xZvRQg4UZ68OxEUdIzYE03a7ZdrAZGs9PwCunMQqvI8iWbCP4rwJ1Ljv2NUupapdT1wLeBTwKIyG7gbmBP8jEPiUhphg9qcma1FI1ZX67BSXMuTzu8rjUdwU/NRXjswHkA+gOzFq8mwclL08yEolUj8EbhQS4C3+Z2YhOWraSZmk8UGZS6RBKyEHil1LPA+JJj02m/NgDGNfxdwGNKqZBSagDoA/aZtFaNyYxMh6h32HEvY35kmsCb1KLd7nUxNhsiaoF9bDnwf/efYzYcw1Fjo29kxurlAIv59+IJfHHmAy9HMBQlFI3nlIO32wSfZ/nJToMW2AQb5J2DF5EHReQC8EGSETywHriQdjd/8limx98vIgdF5GAgEMh3GZoCWDqLdSltJrn5JVq0TRB4jxOlSu9NUg6EojG+8rOz3La9jWvXN9IfKA+BPzAwxqaWeroaixOdpuwKgqV5z3PtYjXo9LqWTR+atQeVD3kLvFLqE0qpjcCjwIeThzNdg2fcoVNKPayU2quU2uvz+fJdhqYARpZpcjIww49mPhxjbDZsSvTSsYabnZ58aZBAMMSHbu+hx+fmTBkIvFKKAwPjRYveATzOGhw1tpJF8PkKfIfXtewmq2GVXZabrFnwz8D7kj/7gY1pt20ALprwHJoiEAiG8GWwKTAww66gUB/4dDpy8N6uJuJxxT89e4Y967y8flsr29rdjM6EmZyz9kqmb2SGiblIUQVeRPC5nanu0mKTdwTfuHwEPzgxj6vWVpQqo9XIS+BFZHvar+8CTiV/fgq4W0ScIrIF2A4cKGyJmmIxMr1yiiblBVJAiubCeOLy1IzoxYjgRywYAGElPzw1wpnALPffvhURoae9AcDyNI2Rf39NEQUejOHbpfljZlwp5JKDh8Rnc3ohynz4yvJVw0Wy1DXwAKu2VYnI14E3AG0i4gc+BbxNRHYAceAc8NsASqnjIvI4cAKIAg8opcqnYFeTYjYUZTYcWzFFA4kcaCHRkyFCPT533ucwaG1wIMKq1qzVxsPP9rO+qY63X9MFwDafB4D+kVlu2lxccV2JAwPjdHidbGop7uZhm9vJpSwHWxdKIBii1i401uXmqdOR1uy0pa3hstusKpGELAReKXVPhsNfWuH+DwIPFrIoTfFZbhbrUnxuZ2qTKB/6A7M019fSbMLlaY3dRpt7bTU7HTo3wYtnJ/jUO3dTk2ydX99cl6iksTCCX8y/txY9Mm1zOzk6OFXU5zAIBEOJskdbbq8pvdlpqcD7J+a4ZkOjaWvMBd3JukYxouBMVsHpFGpX0B+YMSV6N+jwOteU4djDz/bTWFfLr+xd3Nqy24StbQ30W1gq6Z+YZ2h6oaj5d4M2j4Ox2TDxEtgV5NrFatDZmHjMyJLP5lw4ysRcxJINVtACv2ZZjOBXSdEUaFdwxmyB97hSFgvVzpnADN8/McyvvXbzFYOae9rdlubgU/XvRfCfWUqb20ksrpicjxT9uXL1oTHoWMauYNFoTwu8poRknaIpwK5gai7C6Ew4tSloBu1e5xVRUrXyxZ8OUGu3ce8t3Vfc1uNzc358zjJPmgMDYzTV17K93bw/3stRym7WXLtYDdzOGuod9iuanfwWNjmBFvg1y0hwAYfdRlP9yptJhXy5+kfN22A1aPe4GJ0JE6nybtZAMMQTh/2878YNGQWnx9dAXMG5sfz3RwrhwMA4N3e35JyrzgezB8AvRyyuGJsNp54vF0QkY7OT38IaeNACv2YJTCcildU2yAoS+BHzBd64FC72l91qvvbCWSKxOL9125aMt29LRs5WpGmGpxc4OzZX9PJIA5+nNHYFE3MJW+J8InjI7JXkn5jDYbfllfYxAy3wa5SRYCjVBr4ShXiB9AdmqbWLqdGL0exUzbXws6EoX3vhHG/Z1cHWZf44