other/evolve.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From /scratch/sergei/miniconda3/lib/python3.7/site-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "non-resource variables are not supported in the long term\n"
     ]
    }
   ],
   "source": [
    "from gym_compete_rllib.gym_compete_to_rllib import create_env, MultiAgentToSingleAgent, model_to_callable\n",
    "from gym_compete_rllib.load_gym_compete_policy import get_policy_value_nets\n",
    "from gym_compete_rllib.test_single_agent_env import episode\n",
    "import gym\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "import ray\n",
    "from time import time\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-08-19 02:19:05,313\tINFO resource_spec.py:204 -- Starting Ray with 3.86 GiB memory available for workers and up to 1.95 GiB for objects. You can adjust these settings with ray.init(memory=<bytes>, object_store_memory=<bytes>).\n",
      "2020-08-19 02:19:05,543\tWARNING services.py:923 -- Redis failed to start, retrying now.\n",
      "2020-08-19 02:19:05,690\tINFO services.py:1163 -- View the Ray dashboard at \u001b[1m\u001b[32mlocalhost:8267\u001b[39m\u001b[22m\n"
     ]
    }
   ],
   "source": [
    "ray.shutdown()\n",
    "info = ray.init(ignore_reinit_error=True, log_to_driver=False)\n",
    "num_workers = 10\n",
    "env_name = 'multicomp/YouShallNotPassHumans-v0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n",
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n"
     ]
    }
   ],
   "source": [
    "env = create_env(config=dict(with_video=False, env_name=env_name))\n",
    "policy_model_1 = model_to_callable(get_policy_value_nets(env_name, 1)['policy'])\n",
    "env = MultiAgentToSingleAgent(env_config=dict(env=env, policies={'player_2': policy_model_1}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "@ray.remote\n",
    "class Evaluator(object):\n",
    "    \"\"\"Evaluates a policy in the 1-agent env.\"\"\"\n",
    "    def __init__(self, env_name):\n",
    "        env = create_env(config=dict(with_video=False, env_name=env_name))\n",
    "        policy_model_1 = model_to_callable(get_policy_value_nets(env_name, 1)['policy'])\n",
    "        self.env = MultiAgentToSingleAgent(env_config=dict(env=env, policies={'player_2': policy_model_1}))\n",
    "    def episode(self, policy):\n",
    "        return episode(self.env, policy)\n",
    "    \n",
    "def rewards(pool, ps):\n",
    "    \"\"\"Rewards for an array of policies.\"\"\"\n",
    "    return [v for v in pool.map(lambda a, v: a.episode.remote(v), ps)]\n",
    "\n",
    "def compute_rewards(pool, p, total_episodes=100):\n",
    "    \"\"\"Compute rewards using a pool.\"\"\"\n",
    "    return [v for v in pool.map_unordered(lambda a, v: a.episode.remote(v), [p] * total_episodes)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "actors = [Evaluator.remote(env_name=env_name) for _ in range(num_workers)]\n",
    "pool = ray.util.ActorPool(actors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Computing performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating agent humanoid_blocker\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Creating agent humanoid\n",
