train.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loading environment football failed: No module named 'gfootball'\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from kaggle_environments import make, evaluate\n",
    "from gym import spaces"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ConnectFourGym:\n",
    "    def __init__(self, agent2=\"random\"):\n",
    "        ks_env = make(\"connectx\", debug=True)\n",
    "        self.env = ks_env.train([None, agent2])\n",
    "        self.rows = ks_env.configuration.rows\n",
    "        self.columns = ks_env.configuration.columns\n",
    "        # Learn about spaces here: http://gym.openai.com/docs/#spaces\n",
    "        self.action_space = spaces.Discrete(self.columns)\n",
    "        self.observation_space = spaces.Box(low=0, high=2, \n",
    "                                            shape=(self.rows,self.columns,1), dtype=np.int)\n",
    "        # Tuple corresponding to the min and max possible rewards\n",
    "        self.reward_range = (-10, 1)\n",
    "        # StableBaselines throws error if these are not defined\n",
    "        self.spec = None\n",
    "        self.metadata = None\n",
    "    def reset(self):\n",
    "        self.obs = self.env.reset()\n",
    "        return np.array(self.obs['board']).reshape(self.rows,self.columns,1)\n",
    "    def change_reward(self, old_reward, done):\n",
    "        if old_reward == 1: # The agent won the game\n",
    "            return 1\n",
    "        elif done: # The opponent won the game\n",
    "            return -1\n",
    "        else: # Reward 1/42\n",
    "            return 1/(self.rows*self.columns)\n",
    "    def step(self, action):\n",
    "        # Check if agent's move is valid\n",
    "        is_valid = (self.obs['board'][int(action)] == 0)\n",
    "        if is_valid: # Play the move\n",
    "            self.obs, old_reward, done, _ = self.env.step(int(action))\n",
    "            reward = self.change_reward(old_reward, done)\n",
    "        else: # End the game and penalize agent\n",
    "            reward, done, _ = -10, True, {}\n",
    "        return np.array(self.obs['board']).reshape(self.rows,self.columns,1), reward, done, _"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create ConnectFour environment\n",
    "env = ConnectFourGym(agent2=\"random\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:\nThe TensorFlow contrib module will not be included in TensorFlow 2.0.\nFor more information, please see:\n  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n  * https://github.com/tensorflow/addons\n  * https://github.com/tensorflow/io (for I/O related ops)\nIf you depend on functionality not listed there, please file an issue.\n\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from stable_baselines.bench import Monitor \n",
    "from stable_baselines.common.vec_env import DummyVecEnv\n",
    "\n",
    "# Create directory for logging training information\n",
    "log_dir = \"ppo/\"\n",
    "os.makedirs(log_dir, exist_ok=True)\n",
    "\n",
    "# Logging progress\n",
    "monitor_env = Monitor(env, log_dir, allow_early_resets=True)\n",
    "\n",
    "# Create a vectorized environment\n",
    "vec_env = DummyVecEnv([lambda: monitor_env])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/policies.py:116: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/tf_layers.py:103: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/distributions.py:326: The name tf.random_uniform is deprecated. Please use tf.random.uniform instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/common/distributions.py:327: The name tf.log is deprecated. Please use tf.math.log instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/ppo2/ppo2.py:190: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/ppo2/ppo2.py:198: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/ppo2/ppo2.py:206: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/ppo2/ppo2.py:240: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
      "\n",
      "WARNING:tensorflow:From /Users/user/.local/share/virtualenvs/self_study-connect_four-AI-RpFZuoBN/lib/python3.7/site-packages/stable_baselines/ppo2/ppo2.py:242: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from stable_baselines import PPO2 \n",
    "from stable_baselines.common.policies import CnnPolicy, conv, linear, conv_to_fc\n",
    "\n",
    "# Neural network for predicting action values\n",
    "def modified_cnn(scaled_images, **kwargs):\n",
    "    activ = tf.nn.relu\n",
    "    layer_1 = activ(conv(scaled_images, 'c1', n_filters=32, filter_size=3, stride=1, \n",