bm1LHLfCk+ZAkf1nllKqCD7fJieDzsYru1kHJ+ZZ1+QqyZVOJrTAr1FW86ExMD7s+Xy5+gMzdLc2pMr7zGAt2BU8fvACU/MRPvQLPcvep85hZ31TnSUR/IGBcRocdnZ3eUvyfI11tdTapejNToUKvLE/lF7tYwz6sAot8GuUkRVmsaZTiF2B2RU0sLgpXK3NTtFYnP/z0wH2bm7mps3NK953m0WVNAcGxrmpu8XUP9wrISK0NhR/dF9K4PNMp3R6XURiivE0C4nByfmSz2FNRwv8GiQUjTE5F1m1RBLytyuIxOKcG5sztYIGoNXw3q7SUsnvHL3E4OQ899++ddX79vjc9I/MlqQ+3GBiNkzvcLBk+XeDNo+j+AI/U2CKZkmp5EIkRiAY0hG8prQEUoM+svsg52NXcH58jmhcmR7BG97b1VgqqZTi4WfPsNXXwJt3dax6/23tbuYjMS6V8GrmxbOlzb8blGL4diCYmI+wtOcgWzoaDa+kxPtx0eIKGtACvybJtgbewOd25pyDNypoltskLIRqnez0s/4xjl+c5v7btma1KdfjS1wdlXKj9cDAOI4aG9eWuPXe53YW3RM+3xp4g8Vmp8Rn00wn1XzRAr8GWRy2vXqKBvKzKzBGym31mZuiAWP4dmVE8IOT88yGolnd9x9/0k+b28m7b8g4I+cKeoxSyRIK/PP9Y1y/sQlnTWkncbZ5nIzNhlCqeOmofLtYDdo9TkRINTsZTU5WpmjyuxbRVDSBYHY+NAbpdgX2LMu9+gMztHuceF25ufJlQ7vXxeHzk6af12z2nxnjAw//HEiUm25qqWdzawMbW+rZ3FLP5tZ6NrXU4/M4OXkpyE9fHeUP79iBqzY78WxtcNBUX1uyjdaTl6Y5eWmaT79zd0meL502t5NITDE1H6Gpvji+6oGZUEGdubV2G60NztRs1sGJeew2SeXmrUAL/BpkJBhCJCEQ2dDmXrQryPYSthgVNAYdHhfjs2HC0TiOmvK9CD0wMI4I/P6br8I/Mc/58TkODIzz5MuDpAeidbV2XLU26h12fvU1m7M+v4jQ43OXLEXzzUN+au3CXddnd4VhJun9GEUT+GCIW3paCzpHZ+OiGZ5/Yo5Or6tk1UaZ0AK/BhmZDtHa4Mz6g5c+ui8bgVdK0R+Y5Z3XdRW0zuUwmp0CMyFL85urcWRwiq1tDfzum7ZfdjwUjaUE//zYHOfG5jg/Pscbd/poXMU6Yik9vgZ+dKr4M40jsThPvjTIm3d1mGL9nCu+VLNTmG3t5p8/FI0xNR8puOO0w+NK5d4HJ82ZRVwIWuDXINk2ORnkalcwNhtmaj6S6rY0m8XNLOsGKWTDUf8Ur8sQETpr7PT43KZc4Wxrd/P4QT9Tc5Gc/zjkwjOnRhibDfP+vRuK9hwr0ebJ3zIjG4w9pkI2WSFRSXP4/ASQSNG8tsArgkIp3+tbTdEYCYayzr9D7nYFKQ+aIjkNGhu3rw5bP3h6OUamFxiaXuCa9cWtNjH+SBR7+Mc3DvnxeZzcvt1X1OdZjmI7So4W2MVq0Ol1MTEXYSYUZWh6wTIXSQMt8GuQbLtYDdpytCswKmh6ilBBA7CxuZ56h51TQ8GinN8MjvgTE4iKXU5oCHwxN1pHZ0I8c2qE996w3rJ8clNdLXabFE3gC7UpMDA2VI9cmCSuYIPFV5ha4NcYsbhibCaUdYkkgCdHu4L+wAyuWhvrGovz4bbZhB2dHk5emi7K+c3gyOAUNoHd64rr17KxpR6H3VZUgX/ypUGiccUv32RNegYS73lrg6NotfCFdrEaGM1Oh84l0jRWlkiCFvg1x9hMiLjKvkQScrcrOBOYYWubu6gOejs7vZwaCha1LroQjvon2d7uod5R3G0uu03YUsTxfUopvnnIz3Ubm9je4SnKc2RLMbtZjQi+taFAgU9+rw4l8/BWb7JqgV9j5NrFapCLXUF/YLZo+XeDXV0epuYjV0zQKQeUUhwdnCrZoOWE6dhsUc59/OI0p4aClkbvBm2e4gp8c31twWW3Rorm8LkJRKCrSFex2aIFfo1h+GT4ckjRAPjcjqxy8AuRGBcm5tjaVpz8u8HOzkTq49Sl8svDX5paYHQmXLJ2/h5fA+fH5whFzR/f981Dfhw1Nt517TrTz50rbe7CBsCvRKE2BQaNdbU4a2xML0Tp8Lgs79PQAr/GCOQZwfs8zqy+XGfHZlGqeBU0Bjs6E+mCk0Pll4c3NliLXUFj0NPuJhZXpo/vC0VjPPnyIG/d3VHUEsxs8bkTV5HFSMsFsuzxWA0RoTOZh7c6/w5ZCLyIPCIiIyJyLO3Y34jIKRE5IiL/JiJNabd9XET6RKRXRO4o0ro1eWL40OT6YU63K1iJ/pHiVtAYNNbVsr6priwj+KODk9TYhF0lGoiRqqQxOQ//w5MjTM5FeP/ejaaeN1/a3E7C0TjBLL19cqFQH5p0OpJXx1bn3yG7CP4rwJ1Ljv0AuFopdS1wGvg4gIjsBu4G9iQf85CIlNaVSLMiI8EQjXW1WfudGBh2BRNzK0fxRjVHsZqc0tnZ6eFUmUbwV3V4cv5/nC9bi+Qq+c1Dfjq9Lm7d1mbqefOlLTmb9YzJ+w1KKQLBUF7DtjNhVNKUQxPeqgKvlHoWGF9y7PtKKePP6M8BYwfmLuAxpVRIKTUA9AH7TFyvpkBy7WI1yHZ035nADOub6qhzFF/cdnZ56A/MFiX3nC/GBmsp7XTrHTWmj+8bmV7gx70jvPfG9VkbzBWb129ro7m+lo89cYT5sHnv+Ww4xnwkZkqKBqAzWUlTESmaLPgvwHeTP68HLqTd5k8euwIRuV9EDorIwUCg+F4amgS5drEaZNtJ2B+YLYpFcCZ2dnqJxZUlg6eXwz8xz+RcpGQVNAY9JlfS/NtLg8QVZVE9Y9DucfHZD1xP73CQT37r2OoPyBKzmpwMDCsNq7tYoUCBF5FPAFHgUeNQhrtlTNoqpR5WSu1VSu31+axpf16LjEzn1uRkkI1dQcJkrHgukkvZ1ZXYaC2nPHyqg3V9U0mft8fXQH9gxpTxfUopvnHIz02bm4sysKUQ3rCjnQ+/cRvfOOTn8YMXVn9AFpgt8Ds7vTjstoKsh80ib4EXkXuBdwAfVIvb2n4gfUdmA3Ax/+VpzMTINeaTokmZPa3QSTg0vcBcOFb0ChqD7tYGHDW2ssrDHxmcxGG3cVVnab/cPT43c+GYKX0Br/in6BuZ4f1lFL2n85E3X8UtPa38yZPHTOlmNlvgb93exqE/eTPrKiEHnwkRuRP478C7lFLptVlPAXeLiFNEtgDbgQOFL1NjBlPzEcKxeF4fZMOuYKVmp1JV0BjU2G1c1eEuK0+ao/4pdnZ5Sj7xaFu7eZ403zh4AVetjbdfWxy750Kx24T/7+4b8NbV8v8+epjgQqSg8xkDcMyqogHwFGHQTT5kUyb5deAFYIeI+EXkPuDzgAf4gYi8LCL/CKCUOg48DpwAngYeUEqVzw7YGifVxZrHhJls7AoMcdlWwsv6nZ1eTpZJiiYeT3awlqj+PZ2Uq2SB+xELkRhPvXKRX7q6q2xEKhM+j5PP3XMD58Zm+di/Hi2oNj4wE8JuE5qLNEjESlY1ylBK3ZPh8JdWuP+DwIOFLEpTHBZnseYXqaxmV3AmMIPbWWPapW427Oz08M1DftM6EQvh3PgcwYVoyQdSQ2KPpLGu8PF93z8xTHAhWlabq8vx2q2tfPSOHXzm6V72dbdw7y3deZ0nUSLpKKp3klXoTtY1hGFTkK/A+1ZpFe8PzNLja0CkdF8Uo5motwzSNEf8kwBcU+INVjDG9zWk0mT58o2DF1jfVMfrtlo7qCJbfvv2Hn5xZzt//p0TvHxhMq9zlENwUCy0wK8hCknRQOKyeKU6+FJW0BjsTFoWlMNG61H/FM6axL6AFfT43AUN/rg0Nc9zfaO878b1FRPN2mzC373/Oto9Lh549DCTqzTiZSIwY14Xa7mhBX4NMTIdot5hx+3Mz8J2JbuCmVCUS1MLJaugMWh1O2n3OMsiD39kcIo967yWDcXY1u4mEAwxNZ/fpuO/Hk4MA39fBaRn0mlucPCFD97ISHCBP3j8lZxLRXUEr6kK8u1iNVjJrmCgyFOcVmJnl9fyCD4WVxwfnOLaDU2WrcG4ejqTRxRv+L7v29LC5tbSv4eFcv3GJj7xtl388NQI//TsmawfF48rxmbCWuA1lU9iVF9+6RlY7GbNlKY5M5qcw2pBY8yuTg+vDs8QjcVL/twGA6MzzIZjllTQGBhXT/lU0hw6N8HA6GzZ1r5nw723dPP2a7r42+/3sv/MWFaPmZyPEI0rnaLRVD6BYAhfHjYFBkaUk6mbtX9kBpvAptbSt2fv7PIQjsUZGC3O0ItsKNUM1pXY2FyXHN+X+/+Hbx7yU++w87ZryrP2PRtEhL963zVsaqnnd7/+EhfG51Z1P11scso/8ClnijtPTFNWjEwv8AtX5W8LsZJdQX9glk0t9SVv8IHF4R8nh4KWjZU74p+i3mG3tLW/xm6ju60+5wh+Lhzl20cu8bZrumjIc3+mXPC4annogzfy7i88z22feQaAulo7Dc4aPK4aGpyJPSjjv5lQok2nWlM0lf1uarJmNhRlNhzLy2jMYCW7AisqaAx6fG5qbMKpS9O86zprJg8dHZzi6nWNljsv9vjcOZeMPnbgAjOhaEWnZ9LZ1eXlG7/9Og4MjDMbijETijATijETijIbijKzEOXi5AKz4cTPXY2uVCdwtaEFfo2wOIs1/0tRj7MGRwa7glhccWZ0ltsLuDooBEeNjW3t1lkWRGNxjl+c4oOv2WzJ86ezrd3N908ME47GsxoXNz4b5u//8zS3bW9j35aWEqywNFy7ocnSDe9yQefg1wgj04U1OUEix+nLYFcwODFPOBq3pILGYGenh1MmGE/lQ19ghoVI3NL8u0GPzxjfl10e/rM/OM1sOMYn37G7pA1qmtKgBX6NsNjkVFiuMZNdQX+ygsbK/PPOLi8XpxaYmivMeCofSj2DdSVyMR07NTTNo/vP8Wuv3WzZ3oWmuGiBXyOYkaKBzHYFxixQq3LwsNjRasUQ7qP+KTzOGrrLoH58S1tiDatV0iil+NOnTuCtq+Ujb95eiqVpLKCiBd4/McfffO8Ug5PzVi+l7BkJLlBrF5rrC3MIbHNfaVfQH5ilub6Wlgbr3PgMT5pC0jT5OhIeGZzi6vWNZdHe3+CsYV2ja9VKmu8dH+aFM2P8wVuuoqkKXRQ1CSpa4GdDMb7wTD8v9GfX1LCWCUwn/DYKzbP6PFfaFVhZQWPQ7nHSXF+b90brS+cnuPpT3+Pw+YmcHheOxjl5abos8u8GifF9ywv8QiTGg/9xgh0dHu7Zt6mEK9OUmooW+O3tbryuGg6eHV/9zmuckWAIX54mY+lksis4UwYCLyIJb/g8Bf7R/eeZDcf4038/kZOXyenhIOFovOQzWFeix+emf2Rm2SuSR54f4ML4PJ98527LfHM0paGi312bTdjb3cKLWuBXpVAfGoOlw7en5iKMzoTpabc+/7yzy8PpoeCq3YtLmQtH+e7RS6xvquOVC5N865XBrB9r1QzWlehpdzO7zPi+4ekFPv+jPt66u4PXb2uzYHWaUlLRAg+wt7uZ/sAsYysMotAYPjSFC7zR8Wfk4VMVNG3WN4rs6vQyH4lxfnxu9Tun8b3jQ8yGY/zt+6/jmvWNfObpXubD2Q0iOzo4SWNdLRtbrJ+/aWBM