Noisy_DQN.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import gym\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "from utils.replay_buffer import ReplayBuffer\n",
    "from utils.common import NoisyDense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MyModel(tf.keras.Model):\n",
    "    def __init__(self, output_dim=2, hidden=16, activation=\"elu\", name=\"dqn\", **kwargs):\n",
    "        super().__init__(self, name=name, **kwargs)\n",
    "        self.bn = tf.keras.layers.BatchNormalization(axis=-1)\n",
    "        self.dense1 = tf.keras.layers.Dense(hidden, \n",
    "                                            activation=activation, \n",
    "                                            use_bias=True,\n",
    "                                            kernel_initializer=\"lecun_normal\", \n",
    "                                            bias_initializer=\"lecun_uniform\")\n",
    "        self.dense2 = NoisyDense(hidden, \n",
    "                                 activation=activation, \n",
    "                                 use_bias=True)\n",
    "        self.out = NoisyDense(output_dim, \n",
    "                              activation=activation, \n",
    "                              use_bias=True)\n",
    "    \n",
    "    def call(self, inputs):\n",
    "        x = inputs\n",
    "        x = self.bn(x)\n",
    "        x = self.dense1(x)\n",
    "        x = self.dense2(x)\n",
    "        return self.out(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DQN:\n",
    "    def __init__(self, \n",
    "                 env=None, \n",
    "                 buffersize=10000,\n",
    "                 gamma=0.99,\n",
    "                 batch_size=32, \n",
    "                 lr=2e-3,\n",
    "                 update_target_freq=100,\n",
    "                 seed=None\n",
    "                 ):\n",
    "        self.env = env\n",
    "        self.buffer = ReplayBuffer(maxsize=buffersize)\n",
    "        self.model = MyModel(output_dim=2, activation=\"selu\", hidden=32)\n",
    "        self.model_target = MyModel(output_dim=2, activation=\"selu\", hidden=32)\n",
    "        # self.model_target.build(input_shape=(batch_size, 4))\n",
    "\n",
    "        self.epsilon_func = lambda x: max(epsilon_min, \n",
    "                            epsilon_start * epsilon_decay ** x)\n",
    "        self.gamma = gamma\n",
    "\n",
    "        self.batch_size = batch_size\n",
    "        self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)\n",
    "\n",
    "        self.update_target_freq = update_target_freq\n",
    "        self.istraining = True\n",
    "\n",
    "        if seed:\n",
    "            tf.keras.backend.clear_session()\n",
    "            self.env.seed(seed)\n",
    "            random.seed(seed)\n",
    "            np.random.seed(seed)\n",
    "            tf.random.set_seed(seed)\n",
    "\n",
    "    def act(self, state):\n",
    "        Q_values = self.model(state[np.newaxis])\n",
    "        return np.argmax(Q_values[0])\n",
    "\n",
    "    def play(self, episodes):\n",
    "        training_count = 0\n",
    "        self.steps_per_round = []\n",
    "        self.rewards_per_round = []\n",
    "        self.losses = []\n",
    "        for i in range(1, episodes + 1):\n",
    "            state = self.env.reset()\n",
    "            step_count, reward_total = 0,  0\n",
    "            done = False\n",
    "            while not done:\n",
    "                action = self.act(state)\n",
    "                next_state, reward, done, _ = self.env.step(action)\n",
    "\n",
    "                self.buffer.push(state, action, reward, next_state, done)\n",
    "                state = next_state if not done else self.env.reset()\n",
    "\n",
    "                step_count += 1\n",