    "                         init_scale=np.sqrt(2), **kwargs))\n",
    "    layer_2 = activ(conv(layer_1, 'c2', n_filters=64, filter_size=3, stride=1, \n",
    "                         init_scale=np.sqrt(2), **kwargs))\n",
    "    layer_2 = conv_to_fc(layer_2)\n",
    "    return activ(linear(layer_2, 'fc1', n_hidden=512, init_scale=np.sqrt(2)))  \n",
    "\n",
    "class CustomCnnPolicy(CnnPolicy):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super(CustomCnnPolicy, self).__init__(*args, **kwargs, cnn_extractor=modified_cnn)\n",
    "        \n",
    "# Initialize agent\n",
    "model = PPO2(CustomCnnPolicy, vec_env, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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103.977167 54.178235 \nL 104.002863 54.180548 \nL 104.13134 54.175921 \nL 104.336905 56.2487 \nL 104.388296 55.385813 \nL 104.413991 55.37656 \nL 104.516773 54.983287 \nL 104.568164 54.990227 \nL 104.59386 55.011047 \nL 104.748033 54.580761 \nL 104.799424 54.583074 \nL 104.850815 54.772771 \nL 104.876511 54.578448 \nL 104.979293 53.680861 \nL 105.004989 53.690114 \nL 105.05638 53.669294 \nL 105.184857 55.973409 \nL 105.210553 55.966469 \nL 105.261944 55.957215 \nL 105.28764 55.760579 \nL 105.364726 55.941022 \nL 105.390422 55.938708 \nL 105.441813 54.874559 \nL 105.518899 55.036495 \nL 105.647377 55.022614 \nL 105.698768 55.233131 \nL 105.775855 55.196117 \nL 105.80155 55.18455 \nL 105.904332 53.898317 \nL 105.930028 54.09264 \nL 106.341157 51.536368 \nL 106.366852 51.737631 \nL 106.443939 51.522488 \nL 106.469634 51.503981 \nL 106.752285 50.201554 \nL 107.214805 50.099766 \nL 107.2405 49.246133 \nL 107.343282 49.403443 \nL 107.394674 49.410383 \nL 107.47176 49.593139 \nL 107.497456 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123.454384 54.862994 \nL 123.531471 54.858368 \nL 123.659949 54.640911 \nL 123.788426 55.041124 \nL 123.814122 55.03187 \nL 123.839817 55.029557 \nL 123.942599 55.411263 \nL 123.968295 55.205373 \nL 124.045382 55.200746 \nL 124.071077 54.999483 \nL 124.148164 55.182239 \nL 124.199555 55.172986 \nL 124.22525 55.362682 \nL 124.302337 55.168359 \nL 124.379424 55.179926 \nL 124.430815 55.374249 \nL 124.482206 55.198433 \nL 124.533597 55.193806 \nL 124.559292 54.992543 \nL 124.636379 56.056692 \nL 124.68777 55.866996 \nL 124.764857 55.892443 \nL 124.790552 55.69118 \nL 124.91903 56.76921 \nL 124.970421 56.764583 \nL 125.124594 57.160169 \nL 125.15029 57.162482 \nL 125.175985 57.349865 \nL 125.227376 57.146289 \nL 125.253072 57.146289 \nL 125.278767 57.143976 \nL 125.38155 58.404761 \nL 125.407245 58.402448 \nL 125.432941 58.596771 \nL 125.458636 58.592144 \nL 125.484332 57.530308 \nL 125.561418 57.750078 \nL 125.715591 58.164171 \nL 125.89546 58.175738 \nL 125.946851 59.251454 \nL 126.023938 59.246827 \nL 126.203807 59.253767 \nL 126.332284 59.637786 \nL 126.35798 59.628533 \nL 126.409371 59.628533 \nL 126.460762 59.42033 \nL 126.512153 59.598459 \nL 126.666326 59.630846 \nL 126.794804 59.806662 \nL 126.871891 59.806662 \nL 127.000368 58.550503 \nL 127.051759 58.346927 \nL 127.10315 58.538936 \nL 127.128846 58.54819 \nL 127.257324 58.75408 \nL 127.283019 58.749453 \nL 127.411497 59.175113 \nL 127.462888 58.985416 \nL 127.514279 59.182053 \nL 127.539975 59.182053 \nL 127.771234 57.581202 \nL 127.79693 57.773211 \nL 127.874017 57.967534 \nL 127.899712 57.775525 \nL 127.925408 57.770898 \nL 128.053885 57.974474 \nL 128.079581 57.976788 \nL 128.233754 57.620529 \nL 128.25945 57.632096 \nL 128.362232 57.23651 \nL 128.413623 58.305286 \nL 128.490709 58.300659 \nL 128.619187 58.275212 \nL 128.721969 58.078576 \nL 128.747665 58.097083 \nL 128.799056 58.094769 \nL 128.824751 58.284466 \nL 128.850447 57.236509 \nL 128.953229 57.259643 \nL 129.056011 57.266583 \nL 129.081707 57.07226 \nL 129.133098 57.282777 \nL 129.158793 57.282777 \nL 129.287271 58.566696 \nL 129.312967 58.580577 \nL 129.364358 58.38394 \nL 129.441444 58.409387 \nL 129.46714 58.418641 \nL 129.492835 58.615277 \nL 129.569922 58.411701 \nL 129.621313 58.409387 \nL 129.749791 60.329483 \nL 129.775486 60.32023 \nL 129.801182 60.32023 \nL 129.826877 60.512239 \nL 129.903964 60.32023 \nL 130.032442 58.624531 \nL 130.058137 58.821167 \nL 130.135224 58.643037 \nL 130.238006 58.638411 \nL 130.392179 59.068697 \nL 130.494961 59.066384 \nL 130.597744 58.691618 \nL 130.649135 58.714752 \nL 130.700526 58.710125 \nL 130.751917 58.920642 \nL 130.880394 56.614213 \nL 130.90609 56.616527 \nL 131.008872 56.62578 \nL 131.060263 56.443024 \nL 131.111654 56.63966 \nL 131.342914 56.676674 \nL 131.394305 56.475411 \nL 131.445696 56.692868 \nL 131.728347 56.716001 \nL 131.754043 56.912638 \nL 131.805434 56.725255 \nL 131.908216 