1MrkDf+Zp3uJxhSfePuuUi9LYwEVL/A3dyeaMw6dyy13upYIRWNMzkUKrqCBK+0KUhU0ZdAJuLMr6Q2f40brE4cG2dhSx2u2tPAn79jNpakFHs7SkfCIf4prNzSWVQ25cTW1NA//8oVJnjjs577btlSkY6Qmdype4K9Z34jDbtMCvwIBk2rg4Uq7gv7ALLV2YWOz9RHs9nYPNiGnPPzFyXme7x/lPTdswGYT9m1p4W3XdPKPP+lnaOrKFEc6C5EYvUPBsqh/T8fnduJx1VxWSaOU4k///Tg+j5MH3rjNwtVpSknFC7yr1s61Gxp1Hn4FFmvgCxd4w64gFcEHZuhubSiLzbo6h53utoacIvgnX04OubhxferYx+7cRSyu+Jvv9a742FNDQaJxVVYVNJDYcN62pJLmWy9f5KXzk/zRHTvyHviiqTys/1aawE3dzRwdnGIhkl3edK2xOGy78BSNYVeQysGXQQVNOrs6vVmXSiqleOKQn5u7my9LWWxqrec3b+3micN+jiY3UTNx1JjBWoaeJz0+dyqCnw1F+cvvnuTaDY2878bqMBTTZEdVCPzNm1uIxBSv5Dl0t9oJGMO2TUjRwKJdQSQW5/zYHFst9KBZys5OD+fH55gJRVe97xH/FP2BWd6bQfQ+/MZttDY4+LNvn1i23PCIf4rWBgfrGsvPS3xbu5uRYIjphQj/+JN+hqdDfOqde8qiGUtTOqpC4G/a3AzAQZ2Hz8hIMIQItJrUaWrYFZwfnyMaV2UVwe9MdrRmY5n7xGE/zhobb7/2yiEXHlctv//Wqzhwdpynjw1lfPzRwSmuKbMNVgPjPXn2dIB/evYMd12/LvU90awdVhV4EXlEREZE5FjasfeLyHERiYvI3iX3/7iI9IlIr4jcUYxFL6W5wcH2drfOwy/DyHSI1ganaXnyNreT0ZlQWVXQGBieNKvNaA1FYzz1ykXeuqcTryuzfcMH9m5kR4eHv/juSULRy9N/8+EYp4eDXFtmG6wGhrPn/3jyGHYRPvZLOy1ekcYKsvnGfwW4c8mxY8B7gWfTD4rIbuBuYE/yMQ+JSElG/OztbuHQuYmcJ6pXO0opBsZmTdlgNfB5nIzNhHh1xHCRLJ8UzYbmOtzOGk5dWjmCf+ZUgMm5CO9N21xdSo3dxv94xy4ujM/z5efPXnbbiUtTxFV55t8BNrXUU2sXJuci/M4beuhqtL7KSVN6VhV4pdSzwPiSYyeVUplKDO4CHlNKhZRSA0AfsM+Ula7Czd3NBBeinB6xZuhDufLFn57hwMA4d17dado5DbuCg2fHafc4l42ArSBhWeBZNYJ/4rAfn8fJbat0c9623cebdrbz+R/1XTaqsBxmsK5Ejd1Gj8/N+qY67r99q9XL0ViE2Tn49cCFtN/9yWNXICL3i8hBETkYCAQKfmKj4enFszoPb/D0sSH+8runePs1XXzYxNpnw67g4NmJsoreDXZ2eTh1Kbjs5ujYTIhnTo3wnhvWZ5W2+uO372IhEuN//eB06thR/xTtHicdJvj7FIvPfuB6vnbfPly1pZ+TqykPzBb4TLtNGb9lSqmHlVJ7lVJ7fb7CR71taK6jw+vUxmNJXrkwyUf+5SWu29DE3/3KdaZWTxjdrMFQtKw2WA12dnoJhqLL2kj/+ysXicbViumZdHp8bn71tZt57MD51JXBkcGpso3eDXZ1ecvy/dGUDrMF3g9sTPt9A3DR5OfIiIiwd3MLB3UEz+DkPP/1awdpczv54q/vNT2CS59AX44CsitlWZA5XffE4UH2rPOys9Ob9Tk/8ubteFy1PPidk8yEovQHZrimjAzGNJpMmC3wTwF3i4hTRLYA24EDJj/HsuztbmZwcn5NDwAJLkT4L19+kYVwjC//xs2XibFZtKULfBlV0Bhc1bF8Jc3p4