    "                reward_total += reward\n",
    "                print(\"\\rEpisode {}: steps--{}, reward--{}\".format(i, step_count, reward_total), end=\"\")\n",
    "                if done:\n",
    "                    self.steps_per_round.append(step_count)\n",
    "                    self.rewards_per_round.append(reward_total)\n",
    "                    step_count, reward_total = 0, 0\n",
    "                    \n",
    "            if i >= 50:\n",
    "                if i == 50:\n",
    "                    print(\"Training start!\")\n",
    "                samples = self.buffer.get(self.batch_size)\n",
    "                loss = self.train_on_batch(*samples).numpy()\n",
    "                self.losses.append(loss)\n",
    "                training_count += 1\n",
    "                if training_count > 0 and training_count % self.update_target_freq == 0:\n",
    "                    self.model_target.set_weights(self.model.get_weights())\n",
    "        plt.plot(range(len(self.rewards_per_round)), self.rewards_per_round)\n",
    "        plt.show()\n",
    "        plt.plot(range(len(self.losses)), self.losses)\n",
    "        plt.show()\n",
    "\n",
    "    @tf.function\n",
    "    def train_on_batch(self, states, actions, rewards, next_states, dones):\n",
    "        actions = tf.cast(actions, dtype=tf.uint8)\n",
    "        next_Q_values = self.model_target(next_states)        \n",
    "        target_Q_values = rewards + (1 - dones) * self.gamma * tf.reduce_max(next_Q_values, axis=-1, keepdims=True)\n",
    "\n",
    "        mask = tf.squeeze(tf.one_hot(actions, depth=2, dtype=tf.float32))\n",
    "        with tf.GradientTape() as tape:\n",
    "            Q_values_ = self.model(states)\n",
    "            Q_values = tf.reduce_sum(Q_values_ * mask, axis=1, keepdims=True)\n",
    "            loss = tf.reduce_mean(tf.keras.losses.MSE(target_Q_values, Q_values))\n",
    "        gradients = tape.gradient(loss, self.model.trainable_variables)\n",
    "        self.optimizer.apply_gradients(zip(gradients, self.model.trainable_variables))\n",
    "        return tf.reduce_mean(loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "env = gym.make(\"CartPole-v1\")\n",
    "# \n",
    "# Observation:\n",
    "#     Type: Box(4)\n",
    "#     Num     Observation               Min                     Max\n",
    "#     0       Cart Position             -4.8                    4.8\n",
    "#     1       Cart Velocity             -Inf                    Inf\n",
    "#     2       Pole Angle                -0.418 rad (-24 deg)    0.418 rad (24 deg)\n",
    "#     3       Pole Angular Velocity     -Inf                    Inf\n",
    "# Actions:\n",
    "#     Type: Discrete(2)\n",
    "#     Num   Action\n",
    "#     0     Push cart to the left\n",
    "#     1     Push cart to the right\n",
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "WARNING:tensorflow:Layer dqn is casting an input tensor from dtype float64 to the layer&#39;s dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because its dtype defaults to floatx.\n\nIf you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n\nTo change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx(&#39;float64&#39;)`. To change just this layer, pass dtype=&#39;float64&#39; to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n\nEpisode 50: steps--60, reward--60.0Training start!\nEpisode 1000: steps--32, reward--32.0"
    },
    {