56.535559 \nL 132.036694 57.618215 \nL 132.34504 57.611275 \nL 132.447822 57.800971 \nL 132.473518 57.794031 \nL 132.5763 57.777838 \nL 132.910342 59.616965 \nL 132.961733 59.239886 \nL 133.013124 59.640099 \nL 133.03882 59.637786 \nL 133.090211 59.825168 \nL 133.141602 59.633159 \nL 133.244384 59.637786 \nL 133.295775 59.848302 \nL 133.347166 59.660919 \nL 133.449948 59.649352 \nL 133.501339 59.441149 \nL 133.578426 59.459656 \nL 133.655512 59.471223 \nL 133.809686 59.876062 \nL 133.835381 59.686366 \nL 133.912468 59.903823 \nL 133.963859 60.093519 \nL 133.989554 59.908449 \nL 134.040945 58.855867 \nL 134.118032 59.045563 \nL 134.195119 59.047876 \nL 134.683334 55.943336 \nL 134.709029 55.961843 \nL 134.837507 55.961843 \nL 134.965985 54.906947 \nL 135.274331 53.317663 \nL 135.300027 53.322289 \nL 135.325722 53.132593 \nL 135.351418 53.331543 \nL 135.402809 53.153414 \nL 135.4542 53.347737 \nL 135.505591 53.14416 \nL 135.531287 53.141847 \nL 135.659764 52.762454 \nL 135.711155 53.14416 \nL 135.788242 52.956777 \nL 135.865329 52.959091 \nL 136.173675 51.513235 \nL 136.199371 51.517862 \nL 136.225066 51.328166 \nL 136.302153 51.524802 \nL 136.482022 51.534056 \nL 136.533413 51.332793 \nL 136.559108 51.531742 \nL 136.687586 52.811035 \nL 136.841759 52.815662 \nL 136.944541 54.085701 \nL 136.970237 53.893691 \nL 137.073019 52.811035 \nL 137.098714 52.817975 \nL 137.227192 52.403882 \nL 137.304279 53.491165 \nL 137.329974 53.294529 \nL 137.458452 52.07307 \nL 137.58693 51.894941 \nL 137.612625 51.888001 \nL 137.792494 50.469906 \nL 137.895276 50.486099 \nL 137.998058 50.474532 \nL 138.075145 50.499979 \nL 138.126536 50.30797 \nL 138.306405 50.34961 \nL 138.460578 49.970218 \nL 138.511969 49.979471 \nL 138.640447 51.277271 \nL 138.717533 50.911759 \nL 138.820315 50.719749 \nL 138.948793 50.958026 \nL 139.051575 49.70418 \nL 139.077271 49.71806 \nL 139.205748 50.823851 \nL 139.25714 50.830791 \nL 139.282835 50.638781 \nL 139.359922 50.849298 \nL 139.385617 50.657288 \nL 139.411313 50.638781 \nL 139.642573 49.535304 \nL 139.693964 49.546871 \nL 139.745355 48.487348 \nL 139.822441 48.679358 \nL 139.950919 48.466528 \nL 139.976615 48.475781 \nL 140.130788 48.117209 \nL 140.23357 48.117209 \nL 140.284961 47.9252 \nL 140.310657 48.989349 \nL 140.362048 47.749384 \nL 140.387743 47.756324 \nL 140.516221 47.763264 \nL 140.644699 47.557374 \nL 140.875958 47.592075 \nL 140.97874 45.473029 \nL 141.030132 45.484596 \nL 141.055827 45.470716 \nL 141.081523 44.41582 \nL 141.184305 44.626336 \nL 141.21 44.624023 \nL 141.312782 44.425073 \nL 141.338478 44.438954 \nL 141.415565 44.445894 \nL 141.44126 44.253884 \nL 141.466956 44.455147 \nL 141.492651 44.445894 \nL 141.544042 44.651784 \nL 141.621129 44.640217 \nL 141.646824 44.44358 \nL 141.698216 43.388684 \nL 141.749607 44.432014 \nL 141.775302 44.413507 \nL 141.878084 44.20993 \nL 141.90378 44.214557 \nL 141.955171 44.221497 \nL 142.186431 42.572065 \nL 142.289213 44.006354 \nL 142.314908 44.001727 \nL 142.340604 44.001727 \nL 142.520473 43.421072 \nL 142.546168 43.425698 \nL 142.777428 45.225499 \nL 142.88021 45.021923 \nL 143.188557 45.008042 \nL 143.394121 44.242317 \nL 143.496903 44.233064 \nL 143.57399 44.62171 \nL 143.625381 44.404253 \nL 143.651076 44.399627 \nL 143.753858 44.591636 \nL 143.779554 44.580069 \nL 143.908032 44.587009 \nL 143.959423 44.792899 \nL 144.010814 44.598576 \nL 144.0879 44.617083 \nL 144.190683 45.697426 \nL 144.216378 44.626337 \nL 144.293465 45.866302 \nL 144.421942 45.671979 \nL 144.55042 45.660413 \nL 144.653202 45.882496 \nL 144.678898 45.866302 \nL 144.704593 45.669666 \nL 144.78168 45.695113 \nL 144.807376 45.896376 \nL 144.858767 45.50773 \nL 144.961549 45.52161 \nL 145.064331 45.907943 \nL 145.115722 45.898689 \nL 145.192809 45.903316 \nL 145.346982 45.53549 \nL 145.629633 45.54243 \nL 145.655328 45.348108 \nL 145.681024 45.352734 \nL 145.809501 46.60658 \nL 145.937979 46.803217 \nL 145.98937 46.803217 \nL 146.143543 46.403004 \nL 146.272021 46.789336 \nL 146.323412 46.583446 \nL 146.374803 46.782396 \nL 146.503281 46.782396 \nL 146.657454 46.37293 \nL 146.708845 46.36599 \nL 146.785932 47.453273 \nL 146.837323 47.45096 \nL 146.965801 47.432453 \nL 147.042887 47.638343 \nL 147.094278 47.626776 \nL 147.119974 47.626776 \nL 147.274147 47.21731 \nL 147.351234 47.205743 \nL 147.479711 47.006793 \nL 147.556798 47.013733 \nL 147.736667 46.414571 \nL 147.762362 46.423824 \nL 147.865144 46.236441 \nL 148.1221 47.536554 \nL 148.199186 47.543494 \nL 148.250577 47.735504 \nL 148.276273 47.531928 \nL 148.507533 46.55106 \nL 148.636011 46.948959 \nL 148.661706 46.942019 \nL 148.687402 46.750009 \nL 148.764488 46.967466 \nL 148.970053 