SBHB6dybvhpqnfwe2/azk9fHeVzP3oVpco3/67RGGRTJvl14AVgh4j4ReQ+EXmPiPiB1wHfEZHvASiljgOPAyeAp4EHlFIlay818vBrNU0TjcV54J9foi8ww0O/emPR5mwadgWApYO2l8PjSrg7ZvKkeeKwnxqb8K7r1+V83l973Wa2tjXwTz9JGJFdXaYlkhqNQTZVNPcopbqUUrVKqQ1KqS8ppf4t+bNTKdWhlLoj7f4PKqV6lFI7lFLfLe7yL2dnp4cGh31NpmmUUnzqqeM8ezrAn7/7am7bXvi+xnIYdgWuWhvryrT8bmen9wpPmlhc8eRLg7xhhy+1UZwLtXYbf/y2hM3uukZXUa6ONBozqYpOVoMau40bNzevyYanLz03wKP7z/Oh27eWZAxbm8fJljZ32ba+7+r0MDA6e5k/0fN9owxPhwryY3nTrnbeed26jN2vGk25UXW2cns3t/D3PzzN9EKk6PXZfSMzRGLx1MBnq/je8SEe/I+T3Lmnk/9+Z2k6Fv/ojh3kOaO6JOzs8hJX8OrwTGqc3hOH/TTW1fKLu9rzPq+I8Ll7bjBrmRpNUamqCB4SDU9KweES+NL8weMv8+uPHCAcjRf9uZbjqH+Kjzz2Mteub+SzH7i+ZBH167e1cev28h35ZlgWnExutAYXInzv+BDvvK4LZ42uC9esDapO4K/f1ITdJkXPw0/OhTkyOEUgGOLbR0pSCXoFFyfnue+rL9LS4OCL9+6lzqGFy2BzawOuWluqVPK7R4dYiMS1Xa5mTVF1Al/vqOHqdd6i5+Ff6B9DqURFyZeeG1i2a9JsIrE4p4am+dfDfn7zyy8yF47xyG/cbIrXezVhtwk7OhYtC7552M9WXwPXb2yydmEaTQmpuhw8wE2bW3h0/znC0XiqnM9snusbpcFh54/u3MGffOs4+wfGee3WVlOfY3ohwsmL05y4NM2J5L+vDs8QjiVSQh5nDV/44I3s6CxOOWSls7PTy/dPDHFhfI4DA+P84R07ytLaV6MpFlUp8Dd3N/PI8wMcuzjFjZuK44H9fN8or93ayvv3buSz//kqX3puwBSBf/Z0gEf3n+PEpWkujC82bLU2ONi9zstv3trN7i4vu7u8bGkrj1F55crOLg//cvAC//iTfkTgPTdkZ02g0VQLVSnwN3UnB4CcHS+KwPsn5jg7Nsevv64bV62dD75mE59/po+zo7N0t+Xf+DM2E+KBRw9T57Bz85YW7r55E7vXednT5cXnceroM0cMK4LHXrzALT2trGsqz5p9jaZYVGX41+5x0d1aXzRnyZ/1jQGkqkh+7bWbqbEJX/nZ2YLO+79/+CpzkRj//Fuv4Qv/z4088MZtvHFHO+1elxb3PDAqaWJxxXtv0JurmrVHVQo8LA4AKcbm53N9o/g8TrYnfVjavS7eed06Hj94gan5SF7n7A/M8Oj+89yzbyPb2nVO3QyaGxx0el3UO+ym+uFrNJVC1Qr8zd3NjM+GOTM6a+p543HF832j3Lqt7bKo+r5btzAXjvEvL57P67x//d1TuGrtfOTNV5m1VA0J/5jf/cXtNDirMhup0axI1Qr83iIZj/UOBxmbDfP6JZOA9qxr5LVbW/jqz84RjeXW+HRgYJzvnxjmt39ha14eKZrleeCN2/idN/RYvQyNxhKqVuC3tjXQ0uAwPQ//fN8oAK/fdmXFzH23bmVwcp6njw9lfb54XPHgd07Q6XVx3616tJpGozGPqhV4EeGmzc2mR/DP9Y2y1deQcYjxm3a2091az5eeG8j6fN8+eolX/FN89I4duhNVo9GYStUKPCTy8GfH5hgJLphyvnA0zv4z49y6zKBmm034zddv4aXzkxw+v/qVw0Ikxl9/9xS7u7y6Rluj0ZhOVQu8kYc/ZFKa5qXzE8xHYlfk39