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128.938114 209.533304 \r\nL 129.242783 205.917339 \r\nL 129.547451 211.140399 \r\nL 129.852119 207.122661 \r\nL 130.156787 191.855255 \r\nL 130.461456 207.122661 \r\nL 130.766124 197.881863 \r\nL 131.070792 200.292506 \r\nL 131.375461 212.747494 \r\nL 131.680129 213.149268 \r\nL 131.984797 213.952816 \r\nL 132.289466 213.149268 \r\nL 132.594134 214.756364 \r\nL 132.898802 211.943947 \r\nL 133.203471 211.140399 \r\nL 133.508139 211.943947 \r\nL 133.812807 213.551042 \r\nL 134.117475 212.747494 \r\nL 134.422144 213.551042 \r\nL 134.726812 208.729756 \r\nL 135.336149 208.729756 \r\nL 135.640817 207.926208 \r\nL 135.945485 212.747494 \r\nL 136.250154 208.729756 \r\nL 136.554822 209.13153 \r\nL 136.85949 201.899601 \r\nL 137.164158 205.917339 \r\nL 137.468827 202.703149 \r\nL 137.773495 210.738625 \r\nL 138.078163 214.35459 \r\nL 138.382832 213.551042 \r\nL 138.6875 213.952816 \r\nL 138.992168 212.747494 \r\nL 139.296837 214.35459 \r\nL 139.906173 213.551042 \r\nL 140.210842 214.35459 \r\nL 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199.011824 213.149268 \r\nL 199.316493 210.336851 \r\nL 199.621161 210.738625 \r\nL 199.925829 182.212683 \r\nL 200.230498 206.319113 \r\nL 200.535166 189.042838 \r\nL 200.839834 208.729756 \r\nL 201.144502 205.113792 \r\nL 201.449171 209.935078 \r\nL 201.753839 206.720887 \r\nL 202.058507 201.096053 \r\nL 202.363176 207.926208 \r\nL 202.667844 211.943947 \r\nL 202.972512 213.551042 \r\nL 203.581849 211.140399 \r\nL 203.886517 211.140399 \r\nL 204.191186 213.149268 \r\nL 204.495854 213.149268 \r\nL 204.800522 212.345721 \r\nL 205.10519 214.35459 \r\nL 205.409859 210.738625 \r\nL 205.714527 213.551042 \r\nL 206.019195 210.738625 \r\nL 206.323864 209.13153 \r\nL 206.628532 200.292506 \r\nL 206.9332 199.890732 \r\nL 207.237869 181.810909 \r\nL 207.542537 192.658803 \r\nL 207.847205 194.667672 \r\nL 208.456542 206.720887 \r\nL 208.76121 202.703149 \r\nL 209.065878 207.524435 \r\nL 209.370547 208.729756 \r\nL 209.675215 205.917339 \r\nL 209.979883 209.935078 \r\nL 210.284552 206.720887 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196.676541 \r\nL 222.166615 195.069446 \r\nL 222.471284 206.319113 \r\nL 222.775952 204.712018 \r\nL 223.08062 207.524435 \r\nL 223.385289 202.301375 \r\nL 223.689957 210.336851 \r\nL 223.994625 205.515565 \r\nL 224.299294 211.140399 \r\nL 224.603962 212.345721 \r\nL 224.90863 211.140399 \r\nL 225.213299 213.149268 \r\nL 225.517967 211.140399 \r\nL 225.822635 211.140399 \r\nL 226.127303 209.13153 \r\nL 226.431972 198.283636 \r\nL 226.73664 212.345721 \r\nL 227.041308 197.881863 \r\nL 227.345977 189.846386 \r\nL 227.650645 174.57898 \r\nL 227.955313 206.319113 \r\nL 228.259982 149.669002 \r\nL 228.56465 187.033969 \r\nL 228.869318 177.391397 \r\nL 229.173986 199.087184 \r\nL 229.478655 207.926208 \r\nL 229.783323 209.935078 \r\nL 230.087991 206.319113 \r\nL 230.39266 205.917339 \r\nL 230.697328 210.738625 \r\nL 231.001996 212.345721 \r\nL 231.916001 209.935078 \r\nL 232.22067 209.935078 \r\nL 232.525338 211.943947 \r\nL 232.830006 210.738625 \r\nL 233.134674 212.345721 \r\nL 233.439343 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9SCgg5r5QWNwZpsroFW6ZIpXrO9EGTHEneI70Dfa5KyNUPc4B2BtaaY/vgwK2FwqLO8NUGZUv7cWcrMPfcgcI8Rh5CqotWkZruRv/4zGvOPdw64DsVIBeln2hsLgzTG+g6nzu0dOVTONgRxKA1xyqwrTc/XzuOXSo+tjuFMIt4ywzahLBuzAMU3Zy8rkXX92FEMGTUPsWEF1dJFfd9RrW7T4MwCsvjLHeK+mX3Ce77Ndh6uWTl9uzNQhyu7DlzjBMKEphuRd6jmK8gKSwA17RMlmfu5eghplGT1r/GSFcLwCrP1U5v/DoMLU6VNnnzjDHMAUYyZVIOVxHQhihkDEK63PXD2KKEaFffQIZARztSdu3S7cMgi13jpZhGCY3t0wRhHOXmZXSOkeB5RVb3L1myCMy3DJe0TI2n7tHKGSMgMF96wAABxy5anSWu2copFlLjpZhGEY/r6eDYrg8Zt2/xH6OAgWp2Ia7rj9A9bl7uUKCQiEzwih7cGMtAGDf0W77OTRlBt0r9rkzDBPK4i2GIXjQkXO80gNyvCbaICIQEdKeHarZDYu3HHCl4xWm5d6/Tw0AoN2MznEeTyE6VNnnzjBMbqGQxatF9hyFdqiWwekuzxgPOYgJADp6Uo7txoQfMneMU5g1A1R93DL+2wslUNyJaAwRvUBEa4loNRHdZK4fREQLiGiD+X+gcsytRLSRiNYT0RVFqTnDHEvk5HMvQShkga+Qslj+Iutz946WMdZ/8/KTAbiFN2N2qMY8BiAJTZx70CCmYs2LG8ZyTwH4hhDiVAAXALiRiCYBuAXAQiHEBAALzc8wt80EMBnAdABziCiuLZlhmJwI53MvPoVb7tHUw4vahFvaBIxOTCK/rJDG/0TcON6puxkzFDLrUtEfT5p1TsqefkAI0SKEeMNcPgxgLYBRAGYAmGfuNg/AVebyDACPCCG6hRBbAGwEcF7E9WaYY5Jy+dyjp7iV7FfvHp8pY9TjPuJum0YP7laQ2inrtR2w+9w9WznmLhXRoUpEYwGcBWAxgOFCiBbAeAEAGGbuNgrAduWwHeY6Z1k3EFEzETW3trbmUfXCeeatXZjrOZciw/RSesMgpiLX0WuSJCnMQYOYpHg7rWohBGIxxS3j3O5TphOytpdZ3ImoL4A/AbhZCHHIb1fNOlfthRD3CCGmCCGmDB06NGw1IuXLf3gTs59bW5ZzM0w+lCsU0qk/le5z1wmqmvLXM/2AWbOauOwwdZdr+Nz158lOkO1e5ySbOMznQgoglLgTUQ0MYf+9EOIJc/UeIhppbh8JYK+5fgeAMcrhowHsiqa6DMME0RvcMkW33DWvDwEjFDJG3n5uYVnups/d1aFqhELGfNw2BqQco69jJUTLEID7AKwVQvyPsulpALPM5VkAnlLWzySiOiIaB2ACAPsICIZhikav6FAtci29Uu8S4DsTkxRzr1BHOYhJirsrFFITLRP0jZQzK+RFAK4BsJKIlpvrbgNwO4DHiOh6ANsAfBwAhBCriegxAGtgRNrcKIRIu0plGKYolCYUssDjhbpcYIZJbfk6yx2AnIkpwOee8HDLCIfl7nSp5BQtU+QO1UBxF0K8Cu8hFNM8jpkNYHYB9WKYqkMIge5UBvU1xY0MLoZUOMuMMv2A9IVHiVYvBaw5VIOm2Yt7Wu7GICbTa6Ox3A3U6/F2y+jPERU8QpVhSsSDi97BxO/NR0t7Z/DOBVAMrXD5lgssb++hrgJL8Eev7Wa63piPz938H/MYYOQcxOQZCqnYw0EuqGI1tFjcGaZEPL3CiCvYcTB3cZcC8eL6Vle+E699tduEwB8Wb0NnT26eUrflntPhLl7ftN+z7CjwStdLMHOx+2SFVN0uXoOYvNwyKTN/r81y9/i65PfEljvD9HJSGXuTPxfU3//cVwLGZvhoxUtvt+K2J1di9nNrcjq/SwwL1KOG2qxrqhh9BF4+d5l+IOXpczfcLnEvt4sMp/RI+vX9p1YDcKQf8LhZ8lC23Bmml5M2TbiaWO4/O1UA2juT3jvCX3c7TIt93+Een72Cyyw4zl3Nm15QSV7l689JINQlYp6tH+l2Ia9oGMvnrnfLSGxuGc+Yevv/qGFxZ5gSkTLzzOah7TYBCLL7hQCWbj2AOS9udG2TFmfO4uyKGsntcFdxQr+cL7p4dNc5YVjU9TVxdKXSHnOkGr6brM/dvj0t/BOHWYRIHJbdzm4ZhunVpK0Yav3P7rmVLThv9j+Q1AxZtIlVgLoLCHz87kW4Y/56zVZpcYaqsv78EZCxWe6Fl+2Vete5jgDUJWIQAujRDQ0VxgvQyy2TyQjEY94jVCUppWxvt4zwrGsUsLgzTImQ4u7lcv/+U6uw93C3a2IMwC4AFKDufmJBluWeGxG73KO33F0dnx47ElmhqN0a14z0uXu5ZVKZDBKxrFvG66XXv6EmsC6Wz71Ijpkwg5gYhokA2Ynn/VP2U97wAuC3p3wt5OoKcApQpU2zp/ON6z4TgDpT3LuSaTTV19j2c4Y6OstNZ4QjFNJej3PHDkQiFrONZfC6V5bPnS13hundyKa6l7XnN5BHtf6CBvz4CS95iFIQ7sRhhRE0EXWuOO+p0+WSTcUL1Ju53ruTestdpgU2Ptu3pzMCiXjWLePOGunuUwm6PO5QZZhejmW5B3Ww6dbloADqvjvb7DH1luUevjjt/pF2qEbic7d/dsbxy82ErFumK+mO9c9Ylnn2s0rK8rn7j2C1ndszSRnHuTNMVZAJ+DH7GeSqAOYSJT/9ly/bPkurMme3istyL0yQMkW23Dsdwt2dMj7XJmKoMy33Lo3l3plMo6Emnk3Hq3HLJGLebhmvVMM6ZNnslmGYXk4yHdJyDxCIYLdMdvlwl32CZ9kZm7vlHq1fRngs54vTwnaKe+vhbgDA0H51Soeq23LvTGbQUBtXZlqyb085omXcc6jCZbl7dajKWHt2yzCMD92pNH70lzWBA3zKSTrALZONZPGIvw5Jl0a0sieBbx28iNrnHnUiRGd5XT3e4i7nV9WFQnb2pFFfE/ccgZoOcMvI9AXOrJc65Mu+WKY7iztTFfz5zZ24/7Ut+NnfdLHdxWfvoS689Lb/dJFS3L3dMuE6O4NCIb/w0DKfY+FbBy+i97kL7XK+BLll9h0xwksHN9aixgxit8RVoSuZRp9axS2T0Yl7TAmFdNeDiHDuuEHKOn2dpeVepIy/LO6AJm8G0+uQX6FuAFAp+Mic1zHrfv85aZIho2UK7VDd3HrUc1u+edO9JqWIglK4ZaQLpr4mjlop7po4d+lz93LLSJ87ANNC10TLEPDhM47DnZ86y1jncYWyTjxCtYgUq7eaKR1ezehS4YxK0REU525Z1RpjQ72uQnKfZ+Pcczsu6tsafYeq8f9fzxqF6y8eh65kxvail1ZyXSKGmoRxF3SGQEeAW0b63AHDt+607OVMTQAwvF+dsdLj+qw+mJDXmCs8iAnFaxYxpSM78UF565HJCKvJ7kWQpRbYoZpPxUysKI+CE38VdHgon3QuSBE+f/wgq+y9h7sxakADgKyQ1sRjlltG53PvSqbRUBvXpvx9Y9tBrG05hHZzBHEs5p70w0hOZkAesfJOOFqmiLDlXgXk2VEYNWGeJc9cUx6ddEB01p20+r1yjIel8A7V4oRCEhGG968HAPzf89nEaT1KKGStj8+9J5VBbTymnQLvxXV7AQC72o2JRrzdMlm3DaB/karHcZx7EWFx7/1EZZEWitcMPypBu+gnmsghFtKH7CCmQi336Cz/KH5/b2xrA2A8BxNH9AMALN6cnRBEWum1iZjSoaofoRqPQfG5Z+sWdww91btlhDWWwHqRai5Pm7QsYljcAaxtOVzuKjAFkq8vOWrCnD9IzIIGwkQx3WiUbpVCj4/ClfbVh98EYGRzHNm/AeOHNuLUkU3W9qxbhlAT9/a5W3OkalwqcYda6uZizQihRDN553xXk5axW6aIfPTXr5e7CkyB5D3yMmLCzGQfGC2jdctE06EqHP/LhXo9UX5nUljrE3HbICUpprXxGGpknLs2K6Th2tHljnH2pRDpJ8iW30/WLeNGzWtTrNYmiztTFVRKh2oYt4zXb9mvGR+V/omI1L3SLHerXPPC6mtiNus460sny+euc40YuWWybr5Ne49Y2+KOt6rRoertc88madPH01vnZMu9uPzun++UuwpMAWSFsbzqLjSu1E2tRzD2lmetz0E/Zt01RCUAsuyCfe4R5paJ8juT7pe6RNxmHSfTGcsdY/ncU/qQ03gsm8/9Vws3WNucc9/GyC3uMquksd34r7s8dsuUkO/9eRX2HOoqdzWYPFj2zgHc9MhyAOV3N+gs9yVbDtg+B41Q1UfLCNd++SBLCXpZHO1O4cX1e73LKdRyV5ajFHc5lWFdTczmlulJZay0A1Kkf/GPt13Hy3zu9TVuadSLu30fm+Vufk/Xz2t2jV1Q68ZumRJw/n8txNyXA2aWZyqO//jTSmu53D53nVAlHKLgVUO/MMWIgmWUqd3879PNjy7HdQ8sxa4Qg7PyQdW6KL8yOQl5XULjlkkEy10mY1jeI/s3YOKIfjh5eF+lbHtFdaGQquWufk9HeuwJ3NhyLwOzn1vrygXNVDaqL7Tc0TK60aVOUQlK+RsUCllItIwsJug2rd7ZDgBoae/C8u1t7nIKqIOtIojYcs8obpmU3S2jzl376fOPx6DGWtfxGSGs5+mM0QOsRHQdPSn857NrAQB3fOx0APpQSCGyFrsq7s5MkarPvVgGCY9Q1XDLE2/hVzPPKnc1mJCoUQzlFnfVLZNMZzDhO3/F2ccPsO3j9WMuxSAm6QIIcsvI3CxekWSFCpJ6/ig7FC23TCKGbkVAUxlh+dwBoF99Ake6U67jMyL7PPXvU4P2ziTG3vIszlK+w4+ePRqA4abRjVCVj6PqPnN+p2y5l4nVuw6VuwpMDqjxx+XuUFV/7B1mC1AOsJEED2Jyr4vOLROuEh0BrddC77LqZy6G5V7rcMsY0+NlH5S+tQn0pOz5Z9o7kuhMpq37WxuPWeGSbyrfofS960Ihpc8esE+35+xot4dCFgcWdw26JP5M5WJzy5SxHoDdLeOVYsYz/YD5XxvnHkIA+zfUBO4jzx1UWrcmBtxen8BThT4+yqys0ueeiJGrFaX2fTTWGU6Lo6b1nskInPOfC2x1i5F/qyJG5Kq7zecO9bn07lDl9ANFoKle75XSTZzLVC52t0y5LXdlcE6IfWz4xLnbJ8jWvzX6eTzPKtkO1cBdbagdi2ZJuRXgIEq3zNKt2WgkGQpJDn+4nNha0tcUdzlT1a8WbrCsfinYQemRtW4Z5Tj1cOd+PeyWKS5yui0n5W7aM7mhWu5lH8SkVEAX8w7of8ydPWkrD3u+Pvdcxk/lGn7XUGt/cRRuuUfnlvncg83W8sA+RuslHrNb1Ulzkg3JcWa2yG0HOgAAz6/Lhn3Ko5yhjwDw46tOs5a1I1QVn3vMJu5uC995vqgJFHciup+I9hLRKmXdICJaQEQbzP8DlW23EtFGIlpPRFcUqd6R4CUEznknmdIhhMBLb7fm1FSvLMtdXdbXRVfHvYezYywCE4cp3PKnt/CCEo8+ffII3/rJcpIpgVc2+M8cpeL8Pgr2uYvoBp6lTWu9sTaOay4cC8DtUklnMqhRnpNTzORib+9x55VS3TJOThjUx1rWD2JSLXbvDlXb7SyjW+a3AKY71t0CYKEQYgKAheZnENEkADMBTDaPmUNEevO4ItDf1BFmylCm9MxftRuz7l+CB17fGvqYSrLcw4y81NVRtfi7ku4+H3V7JiPwid8swmfnLcUjS7fjMw8sBWAId98A14ys0vo9h3HNfUtsLg0/0hmBi08agmHmBBSFD2LKzmhUaFnStz7jrFHZiTRcPndhs8T71BqypMsvI1s1OreMWkaM3GMShJl4TG7PrnfUWf0+y+WWEUK8DMD5BMwAMM9cngfgKmX9I0KIbiHEFgAbAZwXTVWjR3dTP3T6SBxhy71stJi5srebzeUwqD+4cjvUwvxov/HH5dpUsZJ//22z8xDrvshyl2w5gH+s9R5B6oWzSgeO9oQ6LiME+tTG8YMPTzbLKdzn7jXJdK7I8Edbel4i2+d0RlhpB4DsM5PtYFbcJJbl7hZ3Z+y6X7QMkZ/lrrplKqtDdbgQogUAzP/DzPWjAGxX9tthrnNBRDcQUTMRNbe2hm8eRonuoRrUWItDXcmyN++Z8FSWW0YVCX1dupIZvLntoOM4/3K37MvOi+rp7oHhCLj2whNC1Q9wJ8PyQnZIWtPPFTrZhxAugc0XmQBMrZNzgFEqnbEZAU6XkHpb5Pemc8uo90uXfsA2HaJtvWO/jPtlEjVRd6jqnhRt1YUQ9wghpgghpgwdOjTiaoRDd1P71SeQTAur55wJ5uEl23DvK9Gkbcgnhjvu0/wtNarA+D1C2xwtk6BUwUe7Uzh3rNG15bWn9GMPb/J2Kzrvj67TUEc6Y7gbZKdkoda2gGo9R/OlqeU4p8BzDmKyWg2a+y4c+6iohkQspqm7YrmrxzvPo04CVaxHNt8RqnuIaKQQooWIRgKQ7cMdAMYo+40GsKuQChYT3UPVYEbQGM24Uteod3LrE0Zul8++Z3xZzm93y1SO5e4nWk5NCRL3VEagX31Cm89EImBMFKETpew+dsK+TJMZw/KVfvJCjR8h9LMdFVSmspxtYRhz2qYcPnd5j36+4G2cNrq/w3I3/uvujXpv4x5ZIa0Rqj5GxyEzrYE8phjka7k/DWCWuTwLwFPK+plEVEdE4wBMALCksCoWxqqd7WjvSGq36e6pzAPClnt5EULgjW0HtZ2LTtQfXJjJMsLyzv6j2OmROGvF9jbc9uRKlzCpnXh+ddENfvEjnTE6IHV+Xom03P2McWd9w1ruyZSRc0Xuny7QLyNEtkM16nTGQNZ9Ir+PVMaeW0a97Ptf3WKvm/ma0N0bdRVp3TLQJg5zfmdqKuFi2SNhQiEfBrAIwClEtIOIrgdwO4D3E9EGAO83P0MIsRrAYwDWAJgP4EYhRFmGe3Yl0+hOpfGh/30V19y/WLuPzmKQD0CqBHMcMm7k72FXexf+dc7rVqvAD1vzN8Ifynt/+iIuuv157bZ/u3cx/rB4G446humH9aUmHeKom6xZJWXGaev8vCpE/ta4yy0T0nRPOS33gPoGYetQDfmlpdIZa0SpDvXapPtkq9lXkXIMYvIboOTXoeqOlrHXXUCE6lDN1qOMbhkhxKc8Nk3z2H82gNmFVCoKJn5vvrX81o527T66Z0r65dhyLy+y2bp6l/67U4krP1r1pZzOGNb/uWMHeR477/WtaGpI4CNnjc6pftY0bGmnBa4uez9DTqs+yJhIZ8zh85qBMxI/X3F2H/uxzqnjvOhJGeIu9w8145QPaihk2J/al37/Bv6+Zg+23n6lvkzV527eg+m/egWb/uuDLreMuz7uZd3uNreMZiYmOU0f4N2hqtYzEaPI3FKuuhal1F6C7kcStyx3FvdykJ2azPwcIsFtv7qsjaKK5pwXNuLjdy/C4s37PY/9f0+vxtceXZFzPaVQdKftlrs9FNL7GXI+X0HGRCojEJfRKh67Gqfzv1/5elOSacN3LQW5o7uwBnkmk32xhBW3v6/Z41+marnLl29GIJXO4Eh3CjWxcHKX9bnr3DJ26183QjU7E5M+ikt9RnQpDKKiKsS9J+UOLVNxTpYgEXA3YROW5c5umXKSHUgSvG9DbbbnW7Uo15ujD3cXYYYt+cN15iGyD6v3Pt75fCUDLXeBmgCfOyBMn7v+pmUyAt/98yr7upDCmkxnbD73zz7ojsXPlXxDIb1eBl7XctOjy9HembS18PzKzIZChnDLOINlBLQdqs7IHas8ol4TClkW/uu5tfjInNexQTOUGDAE+1/nvIYfPL3atj6dEVZ0jLVvRD5FxpuOnlSgtZbLD14tSv3e5JLOAutKpkN11nohf+TOSZbTGpHQ4bTUg543w63g73MXwrDbVVvm4pOGWMub9x2x8rSrx4TB8PmTrVOyEOxx7rn91rz6J2wRL8r6Z99qAQBb+gH/uhn/9W4ZddkjK6ScrMOxXrccI6q4QUwVQ0dPCqvMWWPkaLuWdnuEQyIWwxvb2vBbc0j7iu1t6OhJIZ0RGOBIkypzPrPlXhy2H+jApO//Db9fvE27XeqwFMagzHyA/ceic2/sPOiOeJn4vfn45G8WhamyFkvcHcPXww4rd7tlgi33RIy0yaoksiWq3rMg4cglusjwuYfe3ZeMyHbm5iruzhdqtkx/l1jco/LOZ0zeM11/hLrOy+duhWF69L+o95yo9wxiKinNWw/gvNkL0fyO4ZKRX9KFP7FHOKi95Fv3HcWMu16zojAG9MlOtXXF5OGRxfEyeuRIy7+t3u27n2V1hyhT/eFs3HsEV9+72FbIf89fpz1uhUdH+4I1e7Cp9Yj1WTftomyyO8VdFW0/4XQ+X6o1Omlkk3b/eNywCb3EoCuZRl0i7pPTxH03cxHWSC13CCXOPbdjkx655u2jg93bvQx3ZwvLL1rG7XN3lyN1SH1hq6dQ3+NE3KGq5dSRTY4bqL9Jqs/9YIdh3csZ6Qc2Zi33sYMb2S1TZIIyAcpvSv5owvjcnRr66sZ9+PObO/OrIIwUstN+/pL1WfdykBonrcj6GmPF4e5wg1OcceLqc6x7KchomVjMPW8nYLxkOnrSRks0RAieuv3VDfsw58WNvvsBMEeohnNtBJER+Y9Q9bbcs8u5CqbOpROcfkD/QtFNdG5zHSobihkK2avFvbEugUGK5f3OgQ7MX+W2CNUHUlpIchqxgX3sk+QmOBSyqEh/pPqwb9l3FAsckRArzEmZw4i77od886PL862iC2kQqMgfuexQ7VtnGAmHOrNx2H764jQe1I5ZXZih9HnXJ+LamcLkRM4D+tTY7PMgjctkgKvvW4w75q/33xHGdIZewQk6LrnjBfz2tS3abUIgp1DI1zfts5Z1mRwB4LJTh1nLujK9niWXW8bHcldXOUeoymV5XP8+Na5tgP37jXGHqjfqzPLffvwtfOGhZa591KbkJ0w/a0eP8SN0ibu57/Nr/cOuqomlWw/gf9URc0Uk61PPrrv0Zy9mJ1xw/KDChEJ6uauj6qjS1cByy5ihkI11Rsd82GHlTuNB7dzVW+6Gz72hNo4uzUxh7Z3GC6h/n1pHCF52H119dC+Sp5brWz3xWCwny33bgQ784C9rtNuEEJb/Oozl/um52YGITst91IAGTJ88Ap+Yks18UsiQfr9ILXvyMbJb5+Z/uUv/hhrM+bezXfWxuWUKrKsfVSXuXtRoQqCkBa+G0QFZa+LO54ObqdXCx+9ehJ8veLsk58q6XXxCPtT9zQO+/fgKfPXhN23bMhmBjp5UWVpZVpy7KbTyug51hRR3h0B1KoKt61yVI1TrEjFXxAsAtJstBpl/RqK+4HThlmq0h3ypPGNGlwzpW2fbN56DWybILSKQbf3k6kJxWu4ZIdDUkHCMCM2pSHvdQvrcnW4ZuazWQ87Vaps4RDmmrCNUK50w4q7Oeu7E9rBS+FwbTH5Yg5TC7m/+f6x5BwDgzk+dZW375uMr8MQb3r51L83QDXeXCaZ0QqOL2BnctxYb9sLKPSNfMEeU4fH+ce72japg61oidsvdLe5S8OoSMc90DLoWgbqqoyeFfvU1VlmyH0GSi1smSFwLSfnrfElJl5XjBDmVqWvlaXPLxOzb/XzuQNaKtw1isrnk2C3jSa2PcEv8HsiJ5nRbkq6AWd9LTdDgligpRS5068H3OpVTSH2c7n7C7ofOFXG/6RsO2wroV2/4U2X0jxTOnpT6Y/cuy9k31K0IttNyF0IgbQpYQ42HuKez4q76kdQ66K5NvRcyKiiplqUQi4W33INCOwvqUHVa7hpx15UYJp+MsSx95+797Ja7PVpG3rcaW4Iy9wvM7nP3qm3h9H5xD2G5+z2QE0c04YkvvRsA8OEzjvNNTFRq3tl/FBO+81dPH2jURJlRMYjNyuQTtvNH+ILxKkp3net2GwPgdFFSuqdHliE7W6Vwqi9jv9u53zEDUqfN564/VyJGqK+Ja90yUvBq43HPnCa661bFXwYZyLKcv5s4hQ+FDHqWVMs91+fO6XNPZYQrAVou89A6+fT5JwDwmGbPFgrpGGNhPjs1cfs+zvo449yLNaSmCsS9sKTrDTVxnH38QGy9/UpMPq4/xg5ujKhmhbPcjBgJyqkRFaXwXUuXyL4j3ZrzZ1yCWIiTzNnU/seaPdjV1qkVE6ljzmyNXpXIumEMQUxrxD0X0VJj6d1hkkY58bhhuXdo4u4tqzFhz+eu1kD34lLraIm7WVY6I3DbByda23MZxKTG7e/XfNdqtEyu73O95W6vWM4pDcz/E0f0w3njjERzwT53+whV+R2obmDdVIL22Zp4hKonYdwyq3cd8txWX2s/ftJxTZg4oh/OH+edSbBUSP+tmhirmCzeEm6y5EJQXyBOSyqVFi5BjLIL5LMPNmPGXa9p3TLyR+jM8gjoI3Zkh6hs6cnPqvAEWYryGCEE3jRf5ID7paBa7g21cVtEjiRrucdsAuyVsEqyXknZIV1Tsqx0RthEuiYey8ty/5wmD41AdsRooW4Zw+du30dXpp9bRoo0OTpMnbh97tnPSfNDrUbc1erwCNWQOP2CueLMLQMAQ/vVldTX7YWcqFuOwC02s+4v/rwqaR8fcCotXD9KIsJHf/269Xnd7uyL+ozR/V3lv/vEwdrzSiFtPdyt7VCVYXlay11XnlmGFHfL5x7SLaPu+1jzdmzcmx0R6xThblPMauIxjBvSiH1H3HH3lrg7OlS9Bs9IfvNSdnrEx5ftsB2Tygibtd9QGw9tuavn2q5J/5AR2ZS/upfZR+a8htnP6sMonbll0sJtuedqDGfnUlX94UEjVO0vETlyNhF3vyC83DJGbpni0OvF3elzH9CnxmNP4KKT3D/8eo2418RjgZMnlAIpHOoPv7eja8ZKDLeM+74vU15u03/5CoQQ+O/567DnkLu5/8BnzgXgbtF12Dosvd0yWp+7xoKT341sXel87u8ccPcrqEhBdrYsnc+e9LH3qY3jxKF6t+H3nzayPdbGY1b++j61cVuTP4yb6Eh3yjrGsNyz19NYm0BdIo5+9fqW5F0vbLRGfqdtLTT3OTp60qgzo3GcPvRdbZ14c1sb5r6yRXueHk2a5VCWu7Y0Y72uNRcU5+50y7yw3phttEapDOk6VDXJxopBrxd3NRJmRFM92jym1AOAyyeNcK2r0bh1auJUEZZ7j5rhsASRLIUSZkYdVViTKeHa5ixC51/e1HoUv35xkzaVL4HwiSmj0ZPO4G+rs30Vake51nKX+UA04r6p9QiWbs26rNo7kpYgOy13VZi/86Q9vS4AfO2yk3HDJcZcs1LcnV9tTzpjq6P0x9fXxFHnMbGvHNhUm4jhuAEN2Hr7lXj3iYNtnXVhDJbWw93Wd5DOCFtLpo85JuTT5x/vMqq27e/AT/+2Hp/4zSLsPdzluI/28378bmMgoYwBd6ZNfs8dL9g+v+FI5510RCSlI/C5bz/gbl3oAjFUwd+w5zB2tXdZfQo/NAds1QRZ7o4497JNs1fpqE2ghGOw0pljBuBrl51s2z7ew/JRqYnHPPNXlBK1Y627TCGajyzZhlc2tIbaN8zsPDbLXZPT3GnVHDzqdkFc9j8vudZJYh5jFaSLy6uelrhrXBdvbmuzBAnI5o8Z0rcOR80OVfnSkoLtNbPSqIENOGloXwBZi13XoaZ+3zL0saEmHtjHpIouOZr8YSz3rmTa+o7SGrcMYA67d5S1o63DWr7m3iWOvhX7Oda2GNfd38zIGvRs/9RMjXD8oD7G/hrXlzNaZszABt8yVbwMOadb5qvTJqBOCeAY1GiMbt/Uam+h1Wh97kqHqtPnHrqmudHrxf3LUydYy1e+ayTu+Ojp1ud5nzkPN102ATPPNYYlZwRw58yz8IHTRuCuT5+N6y8e51nu5tajZbfe1Y6jcon7LU+sxDX3LfGcKFrFq3m5Ynub5StP+bhl0hnhEo3jB/fJqb7kMYryXqWJrwrWhGF9zePgqp8X0koe0rcWPekMHl6STV8sr+lwlz6kNpnOWAL8md8uxYrtbVorU41lz7plEtqWporNJQBnnHvwM/TsWy1WmGoqI/B5s5UBGG4ZwHh5OvPWP7Jku/V5/Z7Dtpep1x3ta1ruQXn1ZYtBvpweb95uXZe8Jqdhd+2FY33LVHl9k36mLqdb5sZLT7R9/so0Q3uefHOHldvHqIsmWka59fIZm3LCQDO3DLtltIwa0IArJg/HmEEN+Pb0ifjEuWOw7LuXYePsD1iJe4Y11QMA9rR34bRR/fHrq8/BlaePxPc+NElbphx+/ZuXNpXmIjxQc4jokkWVkjBZFr20Y8Zdr2H6L18x9rF1QNkf6p1tnZbQ/fnGizB+aKNnkigvYgRtRMejzVnxUevw80+cAUAfyuiFFCNpeaqTeMsWX7smogUwrD3Vut7V1qmNluhSvm/pmmqojWlTaagkHD5hrygNL/7vhWzajUxGWL8d