46.960526 \nL 148.995748 47.145595 \nL 149.047139 46.939706 \nL 149.072835 46.939706 \nL 149.09853 46.937392 \nL 149.252703 47.319098 \nL 149.355486 47.335292 \nL 149.458268 46.937392 \nL 149.535354 47.816472 \nL 149.586745 47.605956 \nL 149.766614 47.617523 \nL 149.920787 47.24507 \nL 150.049265 47.24507 \nL 150.100656 46.861051 \nL 150.152047 47.249697 \nL 150.254829 47.272831 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154.083464 41.665226 \nL 154.160551 42.046932 \nL 154.417506 42.056185 \nL 154.520289 42.451771 \nL 154.57168 42.440205 \nL 154.648766 42.428638 \nL 154.700157 42.222748 \nL 154.751548 42.229688 \nL 154.777244 42.412444 \nL 154.85433 42.185734 \nL 154.931417 41.135465 \nL 154.982808 41.158598 \nL 155.034199 41.149345 \nL 155.136981 41.540304 \nL 155.162677 41.526424 \nL 155.239764 41.322847 \nL 155.291155 41.350608 \nL 155.31685 41.334414 \nL 155.445328 39.208428 \nL 155.471023 39.224622 \nL 155.496719 39.236189 \nL 155.650892 38.840603 \nL 155.702283 38.826723 \nL 155.856456 38.40569 \nL 155.907848 38.384869 \nL 155.933543 38.572252 \nL 156.01063 38.357109 \nL 156.036325 38.350169 \nL 156.139107 38.153532 \nL 156.293281 38.731874 \nL 156.318976 38.720308 \nL 156.550236 38.736501 \nL 156.653018 39.95796 \nL 156.678714 39.955646 \nL 156.7558 39.962586 \nL 156.858582 39.742817 \nL 156.961365 39.934826 \nL 156.98706 39.925573 \nL 157.089842 39.925573 \nL 157.115538 39.717369 \nL 157.192624 39.895499 \nL 157.295407 39.907066 \nL 157.321102 39.712743 \nL 157.398189 39.724309 \nL 157.423884 40.786146 \nL 157.526666 40.580256 \nL 157.603753 40.587196 \nL 157.629448 39.516106 \nL 157.706535 39.712743 \nL 157.732231 39.909379 \nL 157.809317 39.703489 \nL 157.886404 39.698863 \nL 158.040577 40.934201 \nL 158.117664 40.756072 \nL 158.143359 40.749132 \nL 158.220446 40.955022 \nL 158.271837 40.938828 \nL 158.580183 39.30559 \nL 158.65727 39.289397 \nL 158.760052 38.708741 \nL 158.811443 38.884557 \nL 158.88853 38.877617 \nL 158.939921 40.117583 \nL 159.017007 39.92326 \nL 159.042703 39.92326 \nL 159.145485 39.72431 \nL 159.171181 39.728937 \nL 159.222572 39.726623 \nL 159.428136 38.937765 \nL 159.556614 39.162162 \nL 159.685091 38.958585 \nL 159.736483 38.960899 \nL 159.839265 38.761949 \nL 159.993438 39.118208 \nL 160.09622 38.898438 \nL 160.250393 41.228 \nL 160.507349 41.228 \nL 160.661522 41.653659 \nL 160.687217 41.658286 \nL 160.815695 42.039992 \nL 160.841391 42.033052 \nL 160.944173 42.002978 \nL 160.969868 42.206554 \nL 161.046955 41.991411 \nL 161.098346 41.989098 \nL 161.226824 41.799402 \nL 161.252519 41.787835 \nL 161.278215 41.982158 \nL 161.355301 41.799402 \nL 161.406692 41.607392 \nL 161.458084 41.806342 \nL 161.560866 41.785521 \nL 161.637952 41.986784 \nL 161.663648 41.972904 \nL 161.689343 41.778581 \nL 161.740734 41.984471 \nL 161.76643 41.972904 \nL 161.843517 41.968277 \nL 161.869212 42.164914 \nL 161.946299 41.956711 \nL 162.100472 41.95671 \nL 162.22895 42.56744 \nL 162.254645 42.373117 \nL 162.383123 40.911068 \nL 162.537296 40.913381 \nL 162.562992 40.719058 \nL 162.588687 41.785521 \nL 162.665774 40.714431 \nL 162.845643 41.487096 \nL 162.871338 41.48247 \nL 162.922729 41.49635 \nL 163.128293 40.186984 \nL 163.179685 40.196237 \nL 163.333858 40.203177 \nL 163.488031 40.786146 \nL 163.565118 40.58951 \nL 163.616509 40.601077 \nL 163.744986 40.587196 \nL 163.822073 40.392873 \nL 163.847768 40.584883 \nL 164.104724 40.420634 \nL 164.207506 41.487097 \nL 164.233202 41.30434 \nL 164.258897 41.498663 \nL 164.361679 40.427574 \nL 164.387375 40.628837 \nL 164.464461 40.621897 \nL 164.567244 40.818533 \nL 164.592939 40.811593 \nL 164.721417 40.795399 \nL 165.004068 39.289396 \nL 165.081154 40.554809 \nL 165.132545 40.38362 \nL 165.183936 41.454709 \nL 165.261023 41.445456 \nL 165.33811 41.413069 \nL 165.389501 42.477218 \nL 165.466587 42.266702 \nL 165.492283 42.077005 \nL 165.569369 43.148095 \nL 165.620761 43.131901 \nL 165.774934 43.101828 \nL 165.903411 42.912131 \nL 165.954803 42.919071 \nL 165.980498 43.115708 \nL 166.108976 42.035364 \nL 166.186062 42.046931 \nL 166.468713 43.50204 \nL 166.5458 43.485846 \nL 166.571495 44.547683 \nL 166.674278 44.536116 \nL 166.725669 44.533802 \nL 166.77706 44.920135 \nL 166.879842 44.883121 \nL 166.905537 44.887748 \nL 167.085406 45.449897 \nL 167.136797 45.440643 \nL 167.29097 45.03349 \nL 167.368057 45.04043 \nL 167.625012 43.788897 \nL 167.650708 43.786584 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170.451521 42.951458 \nL 170.477217 42.949145 \nL 170.528608 43.818971 \nL 170.605695 43.793524 \nL 170.63139 43.793524 \nL 170.657086 43.992474 \nL 170.708477 43.587634 \nL 170.785563 43.585321 \nL 170.914041 