P55Zs24HXVZBXFf+2FswxOzvOJt+/CXqa2uxqNpnKpaoG/el0jzhqbaXn45/vHsAkrdqw2OGu4Z98mnj42tOLowInZMJ/7UR9v3OFb8Q+GRqPR5EtVC7yjxsb1G5s4dM6cPPzzfaNcu6GJxrqVfeZ//ZZuAL66QuPT//7Rq8yGonw8OSFIo9FozKaqBR4Sc1qPXZxmLhwt6DzBhQgvX5hcNv+ezvqmOu68upOvHzjPbOjK5z07Osv//fk5PnDzxtSAaI1GozGbqhf4vd3NxOKKl89PFnSe/WfGicVV1umU+27dQnAhyjcOXrjits987xS1dhv/TTc1aTSaIlL1An/j5mZEKDgP/1zfKK5aGzdubsrueTc1c8OmJr78s7PE4ot2CYfOjfMfR4f40O09tHu1h7tGoykeVS/wXlctOzo8HCwwD/983yg3d7fkNO7tvlu3cG5sjh+eHAZAKcWff+ck7R4nv3X7loLWo9FoNKtR9QIPiTz84XMTOVsIGAxPL/DqyExW+fd07tzTyfqmulTJ5H8cHeKl85N89K07qHdobxSNRlNcVhV4EXlEREZE5FjasRYR+YGIvJr8tzntto+LSJ+I9IrIHcVaeC7s7W5mNhzj1FAwr8cv2hPkJvA1dhv33rKZ/QPjHD4/wV8/fYqdnR7ed5O2rtVoNMUnmwj+K8CdS459DPihUmo78MPk74jIbuBuYE/yMQ+JiOX99zcnG57yndP6XN8ozfW17O7y5vzYD9y8iXqHnfu/dpDz43P88dt0U5NGoykNqwq8UupZYKky3gV8NfnzV4F3px1/TCkVUkoNAH3APnOWmj/rmupY31THgYHcBV4pxc/6xrhlWxu2PIS5sa6WX9m7kdGZMLdtb+P2q3w5n0Oj0WjyId8cfIdS6hJA8t/25PH1QHpdoD957ApE5H4ROSgiBwOBQJ7LyJ637O7ge8eHeOXCZE6P6w/MMjS9kHP+PZ3/etsWbtrczCffsTvvc2g0Gk2umL3JminEzThSSSn1sFJqr1Jqr89X/Kj2v73lKto9Lv7wm68QisayfpyRfy9E4Dc01/PE79zCdt3UpNFoSki+Aj8sIl0AyX9Hksf9wMa0+20ALua/PPNorKvlL997DaeHZ/jcD/uyftxzfaNsaqlnY0t9EVen0Wg05pOvwD8F3Jv8+V7gW2nH7xYRp4hsAbYDBwpbonm8cWc7v3zTBv7hJ/0c9U+tev9oLM7P+8cyDvfQaDSaciebMsmvAy8AO0TELyL3AX8FvEVEXgXekvwdpdRx4HHgBPA08IBSKvt8SAn4k7fvps3t4KPfeIVwdOW6+CODUwRDUe32qNFoKpJsqmjuUUp1KaVqlVIblFJfUkqNKaXepJTanvx3PO3+DyqlepRSO5RS3y3u8nOnsT6RqukdDvK5H7264n2ffzWRf7+lRwu8RqOpPNZEJ+tSfnFnB++7cQMP/bifY4PLp2qe6xtlzzovLQ2OEq5Oo9FozGFNCjzAJ9+xm9aG5VM1c+Eoh89PFFQ9o9FoNFayZgW+sb6Wv3jPNZwaCvL5Z66sqnnx7ASRWPb2wBqNRlNurFmBB3jz7g7ee8N6Hnqm74pUzfN9ozjstpTNgUaj0VQaa1rgAT75zt00Nzj4w28euSxV89yro9y0uZk6h+VWOhqNRpMXa17gm+od/MV7ruHkpWke+nEiVTM2E+LEpWlu3a7TMxqNpnJZ8wIPCZ+ad1+/js//qI8TF6f5Wf8YALf06AYnjUZTuWiBT/Lpd+2hqT5RVfPj3gAeVw3XrG+0elkajUaTN1rgkyRSNVdz4tI0Txz287qtrdTY9f8ejUZTuWgFS+Otezq56/p1ADr/rtFoKh49GHQJf/quPTTXO3jnteusXopGo9EUhBb4JTTVO/j0u/ZYvQyNRqMpGJ2i0Wg0mipFC7xGo9FUKVrgNRqNpkrRAq/RaDRVihZ4jUajqVK0wGs0Gk2VogVeo9FoqhQt8BqNRlOliFLK6jUgIgHgXAGnaANGTVpOOaBfT/lTba+p2l4PVN9ryvR6NiulfMs9oCwEvlBE5KBSaq/V6zAL/XrKn2p7TdX2eqD6XlM+r0enaDQajaZK0QKv0Wg0VUq1CPzDVi/AZPTrKX+q7TVV2+uB6ntNOb+eqsjBazQajeZKqiWC12g0Gs0StMBrNBpNlVLRAi8id4pIr4j0icjHrF6PGYjIWRE5KiIvi8hBq9eTKyLyiIiMiMixtGMtIvIDEXk1+W+zlWvMlWVe06dFZDD5Pr0sIm+zco25ICIbReQZETkpIsdF5PeSxyvyfVrh9VTye+QSkQMi8kryNf1p8nhO71HF5uBFxA6cBt4C+IEXgXuUUicsXViBiMhZYK9SqiIbNETkdmAG+JpS6urksc8A40qpv0r+IW5WSv13K9eZC8u8pk8DM0qpv7VybfkgIl1Al1LqsIh4gEPAu4HfoALfpxVez69Que+RAA1KqRkRqQWeA34PeC85vEeVHMHvA/qUUmeUUmHgMeAui9e05lFKPQuMLzl8F/DV5M9fJfHlqxiWeU0Vi1LqklLqcPLnIHASWE+Fvk8rvJ6KRSWYSf5am/xPkeN7VMkCvx64kPa7nwp/U5Mo4PsickhE7rd6MSbRoZS6BIkvI9Bu8XrM4sMiciSZwqmIdMZSRKQbuAHYTxW8T0teD1TweyQidhF5GRgBfqCUyvk9qmSBlwzHKjPfdDmvV0rdCPwS8EAyPaApP/4B6AGuBy4Bf2fpavJARNzAE8BHlFLTVq+nUDK8nop+j5RSMaXU9cAGYJ+IXJ3rOSpZ4P3AxrTfNwAXLVqLaSilLib/HQH+jUQqqtIZTuZJjXzpiMXrKRil1HDyCxgHvkiFvU/JvO4TwKNKqX9NHq7Y9ynT66n098hAKTUJ/Bi4kxzfo0oW+BeB7SKyRUQcwN3AUxavqSBEpCG5SYSINABvBY6t/KiK4Cng3uTP9wLfsnAtpmB8yZK8hwp6n5IbeF8CTiql/lfaTRX5Pi33eir8PfKJSFPy5zrgzcApcnyPKraKBiBZ9vT3gB14RCn1oLUrKgwR2UoiageoAf650l6TiHwdeAMJa9Nh4FPAk8DjwCbgPPB+pVTFbFou85reQOLSXwFngQ8ZudFyR0RuBX4KHAXiycN/TCJvXXHv0wqv5x4q9z26lsQmqp1EIP64Uup/ikgrObxHFS3wGo1Go1meSk7RaDQajWYFtMBrNBpNlaIFXqPRaKoULfAajUZTpWiB12g0mipFC7xGo9FUKVrgNRqNpkr5/wEt2AH0r06BywAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fd8a2a61550>]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.show()\n",
    "plt.plot(eval_avg_ret)\n",
    "\n",
    "plt.show()\n",
    "plt.plot(eval_ep_ret)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "W6vLJACbE6Fu"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "collapsed_sections": [],
   "machine_shape": "hm",
   "name": "Copy of dqn_working.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": "3.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}