4/xxW7lSlDa1HsHTK3bZypm/usVa9hIvIqOPIMhwaXCI+4od7Xhlg5FITLqanC907dywOUZeOS33OkfYtXzZPbxkuy01hjopiC7OXRow37ziFDO3TG71CkuvF3cA+M01U/DKt6daX/DgvnW2t6fshDoScoCSHHb9yNLtAXsWF7UT0OmXLDV9Q4RjqnlVvFCtZqfr69NzF1sW4Rmj+2Nz61Er1j8sRKSNclB5dWM2w+CQvnUY0VRv+bX9Uj1LkZIvWp0VLV8ObRpxv/+6Kbh80nCba6UmHtOKn/piV33uQZY7+URz5DqOYe6sKbbP8mXmHHx0SNNKueuFrGHkd9aedAZ3v7TJ13odN8T4/f766rOtdfJZm/uyEe0jXT1+XH+Rd0tdR1Da7z5KXio1/796m7Pinl0nn/F4jFyusyipCnEP4kOnH4dvvP9k3HzZhOCdATz+hXdjzKAG7DjYicMhBKtYqANbiuWW8YvEUV0kjSHE/fz/Whi4j9qcX7xlPza32s8vhECMws3A5GT2R04DEBx9oHZ0DmqsxdghfbB29yEcPNqDvYfdETgS+R1I4dVVUbY0XtX0U0ydOBxEZHtpf/bBZm19b3tipfUS6Uwa4tlQEw81Ilvi9OfKzt+vTj3Jtt+QvrV40hylrXLBeHt02WDTxyzF/ZP3/BMA8K9zXocfYbwOaqpkJzKvzpljBri2bT9o+PqDnpZvXXGKrRUShrZOd3+PivqbUF8Eal+SzucuX7IxIpfrLEqOCXGPxwhfmTbBNuuSH6eN6o/bPnAqAOCNbW348P+9iu0HOgKOyp27X9qE//eUO6JCItMSA8F+yXxxdk6qgq66Brw6CJ04W0fOB1ct/ztPrsLUn9vPf7grlVfytvPGDsK/mcPGc+kMr6+JY9LI/tjSehRn/XiBdsCNRLpH5HfRVO8Ou5Uv5F3t9kiee6/NWsHD+tmzLeoM6kWb92NtizHASPrv+9XXBFruKuTINy4jyW667GT8/rPnW+uHN9VbVrkf0tUhv59lIcdftHcmA91dUqSdfS4vvd2KnlQGdYmY9oU/rJ8h2D+66jTf8nUd8zounZjNCR/UWpZpngF7f4Yq9FLcf/fPd7DL7Lfq6smGts6/+RL88pNnhqpbrhwT4p4Pw5qMH+B9r27BWzva8eNn9AMqCuH2v67DvEXvaLd1JdO2EDAvy313e5ftJZAL/9zs7khSh57/+Jm11npdPhPALeatDstXPU4IgV/+wz9v/MsbWtGnNvcRuV+/PBsV5TUNmxf96hM4qgm5dCItX2m536B0Nkrk9XY47os6nsJpEavvv3uuOcdalq1GOSK1X31C63P3GsHszEnV3plEU30C8RjZRmBPGtnkemmMUKzcR264AHd9OusSybUfBAAmfOev+PTcf9r6jtTr3mOGtTr7KmbdvwRHe9KuENB9h7tx8GgP7jb7xXQvWpUDmglXVO742OlYcts0fPPyU6x1/3LGcdby3Vef4zpGda+pA8ualHEA0nX1+qb9uPJOo99JPmuNtQnUJmK+WWsLgcXdg6F9jYf75beN5nWp8rtIWg932ztUNeK6YM0eXPCThdk5Q3NgU+sRzDSb1SrSwrryzldsUSBe4n7Wj/7uqreKGjWyaNN+bWy6yubWo3mJhypOquU+VbHEnPz840ZnqrM/oW9dQhtyKK9FWu4D+tS4yk+mjYE/R3vSOHVkE84dO9Csn12U//TFrBtEbd2MGZSNDjrUmUJPKoMHF72DGBkvCLVei2+bhj998UIM7693NwzoU4PWw91W+e2dSSvIIBGPYevtV+KB687Fj686zXYP5vzb2Xj6yxdZny8YPxhXnj7S+qxOQN+sxP8H8fqm/XjPf2dj2OsSMXzILFeKujOhGmAIv3Mk+g/+sgaLt+ijXHScMXqA7/bG2gSGNdXbWo2qC2z6ae4xMkSEd43qb1v3gdNG4Hzl5S37CwDgYEcSt/91nWWM9akrLC9WECzuHgzvX+daF9Y1EYY2xZLYqsmQKLMNys7gw5rOYOlCeGNbW+D5tu3vsImIVwdUMi3QlUxbWRIlutl/1Nwj0ycbD7/6wweAvyhRFP/57FqEQb5I5l47JWDPLGqUyAFFID5z0VjPY2SnZ1/HiMv3nTLUFVoHZL8zWX5TfQ3u+Njprv06k2l09KTQWBu37o/TOpOx7oB9oJaauG7uK5vxkTmvYa8ysEh9iQ1vqsc5JwzCA9edi3eN6o+HP3eB7RyTRvbH4e6UNUBn58FODHVMwHHpxGGor4ljYGMtHr3hAvzH9In4wGkjfP3Tu9qyL+iPKfH/YVD7NK6+4AT8aIbhTpGtk73my199aT6/bi9a2t1GwRceegMA8JcvX6w916wLDTddbTyGa81lL7yE9skvvRs/M40AHU4P4sfOGW373FAbt+oBGK5YOTaiMY8Wai6wuHtQl4jj0RvsPxZdTo8n39yBW594K+fyX3o72+H2vp+9iF84ZkKSAnKTOQgryGeoludk+fY2XPLTF/AH0xLPZITNKlfZebDTJsiA0ZHX6XD9LN68H1fd9Zr1eVDfWhAZlreKKuhrQkQ0qLx/0nBcOF6fK8bJUaV+Ugjm3/weXHzSEM9jZAuhT607xE0K7qfOO97yR9/76hY8v24PZj+3FrWJGAY21mJI3zqro17+iDu603ht437EY2T5Yp2WuzoD2PzVu23rpTtg+fY2V2oCXYfqmEF98JevXIwLHXl1Jh/XBABY09KOza1HsGTrAZyh6ZSUnD9+ML74vhMDO7OdHZvnjRuE90xw3+cpJwz0LOOuT5+N+pq45cKQ0/G9vGEfiICvv/9k7XHDm9xGl9fAxB98eDI2zP4A3p79Ae01feuKrAtmhMfL7KzjB7oEW8VZruwDULnxUnsH9txXjAgf52QoUcPi7sP54wdj3Y+n4xefNN7cy7fbO5DaOnrwtUdX4OEl27Gp9Qha2jvxvT+vwk/+uha/WPC2thd8c+sRfHbeUtz0yHLb+l8t3GDrTJIvEmm5H3SkVXB2UP2xWR+22dLeiZU72gAAz600Yo8XbzmA1zYaTdpppoV05buM5vHV9y3Gtx63v6yaGhI2X2hLeyc+ec8/sXJne7a+h7tx4tC+Vm4RAPiKY1o8lY+cNcpzm8oDnzkXp2sShEk+ds5o3HjpiThvbNaHLK20sYMbbT++P3zufNuxs95t7OfswP3GFVlh+fwl47Hga5cAMCzIf/+t0VpSXUc3X3Yytt5+Jc48fgAA4IKfGFFDi7ccsGL5nbH3tYmYTVxU7px5pmvdP2+dZpRjviTCBBOdMqIf6mtieHblbqvj+tSRTcEHBvCVqSfh1f+41Po8ffIIzL12CpbcNs1a954JQ3CKYyIcABg7uA/+8fX34oPvMlp6aovmuZUtuPulTbj4pCE4bVR/fPNyt8A//433uVw0zpezhIh8O6BV0R2dw4hW+znsn8dpXjTDmurxY6XDVxpu+USE5UKvn2av2NTXxPEvpx+H7zy5Cou3HMD00wwRFELg9r+us/a795UtLmv4Y+eMtnyoQgjc8LtlWODjuz/Q0WPNW/nGtoPoV5fAKcP7oS4Rw8tvt+Lzl4wHmWGCH7rzVQCGdbt06wE8t7IFQgikMgI3/v4NXH/xOHQm07jugaVW+a9t3I+Lbn8euxTXyT3XTkE8Rnhq+U48u7JFO/hmcGMdHmvegdED++ArU0/CH80p71TGD+2LEwb3wdxXtmBXWyfmvb7V1QJQ+cUnz8RHzx6Nxro47pi/Hv9yxnFYt/sQHlz0js2PWV8TN62hduueyuyFd199Di6dONQ1uOTaC8faRih+7j3jMLypHu8+cQg+OWUMHm3ejm9dcYrVcTt98gi875SheHF9Ky6fNNxmfY0d0hjaHdfZY9/vu1eeat0DncV95btG4qd/W299ft8pQwEY3+/xg/pgmxKhNcL0q9fGY7jk5KG2pr4X9TVxfOyc0Xjon9nn8rJTh4e6Fj8S8RhGD+yDx79wIQY21uJE08VUXxPHP77+XnSn0ph8XH90JdO45OSh+PzvspPW/+KTZ+KkYX215X7p94ab5bj+htB+eeoE7DjYiUeWbsf91xkuusa6BB77/IWYobQaCxHJuddOwXMrW6yZtXJF7at416j+nuNBVDccEG0qay+oEhJSTZkyRTQ3e4egVQJX3vkKVu86hJnnjkHzOwdDZWo8b9wgnDqiHx5est0zPO/RGy6w4oX7N9TgE1NG44k3dmL/0R5MmzgM9113Lq7/7VIsXGdknBvYpwZHulOWL/ezF4/Dva9mh9a/a1R/m0UdxNbbrwRgjJR89+3P27b9+0XjcNFJg/HFh97wDS98/hvvxaiBDbjv1S24Y/56z/0ky7//fs+w1H1HulGbiNmiH/6+ejduMAXioevPx9X3LbbVPReS6Qz2Hu7GqAF2S+3A0R6c/eMF+NXMMzHjzFG45r7F2Nx6FK/dMhUAMPaWZ237nzqyCX+96T22de0dSdz5/AbMX7Ubk45rwtxrp2BnWyeeWLYDX556kt418McV+OOyHXj9lqkY1FhrRdW0dySRymTQUBvH4a4UhucYoy3p6Enhp39bj351CXz9cn1LodjMfXkzZj+3FrMuPAE/nOEOWfzLil22Vt5Xpp6EbwTU9bt/XomH/rkND/77ebjk5KGR1zksew914aHF23DztAn6UbEmXck0Jn5vPgDg7OMH4J5rp7gmIM8HIlomhNB2TrG4h+TmR97En5e7LdHPv3c8Nu45goXr9qK+JqbteJQMaqzFgIYafOG9J6K+No4PnDYCiRjhgde24keOUMt+9Qn89GOnY/ppI3GoK4nTf/B3bZlrfzQdizbvs9wFOgb0qUFbRxLD+tVZHVq1ZnI0VSA/cfciLNl6AE31CXz+vSfiS6b/9SNzXsObHp229183BVMnGtbg/FW78YWHlrn2ueeac/Di261YtvUgLjxxMH7w4cmedfVDTmJ990ubsPNgp62pGwVCCE8rcM2uQ8gIgS/+fhm2H+hE83cvi+THeSwghMAzb7UYz7vGTdKVTGP+qt14/6TheH7dXlw+ebirNaZDPg+9hUNdSSzefADvn1R460lSFnEnoukAfgUgDuBeIcTtXvv2BnHfcbADDy/ZhqkTh2NnWydefrsVDTVxfP6949FYm0DrkW6cPLwfNrcewfihfbF6V7s16u7k4X2RiMd8B4oc7U7hjW0H8dL6VkydOAwXnjjYJjRb9h3FqAENqE3E8L8LN6C+Jo6PnjPayky3ufUIThjciFU72zG8qR77jnRj/NBGy/Ww93AXGmriaKiJY/vBTgzrV4ej3SlbVEQqncH/LHgb11x4Akb2z1q2h7uSaO9MoiuZRmNdAiP7N+DA0R4QgIGNWQtcCIGNe4+g9Ug3CITaBGHMwD45jwysZI50p3C0O39LmmGipOTiTkRxAG8DeD+AHQCWAviUEEI7Eqg3iDvDMEyl4SfuxYqWOQ/ARiHEZiFED4BHAMwo0rkYhmEYB8US91EA1Ni8HeY6CyK6gYiaiai5tTXcZBAMwzBMOIol7rpeDpv/RwhxjxBiihBiytCh5evtZhiGqUaKJe47AIxRPo8G4B30zDAMw0RKscR9KYAJRDSOiGoBzATwdJHOxTAMwzgoyghVIUSKiL4M4G8wQiHvF0KsLsa5GIZhGDdFSz8ghHgOwHPFKp9hGIbxhhOHMQzDVCEVkX6AiFoB6KckCscQAPsC96oejrXrBfiajxX4mnPjBCGENtywIsS9UIio2WuUVjVyrF0vwNd8rMDXHB3slmEYhqlCWNwZhmGqkGoR93vKXYESc6xdL8DXfKzA1xwRVeFzZxiGYexUi+XOMAzDKLC4MwzDVCG9WtyJaDoRrSeijUR0S7nrExVENIaIXiCitUS0mohuMtcPIqIFRLTB/D9QOeZW8z6sJ6Irylf7/CGiOBG9SUTPmJ+r+noBgIgGENHjRLTO/L4vrObrJqKvmc/0KiJ6mIjqq/F6ieh+ItpLRKuUdTlfJxGdQ0QrzW13Ui6zgQsheuUfjJw1mwCMB1ALYAWASeWuV0TXNhLA2eZyPxizWk0CcAeAW8z1twD4b3N5knn9dQDGmfclXu7ryOO6vw7gDwCeMT9X9fWa1zIPwGfN5VoAA6r1umHM6bAFQIP5+TEA11Xj9QK4BMDZAFYp63K+TgBLAFwII436XwF8IGwderPlXrWzPQkhWoQQb5jLhwGshfHDmAFDDGD+v8pcngHgESFEtxBiC4CNMO5Pr4GIRgO4EsC9yuqqvV4AIKImGCJwHwAIIXqEEG2o7utOAGggogSAPjBSgVfd9QohXgZwwLE6p+skopEAmoQQi4Sh9A8qxwTSm8U9cLanaoCIxgI4C8BiAMOFEC2A8QIAMMzcrRruxS8BfBtARllXzdcLGK3OVgAPmO6oe4moEVV63UKInQB+BmAbgBYA7UKIv6NKr1dDrtc5ylx2rg9Fbxb3wNmeejtE1BfAnwDcLIQ45LerZl2vuRdE9CEAe4UQy8IeolnXa65XIQGj6f5rIcRZAI7CaK570auv2/Qxz4DhejgOQCMRXe13iGZdr7neHPC6zoKuvzeLe1XP9kRENTCE/fdCiCfM1XvMphrM/3vN9b39XlwE4MNEtBWGe20qET2E6r1eyQ4AO4QQi83Pj8MQ+2q97ssAbBFCtAohkgCeAPBuVO/1Osn1OneYy871oejN4l61sz2ZPeL3AVgrhPgfZdPTAGaZy7MAPKWsn0lEdUQ0DsAEGB0xvQIhxK1CiNFCiLEwvsfnhRBXo0qvVyKE2A1gOxGdYq6aBmANqve6twG4gIj6mM/4NBj9SdV6vU5yuk7TdXOYiC4w79e1yjHBlLtXucAe6Q/CiCTZBOA75a5PhNd1MYzm11sAlpt/HwQwGMBCABvM/4OUY75j3of1yKFHvdL+ALwP2WiZY+F6zwTQbH7XfwYwsJqvG8APAawDsArA72BEiFTd9QJ4GEa/QhKGBX59PtcJYIp5rzYB+D+YWQXC/HH6AYZhmCqkN7tlGIZhGA9Y3BmGYaoQFneGYZgqhMWdYRimCmFxZxiGqUJY3BmGYaoQFneGYZgq5P8DS2VjDV3zw+oAAAAASUVORK5CYII=\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "&lt;Figure size 432x288 with 1 Axes&gt;",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "agent = DQN(env=env, \n",
    "            buffersize=20000,\n",
    "            gamma=0.95,\n",
    "            batch_size=32, \n",
    "            lr=5e-3,\n",
    "            update_target_freq=100,\n",
    "            seed=42\n",
    "            )\n",
    "agent.play(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "20000"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "len(agent.buffer)"
   ]
  }
 ]
}