43.400252 \nL 171.170997 43.407192 \nL 171.299474 44.512982 \nL 171.32517 44.512982 \nL 171.350865 44.709618 \nL 171.376561 44.693425 \nL 171.505039 43.439579 \nL 171.607821 43.242943 \nL 171.633516 43.247569 \nL 171.736298 43.263763 \nL 171.813385 43.453459 \nL 171.83908 43.444206 \nL 171.890472 43.05556 \nL 171.941863 43.245256 \nL 171.993254 43.441892 \nL 172.044645 43.428012 \nL 172.121731 43.231376 \nL 172.173122 43.249883 \nL 172.250209 43.263763 \nL 172.404382 44.704992 \nL 172.430078 43.657036 \nL 172.507164 44.029488 \nL 172.661338 44.022548 \nL 172.738424 43.821285 \nL 172.76412 44.008668 \nL 172.789815 44.006354 \nL 172.99538 44.82066 \nL 173.072466 44.809093 \nL 173.200944 44.617084 \nL 173.252335 44.61477 \nL 173.406508 45.001103 \nL 173.534986 44.994163 \nL 173.560681 45.188486 \nL 173.586377 44.987223 \nL 173.612073 44.989536 \nL 173.74055 43.923073 \nL 173.766246 43.9277 \nL 173.920419 43.552934 \nL 173.946115 43.557561 \nL 174.023201 43.772704 \nL 174.074592 43.765764 \nL 174.20307 43.786584 \nL 174.331548 43.409505 \nL 174.357243 43.418759 \nL 174.382939 43.418759 \nL 174.511416 43.198989 \nL 174.66559 43.178168 \nL 174.742676 42.976905 \nL 174.768372 42.986159 \nL 174.794067 44.057248 \nL 174.871154 43.874492 \nL 174.896849 43.677856 \nL 174.94824 43.888372 \nL 174.973936 43.888372 \nL 175.153805 43.913819 \nL 175.230891 42.83579 \nL 175.282282 42.865863 \nL 175.307978 42.868177 \nL 175.436456 43.74263 \nL 175.564933 43.777331 \nL 175.719107 44.149783 \nL 175.770498 44.15441 \nL 175.796193 44.346419 \nL 175.847584 43.9624 \nL 175.976062 43.987847 \nL 176.078844 46.109206 \nL 176.130235 45.928763 \nL 176.233017 45.935703 \nL 176.361495 45.547057 \nL 176.387191 45.563251 \nL 176.438582 45.579445 \nL 176.464277 45.378182 \nL 176.541364 45.408255 \nL 176.721233 45.963464 \nL 176.798319 45.956524 \nL 176.952492 46.361363 \nL 177.08097 46.155474 \nL 177.106666 46.148534 \nL 177.209448 45.093637 \nL 177.235143 45.28102 \nL 177.260839 45.28796 \nL 177.415012 45.695113 \nL 177.569185 45.702053 \nL 177.646272 46.104579 \nL 177.697663 45.914883 \nL 177.877532 45.935703 \nL 178.031705 45.584071 \nL 178.0574 45.593325 \nL 178.134487 45.963464 \nL 178.211574 45.94727 \nL 178.314356 45.942643 \nL 178.468529 46.335916 \nL 178.51992 46.338229 \nL 178.648398 46.548746 \nL 178.674093 46.560313 \nL 178.699789 45.503103 \nL 178.802571 45.70668 \nL 178.931049 45.882496 \nL 178.98244 45.889436 \nL 179.110917 46.081445 \nL 179.239395 46.037492 \nL 179.367873 46.419197 \nL 179.547742 45.868616 \nL 179.779001 45.850109 \nL 179.856088 45.475343 \nL 179.984566 46.726876 \nL 180.113043 46.736129 \nL 180.215826 46.162414 \nL 180.241521 46.176294 \nL 180.369999 47.268204 \nL 180.42139 47.256637 \nL 180.447085 47.446333 \nL 180.524172 47.27283 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188.54118 44.142844 \nL 188.618266 44.142844 \nL 188.746744 44.401941 \nL 188.849526 44.408881 \nL 188.900917 44.619397 \nL 188.952308 44.425074 \nL 188.978004 44.418134 \nL 189.029395 44.610144 \nL 189.080786 44.427388 \nL 189.209264 44.237691 \nL 189.440523 44.237691 \nL 189.51761 44.443581 \nL 189.543306 44.256198 \nL 189.800261 44.283959 \nL 189.928739 43.215183 \nL 190.082912 42.861237 \nL 190.159998 42.870491 \nL 190.314172 42.479532 \nL 190.468345 42.484158 \nL 190.571127 42.282895 \nL 190.596823 42.299089 \nL 190.699605 42.326849 \nL 190.7253 42.137153 \nL 190.776691 40.876367 \nL 190.853778 40.880994 \nL 191.033647 40.867114 \nL 191.085038 39.805278 \nL 191.162124 40.018108 \nL 191.18782 40.011167 \nL 191.213515 38.960898 \nL 191.316298 39.182981 \nL 191.573253 39.178355 \nL 191.65034 39.173728 \nL 191.778817 38.979405 \nL 191.881599 38.965525 \nL 192.061468 37.55437 \nL 192.16425 37.579817 \nL 192.241337 37.586757 \nL 192.369815 36.342165 \nL 192.421206 36.353732 \nL 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199.102045 35.460771 \nL 199.127741 35.470024 \nL 199.256219 35.668974 \nL 199.513174 35.666661 \nL 199.590261 35.456144 \nL 199.615956 35.655094 \nL 199.744434 36.05762 \nL 199.770129 36.05068 \nL 199.795825 36.05762 \nL 200.181258 38.285394 \nL 200.335431 38.327034 \nL 200.463909 38.720307 \nL 200.566691 37.639964 \nL 200.592386 37.646904 \nL 200.695169 37.434074 \nL 200.720864 37.441014 \nL 201.003515 37.415567 \nL 201.080602 36.534174 \nL 201.131993 36.735437 \nL 201.157688 36.73775 \nL 201.26047 36.941327 \nL 201.414644 36.564247 \nL 201.620208 36.5388 \nL 201.671599 36.735437 \nL 201.800077 34.641838 \nL 201.825772 34.836161 \nL 202.082728 34.801461 \nL 202.288292 35.564872 \nL 202.313987 35.569499 \nL 202.442465 35.379803 \nL 202.673725 35.377489 \nL 202.904985 35.43995 \nL 203.084854 34.609451 \nL 203.213331 34.826908 \nL 203.264722 34.815341 \nL 203.3932 34.417441 \nL 203.418896 34.422068 \nL 203.573069 34.459082 \nL 203.598764 34.653405 \nL 203.701546 34.630271 \nL 203.804329 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35.819342 \nL 229.397083 37.075501 \nL 229.551257 36.864984 \nL 229.679734 36.834911 \nL 229.782517 36.619767 \nL 229.988081 37.408626 \nL 230.116559 37.390119 \nL 230.219341 36.994533 \nL 230.245036 37.172663 \nL 230.270732 37.174976 \nL 230.347818 37.561309 \nL 230.424905 37.524295 \nL 230.501992 37.494221 \nL 230.604774 37.249004 \nL 230.630469 37.255944 \nL 230.656165 37.260571 \nL 230.68186 37.443327 \nL 230.733251 37.237437 \nL 230.758947 37.249004 \nL 231.067293 37.253631 \nL 231.092989 37.447954 \nL 231.170076 37.237437 \nL 231.221467 37.228184 \nL 231.272858 37.022294 \nL 231.401335 38.093383 \nL 231.555509 38.065623 \nL 231.683986 38.278453 \nL 231.735377 38.257632 \nL 231.863855 38.458896 \nL 232.12081 38.405688 \nL 232.146506 38.590758 \nL 232.197897 38.382555 \nL 232.223593 38.380241 \nL 232.35207 38.368674 \nL 232.506243 38.75732 \nL 232.531939 38.748067 \nL 232.737503 39.509165 \nL 232.788894 39.300962 \nL 232.840285 39.486031 \nL 232.865981 39.486031 \nL 232.917372 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40.300337 \nL 236.797398 40.29571 \nL 236.874485 40.478466 \nL 236.90018 40.47384 \nL 237.054354 40.08288 \nL 237.105745 40.073627 \nL 237.234222 39.853857 \nL 237.337004 39.851543 \nL 237.465482 40.25407 \nL 237.491178 40.249443 \nL 237.696742 40.235563 \nL 237.748133 40.251756 \nL 237.799524 39.194547 \nL 237.876611 39.233874 \nL 237.953697 40.459959 \nL 238.005088 40.457646 \nL 238.159262 40.066686 \nL 238.210653 40.087507 \nL 238.287739 39.874677 \nL 238.364826 41.121583 \nL 238.416217 41.105389 \nL 238.596086 41.079942 \nL 238.698868 41.281205 \nL 238.724563 41.274265 \nL 238.981519 41.417694 \nL 239.084301 41.214117 \nL 239.109996 41.218744 \nL 239.161388 41.214117 \nL 239.187083 41.005914 \nL 239.289865 41.024421 \nL 239.546821 40.820845 \nL 239.649603 40.436825 \nL 239.700994 40.457646 \nL 239.726689 40.453019 \nL 239.855167 40.850918 \nL 239.957949 40.661222 \nL 239.983645 40.668162 \nL 240.112122 40.686669 \nL 240.214905 40.28877 \nL 240.266296 40.304963 \nL 240.369078 40.328097 \nL 240.394773 39.273201 \nL 240.497555 39.472151 \nL 240.677424 40.066686 \nL 240.70312 40.057433 \nL 240.831597 40.443765 \nL 240.857293 40.434512 \nL 240.882989 40.450706 \nL 240.908684 40.645028 \nL 240.93438 39.587819 \nL 241.037162 39.784455 \nL 241.062857 39.784455 \nL 241.217031 39.412003 \nL 241.242726 39.418943 \nL 241.319813 39.64334 \nL 241.345508 38.581504 \nL 241.422595 39.678041 \nL 241.499681 39.88393 \nL 241.525377 39.675727 \nL 241.833723 39.495285 \nL 241.936506 38.239125 \nL 241.962201 38.431135 \nL 242.14207 38.438075 \nL 242.476112 36.649841 \nL 242.527503 36.679915 \nL 242.707372 36.106199 \nL 242.784458 36.115453 \nL 242.912936 36.506412 \nL 242.938632 36.312089 \nL 242.990023 36.538799 \nL 243.015718 36.536486 \nL 243.169891 36.55268 \nL 243.298369 36.756256 \nL 243.375456 36.742376 \nL 243.606715 38.012415 \nL 243.658107 37.996222 \nL 243.709498 38.014728 \nL 243.786584 37.820406 \nL 243.81228 37.998535 \nL 243.889366 38.014728 \nL 243.966453 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37.672349 \nL 247.846479 37.672349 \nL 247.923566 37.471086 \nL 247.949261 37.658469 \nL 248.000652 37.656156 \nL 248.077739 37.850479 \nL 248.103434 37.845852 \nL 248.12913 37.665409 \nL 248.180521 38.063309 \nL 248.231912 38.081815 \nL 248.36039 38.963209 \nL 248.411781 38.979402 \nL 248.540259 37.739437 \nL 248.822909 36.353729 \nL 248.848605 36.552678 \nL 248.925692 36.346789 \nL 248.951387 36.145526 \nL 249.028474 36.374549 \nL 249.10556 36.409249 \nL 249.234038 37.501159 \nL 249.285429 37.135647 \nL 249.645167 37.174974 \nL 249.696558 36.976025 \nL 249.773644 37.001472 \nL 249.876427 37.013038 \nL 249.902122 35.960456 \nL 250.004904 35.99747 \nL 250.133382 35.999783 \nL 250.287555 38.336285 \nL 250.338946 38.141962 \nL 250.390337 38.327032 \nL 250.441728 38.336285 \nL 250.570206 36.922817 \nL 250.724379 37.114827 \nL 250.852857 37.119453 \nL 250.981335 37.355417 \nL 251.135508 36.996845 \nL 251.263986 37.232808 \nL 251.289681 37.232808 \nL 251.418159 37.054679 \nL 251.546636 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33.852976 \nL 256.377397 34.084313 \nL 256.428788 33.90387 \nL 256.48018 33.922377 \nL 256.505875 34.114386 \nL 256.582962 33.91081 \nL 256.634353 33.90387 \nL 256.76283 34.10976 \nL 256.788526 33.929317 \nL 256.891308 33.970957 \nL 256.968395 33.989464 \nL 257.096872 32.925315 \nL 257.430914 32.814273 \nL 257.45661 32.61995 \nL 257.559392 32.638457 \nL 257.585088 32.636143 \nL 257.713565 34.102819 \nL 257.816347 34.128266 \nL 257.842043 33.94551 \nL 257.944825 33.961703 \nL 257.970521 33.970957 \nL 258.047607 34.169907 \nL 258.073303 33.977897 \nL 258.098998 33.994091 \nL 258.150389 33.797454 \nL 258.201781 33.994091 \nL 258.227476 33.987151 \nL 258.458736 33.05255 \nL 258.484431 33.054863 \nL 258.535822 32.8536 \nL 258.612909 32.88136 \nL 258.638605 32.885987 \nL 258.6643 32.687037 \nL 258.715691 33.751187 \nL 258.792778 33.566117 \nL 258.921256 33.566117 \nL 258.998342 33.186725 \nL 259.075429 33.209858 \nL 259.101124 33.207545 \nL 259.255298 33.623951 \nL 259.280993 33.420375 \nL 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283.820231 35.789256 \nL 283.871622 35.786943 \nL 284.025795 36.346778 \nL 284.051491 36.339838 \nL 284.179968 36.330585 \nL 284.23136 36.536474 \nL 284.308446 36.494834 \nL 284.359837 36.492521 \nL 284.488315 35.412177 \nL 284.668184 35.983579 \nL 284.770966 35.965073 \nL 284.796661 36.152455 \nL 284.873748 35.932686 \nL 284.899444 35.925745 \nL 285.027921 35.717542 \nL 285.182094 35.673589 \nL 285.233485 36.055294 \nL 285.336268 36.029848 \nL 285.361963 36.020594 \nL 285.43905 36.386106 \nL 285.516136 36.369913 \nL 285.824483 36.307452 \nL 285.978656 36.675278 \nL 286.235611 36.654458 \nL 286.261307 36.654458 \nL 286.389785 36.446254 \nL 286.466871 36.443941 \nL 286.569653 36.649831 \nL 286.595349 36.633637 \nL 286.64674 36.642891 \nL 286.775218 38.056359 \nL 286.903695 38.012405 \nL 287.032173 38.197475 \nL 287.057869 38.195161 \nL 287.160651 37.131012 \nL 287.186346 37.309141 \nL 287.314824 37.484957 \nL 287.520388 37.480331 \nL 287.597475 37.47339 \nL 287.674562 37.276754 \nL 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291.914325 35.622696 \nL 291.965716 35.613443 \nL 292.042803 35.594936 \nL 292.145585 34.523847 \nL 292.17128 34.528473 \nL 292.453931 34.521533 \nL 292.505322 34.507653 \nL 292.582409 34.685783 \nL 292.6338 34.685783 \nL 292.685191 34.493773 \nL 292.710887 34.50534 \nL 292.839364 35.928061 \nL 292.967842 35.941942 \nL 292.993538 36.999151 \nL 293.070624 36.790948 \nL 293.199102 36.573491 \nL 293.378971 36.557298 \nL 293.404666 36.561925 \nL 293.507448 35.490835 \nL 293.533144 35.493148 \nL 293.584535 35.483895 \nL 293.610231 36.534164 \nL 293.713013 36.362975 \nL 293.944273 36.298201 \nL 294.021359 35.227111 \nL 294.07275 35.243305 \nL 294.252619 35.234052 \nL 294.30401 35.238678 \nL 294.432488 33.973266 \nL 294.458183 33.970952 \nL 294.53527 33.783569 \nL 294.586661 33.795136 \nL 294.715139 33.785883 \nL 294.817921 34.165275 \nL 294.869312 34.135202 \nL 295.049181 34.137515 \nL 295.126267 33.057172 \nL 295.177658 33.059485 \nL 295.203354 33.047919 \nL 295.331832 34.311018 \nL 295.434614 34.521534 \nL 295.537396 34.341092 \nL 295.563091 34.336465 \nL 295.614482 34.516908 \nL 295.691569 34.486834 \nL 295.871438 34.484521 \nL 295.948524 35.699039 \nL 296.128393 35.650459 \nL 296.231175 35.622698 \nL 296.333957 35.830901 \nL 296.359653 35.817021 \nL 296.411044 35.810081 \nL 296.488131 36.18022 \nL 296.539522 36.157087 \nL 296.642304 35.92575 \nL 296.847868 36.728489 \nL 296.95065 36.730802 \nL 297.079128 35.650459 \nL 297.104824 35.648146 \nL 297.130519 35.835529 \nL 297.233301 35.810082 \nL 297.41317 35.768441 \nL 297.438866 35.967391 \nL 297.515952 35.951197 \nL 297.541648 35.745308 \nL 297.618734 35.916497 \nL 298.081254 35.89105 \nL 298.10695 35.708294 \nL 298.209732 35.733741 \nL 298.415296 35.719861 \nL 298.543774 35.305768 \nL 298.569469 35.310395 \nL 298.595165 35.310395 \nL 298.697947 35.476957 \nL 298.723642 35.284948 \nL 298.775034 34.218485 \nL 298.85212 34.415121 \nL 298.903511 34.422062 \nL 299.031989 34.209232 \nL 299.186162 34.209232 \nL 299.468813 35.657401 \nL 299.571595 35.64352 \nL 299.725768 36.02754 \nL 299.751464 36.022913 \nL 299.879942 35.796203 \nL 300.162593 35.812397 \nL 300.188288 35.812397 \nL 300.342461 36.138582 \nL 300.470939 36.136268 \nL 300.599417 35.060552 \nL 300.676503 35.039732 \nL 300.804981 33.785886 \nL 300.933459 33.799766 \nL 300.959154 33.987149 \nL 301.036241 33.80208 \nL 301.061936 33.799767 \nL 301.190414 33.59619 \nL 301.21611 33.607757 \nL 301.344587 33.79514 \nL 301.447369 33.78126 \nL 301.473065 33.598504 \nL 301.550152 33.792826 \nL 301.627238 33.790513 \nL 301.704325 33.977896 \nL 301.755716 33.966329 \nL 301.781411 33.957076 \nL 301.909889 33.746559 \nL 302.115453 33.78126 \nL 302.269627 33.38336 \nL 302.4238 33.811333 \nL 302.526582 33.799767 \nL 302.552277 33.998716 \nL 302.65506 33.970956 \nL 302.732146 33.957076 \nL 302.860624 33.549923 \nL 302.886319 33.55455 \nL 302.937711 33.559176 \nL 303.040493 33.940882 \nL 303.091884 33.917749 \nL 303.143275 31.801016 \nL 303.220361 32.187349 \nL 303.297448 32.178095 \nL 303.451621 31.764002 \nL 303.554403 31.764002 \nL 303.63149 30.706793 \nL 303.682881 30.71836 \nL 303.86275 30.692912 \nL 303.888445 30.503216 \nL 303.939836 30.873355 \nL 303.965532 31.069992 \nL 304.016923 30.894176 \nL 304.145401 30.683659 \nL 304.248183 30.665152 \nL 304.299574 30.459263 \nL 304.376661 30.477769 \nL 304.402356 30.461576 \nL 304.428052 30.644332 \nL 304.556529 29.563989 \nL 304.710703 29.191537 \nL 304.864876 29.177657 \nL 304.916267 28.985647 \nL 304.967658 29.008781 \nL 305.173222 29.781446 \nL 305.3017 29.769879 \nL 305.327395 29.573242 \nL 305.404482 29.758312 \nL 305.558655 29.779132 \nL 305.584351 29.966515 \nL 305.635742 29.746745 \nL 305.687133 29.929501 \nL 305.712829 28.872292 \nL 305.815611 29.045794 \nL 305.892697 29.041168 \nL 305.918393 29.242431 \nL 305.995479 29.055048 \nL 306.123957 28.835278 \nL 306.457999 29.011094 \nL 306.560781 28.643268 \nL 306.612172 28.652522 \nL 306.689259 28.668715 \nL 306.843432 29.927188 \nL 306.946214 29.721298 \nL 307.048996 29.749059 \nL 307.20317 30.121511 \nL 307.305952 30.297327 \nL 307.43443 30.496277 \nL 307.485821 30.510157 \nL 307.588603 30.702167 \nL 307.614298 30.514784 \nL 307.691385 30.713734 \nL 307.71708 30.926563 \nL 307.819863 30.88261 \nL 307.896949 30.868729 \nL 307.974036 31.037605 \nL 308.025427 31.009845 \nL 308.1796 31.012159 \nL 308.205296 30.834029 \nL 308.230991 30.840969 \nL 308.359469 32.067055 \nL 308.590729 32.055488 \nL 308.667815 33.140458 \nL 308.719206 33.138145 \nL 308.770597 33.138145 \nL 308.87338 32.929941 \nL 308.899075 32.934568 \nL 308.924771 32.939195 \nL 309.027553 32.751812 \nL 309.181726 33.320901 \nL 309.207422 33.316274 \nL 309.233117 33.126578 \nL 309.310204 33.295453 \nL 309.38729 33.688726 \nL 309.490072 32.439507 \nL 309.515768 32.453387 \nL 309.541464 32.453387 \nL 309.644246 32.268318 \nL 309.669941 32.284511 \nL 309.875506 32.298391 \nL 309.901201 32.106382 \nL 309.926897 32.111008 \nL 310.055374 33.420375 \nL 310.183852 33.431942 \nL 310.363721 32.86748 \nL 310.569285 32.892927 \nL 310.697763 33.082623 \nL 310.723458 32.899867 \nL 310.800545 33.112697 \nL 310.877631 33.119637 \nL 310.929023 32.735617 \nL 311.031805 32.749498 \nL 311.108891 32.737931 \nL 311.263065 32.360852 \nL 311.391542 32.337718 \nL 311.442933 32.131828 \nL 311.52002 32.164215 \nL 311.622802 32.175782 \nL 311.648498 31.965266 \nL 311.725584 32.194289 \nL 311.879757 32.203543 \nL 312.008235 32.397866 \nL 312.162408 32.407119 \nL 312.26519 32.201229 \nL 312.342277 32.402492 \nL 312.470755 31.194914 \nL 312.599232 31.231928 \nL 312.72771 31.030665 \nL 312.856188 31.028351 \nL 313.010361 30.653586 \nL 313.113143 30.660526 \nL 313.138839 30.854849 \nL 313.241621 30.827088 \nL 313.293012 30.829402 \nL 313.447185 31.187974 \nL 313.498576 31.208794 \nL 313.627054 31.761689 \nL 313.678445 31.754749 \nL 313.755532 31.764003 \nL 313.806923 31.953699 \nL 313.832618 31.766316 \nL 313.909705 31.578933 \nL 313.961096 31.592813 \nL 314.038183 31.599754 \nL 314.218051 31.012158 \nL 314.295138 31.012158 \nL 314.39792 31.396177 \nL 314.449311 31.382297 \nL 314.500702 31.386924 \nL 314.654875 32.474207 \nL 314.757658 32.451073 \nL 314.834744 32.464954 \nL 314.988917 31.856538 \nL 315.014613 31.872731 \nL 315.040308 31.865791 \nL 315.168786 31.678409 \nL 315.477133 31.659902 \nL 315.528524 31.460952 \nL 315.60561 31.497966 \nL 315.759784 31.488712 \nL 315.862566 31.703856 \nL 315.965348 31.502593 \nL 316.042434 31.703856 \nL 316.093825 31.678409 \nL 316.196608 31.479459 \nL 316.222303 31.491026 \nL 316.273694 31.500279 \nL 316.402172 31.703856 \nL 316.53065 31.708482 \nL 316.607736 32.113322 \nL 316.684823 32.099442 \nL 316.736214 32.080935 \nL 316.761909 31.879672 \nL 316.813301 32.249811 \nL 316.890387 32.254437 \nL 317.070256 31.673782 \nL 317.250125 31.625201 \nL 317.27582 31.627515 \nL 317.404298 31.817211 \nL 317.686949 31.590501 \nL 317.712644 31.78945 \nL 317.815426 31.759377 \nL 317.841122 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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Train agent\n",
    "model.learn(total_timesteps=100000)\n",
    "\n",
    "# Plot cumulative reward\n",
    "with open(os.path.join(log_dir, \"monitor.csv\"), 'rt') as fh:    \n",
    "    firstline = fh.readline()\n",
    "    assert firstline[0] == '#'\n",
    "    df = pd.read_csv(fh, index_col=None)['r']\n",
    "df.rolling(window=1000).mean().plot()\n",
    "plt.show()\n",
    "model.save(\"connect_four_agent\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}