      "Reading agent XML from: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/humanoid_body.xml\n",
      "Scene XML path: /home/sergei/git/chai/multiagent-competition/gym_compete/new_envs/assets/world_body.humanoid_body.humanoid_body.xml\n",
      "Created Scene with agents\n"
     ]
    }
   ],
   "source": [
    "random_policy = lambda _: np.random.randn(17)\n",
    "policy_model_0 = model_to_callable(get_policy_value_nets(env_name, 0)['policy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Throughput: 6.750376882405489 episodes/sec\n"
     ]
    }
   ],
   "source": [
    "p = ray.put(random_policy)\n",
    "total_episodes = 500\n",
    "\n",
    "t = time()\n",
    "R = compute_rewards(pool, p, total_episodes=total_episodes)\n",
    "t = time() - t\n",
    "print(\"Throughput:\", total_episodes / t, \"episodes/sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sergei/miniconda3/envs/chai3.8/lib/python3.8/site-packages/numpy/core/fromnumeric.py:3334: RuntimeWarning: Mean of empty slice.\n",
      "  return _methods._mean(a, axis=axis, dtype=dtype,\n",
      "/home/sergei/miniconda3/envs/chai3.8/lib/python3.8/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7efe681d64c0>"
      ]
     },
     "execution_count": 10,
     "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.plot([np.mean(R[:i]) for i in range(len(R))])\n",
    "plt.axhline(np.mean(R))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Throughput: 0.45114286124492026 episodes/sec\n"
     ]
    }
   ],
   "source": [
    "total_episodes = 50\n",
    "\n",
    "t = time()\n",
    "R = [episode(env, policy_model_0) for _ in range(total_episodes)]\n",
    "t = time() - t\n",
    "print(\"Throughput:\", total_episodes / t, \"episodes/sec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7efe0803cdc0>"
      ]
     },
     "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.plot([np.mean(R[:i]) for i in range(len(R))])\n",
    "plt.axhline(np.mean(R))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "stats, p = pickle.load(open('evolve_result.pkl', 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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"
    }
   ],
   "source": [
    "plt.hist(stats.get_fitness_stat(lambda x: x))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb9550d290>]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cMcEtdtXHrgpYmBZ7oTHx6jJ5IrKoJyJERJFe6Rn8frOeJur3mBcSJOy9jDCiDQJFkUj7+IklB7fCJGVhd8tn7jhnaFMp/euqCm0hBHKrWK6YPGaemruqxySV935len5e4GXo7Hax2HtA2MVvpwq8bp9SUayOhYS9gtG6SkJouF5l6CYvBVmYWLhi5Pj3rCsmmHskDIK6YoJkBPS7R3OZbJRS3Ai5lKu+Tyrf0a8eXp/r6pOLtd2hWuwe5RYK5/ZrqDMYwuiwC4GEnfBFJ3hhCruuKLX89u4UOlzyxsjVqzeFvd1msQtXjHedgy215o58tF8n5CUK+9u7be9zdcUk0xnHtbJ8+mnhPvAs0lY3UVdVaFU3kl/H6HDlSK91rrdc2pgq7NlzGk9vYXbaqgtGV3axU/im0hlbErgieWJI2MuZxhkzcduzy3H+r9/AyT+b7fh84q0vYPPedgDAGb94Befd9Vo241+e5zzlZy/7RMUY/zkHdrV04sjbZuHKBxa47JstqSpqNDU5eZio48f+900c84NZ4JyjccZM3P7CSry6chcaZ8zEml32/NpdqTQaZ8zEVx56B40zZmLRRv+45P99ZQ3+35NLAfgP1F527zyc+vOXtQJw3I9esmL9Af+bVBWW//zdXBxx24tonDHT2nba7a/g208ttQYZM5xj98Eu2z6OctMcX3/sXTz01gbjvSK003/1hu293Enp5g2onZh98NR489qqZjTOmIlt+ztyGlBVOzLRF27d346jvj8LVz280LeMB99cj8YZM30jmkS9REf3pYfeceyj/q43P7nU81rnwy9nrUTjjJnIZDiufGCB7SmRfOwEAODheRuxamcrdrboJ4isNNPobtnXgbXNbQVb7DtaOj0H32SxEmlSt+zTT7qQy8lGKmQbubjJD3al0NKZtd7ufX0tZi7bDgBYvHGf7Zztpivn5ZW7AACvfxgsPfFTi7YE2g8Ath/odH2KWNd80HrtZ7Grv8XyrfpUx08s3GKJaTKd8V2UIZXh+NfSbdZ7PyM0LX0X0Rm4fe44l/LZht25tTF18FSwcY9hkIjf0YuH520AAOz2mSQl2puXa0ntxJ7MoV0E5b7X1wEwggoWrLcbHsXKeErCXuEEmZySK14BIeLm4T77qXXJCnt2W0SxXuQb0pquz7lnhEquBpCbGE8aXm9773bO1k4pqsfn5PlEfgTJ2a0Kq5/Qytc1pqmztxDa61ObiOWUasDhYzdPv7/Df/ERFTdRVDNGerl3iuGKEVa5bqA+yNyGMCBhr3DUdhKGz9KrBPm+8BuUE8LGueQPlo5xdEpS2eLmSHNu+07q9831RnHT4kTMPvPVzWUjC7vfufP5KbrSGfg50lSr0+83l8VZ17F5dZyqOMWjkdx87KorxvxuYrwiHvOXoKBnE22Tc/dOtRiDp6Lz1A3UUxx7HydoLLJ6o4YRwxwoKkYRXP2+ok7Zmy5lc8V4WewiU59duNSq+VnNKm4+zuoq+63gJgCynzeMqBiVLhfXha3cdG7CLn8e08zQcQ6eZt+r4pThPKfv5Wqxm6sb1QRIJRF03CgjuWLc2rAaCtoTZC12529JUTF9nMD3jksu7sJWUPIX9jT3dwHIAsE1j8mqxto/y05kkberopLrjeJWZ4fFHsAVo0P+Tvl0sm6TvWRUi93vd5A7KV3H5mXFqhZ7OsPBA3gzEqYl7hYxlZOwi0W7fX5q2RXj1vkUY2H1qIfFToOnfZygj7tOH3vuYqI+tnoVIYfY+VmKcjlqWJ+BvfYZjeinM9wmZOr3i+bYgt1cLIlYMItdXphad61jUoXycYvl42P3f3Ly9rE7vof0Vh389LKGZWrMlbbc4tgPmD72IDqXtdi9d5Z/W7cquv2uYc6EjZr+Fp2PvVjzo0jYyxRdO7v/jbW2aAjAaZlb6z2a799asxtb92ejVva3d2O2koJ29a6DtvfyuZcqqV6FwZPOcMfNIKqycMNe3PPqGlsEidh13e42vGOmTlUtsHnSotfie73+YTO2SfVXDWmdxb6rpdOxTeA2eJZQXDFvfNjs2GdAbRXWNrehubULzy7ZqhXUKkmpctWKeCyCrfs7sGVfu+d+qsX+6IJNLns692dg+Me79rov2bwfH2xvwRsfNmNnS6dNuOestkcdZTIBhb1KL+xiEXGRDC6Z4ZirtFEAeG3VLmsx9KBiKFvjf3hjnXafZCaDdIY7rkGacyzcsBcbdrdh1ood2NvWjX+8u9V66lq1oxWPzNuA1TuNKLS1zQexWElTLRCdpy6GP0iqijCgtL1liu7m+dnzKx3bGOyP/Ophn3lgAaqrIlj54wsBAFc/sggL1u/Fov/JrlF+/m/scc/yuS+5Z64t1as8u0993BWDiT+e+QGWbt6P0QNrHGUu2rgPl907Dxtuv9hhf4k0wkD2kfX1D5vxuiSyQVwxIh2tSjKdcbXYVFfMI/M3Ovb5xLGH4OF5G/Gx/53jGn46vH811jUb4Yq5WuwN1THsPtiNG/62xHO/dI7iINdj1c5W3Pj4EtukGfl8IxqqcfJ497S26QwP1GEJX367zxKKqXQGVz6wAP1rqrD0+x+1tn3xT+9g8vB+mPXNs7Ln9nM5SRW766UP9fukOR5/ZzO++8wy23hJOsNx6b3zHPtHIgyfOPYQ2z2y4faLcd5dr1uvVUTbPahx2/V0wjsBWexlSlCXSoQxW2PRiUmn9Hgv4oc7PabWe0fFSH7MjF5kxZR28RjPof8+Xj5Tt0f0II/M2w7oLXbDreNisftEZ3zxtEbccuERAOAq6gBw1Znjrde6GO5RA2oc2wSD6xKedRDkGtmh2/+AS7jhjpZOT+FOc2517hdNGeH4fPJwM9+6eZnbXHzsgHFNRduU6yMEfG2z+iTp/b2DhDKm0hnsMyNytu3PthO3sve1dWu3eyGEXTceUwwfP0DCXrYENfYYsw+4ZWzWu7MQIZhqPhEZ78FT47/O1ypCucRW2ceYa6Y9t2gX9ebNxSpOZXhgH7ujPoyhuiqi9VHLiJw44ny5nKe+OtgDdK5PArr9verh+ftnssJbU+Wsb0ONsU20Da8lFOOxiNZdIeormoc1dyIEYU9muPXd5aiVMFMbxCxhd3aehabHCAoJe5mSyyCoHCInH6aLsBCC2emRMyVIuKMxeKqUraRklaMCdO05n/gA9QZ0i17R4ZUALOETnRGNGH7/fj7i6yf8XueJBxwJzjWMUre/V/y4V/FGh268VsclgGxOIPE7eS167nat1MXBRXX8NDHIk0wqnbGE3dY+XZpGPpFNXhZ7sXLVkLCXKUFCygDjBnKz2LUz38xG1+ZxwwWJilEnDgHOzH22emnuSq+QTLdp7qrFnYtbwisBWBCLHQD6VVd57ucXkud1Hp1Q6sjdYne2A69OxCsHe0YaNNeVIa6PZbF7+NhjLnWwfnvLYoetTDeCxKin0tzKW2R7onQp28tl6UbUy2InH3vfJqjFnuHcFiKXjWN3mflmKo9bfLFchg5rBfiMs47ixlKzQ/KAIXIybo+sqqjl4rPs9Agl9BX2iBB2P3eJj8XucZ6gFnsYPnavJx2vh6B0hlu/q+67CHeS+JnURc9lqlwsdlVkRUfj93AWpC0kMxmrbfm5CgGgpSOZs5UthL1FZ7GTK6Zvk4uw6yx2Bqa12MW91Obh+/R2xRj/dYOn4sbSHe64WTn3nF7tdjOpgpTLjSIGM9VZpoC79SgQ162hUIvdxRUTYfpZoTrC8LF7xct7Wezy2IrOnVNrfj9xTq925mqxW64Ysz4BLfZgrhhu3Rfd0hOcW9mtnSltdIsXsTIYPKVwxzIlqB/1yYVb8M6GbDyt7PKQfe/XPLIIQ/slsNYMxfOy2L/91HuObc+8uwUPzd1gxSHvaOnE1x9717ZPhgNzVjdrbxJ1023PrvAcPHW7Sd06k+bWLlx671u45uwJrmUKYR9UG3dEzvjlfak1V4AKOsDphpvFXp+IoTrALEwAeHuDf6piwTsb9mrb0o+ee9/1GK9+4+pHFmFIfRyA/gmj1vSx96+pwoGOpJWNU0eV0pFxzsEYy/rYHbOqOX45ayU6kxm0dCSxvyOJtbsOWp1HkOuSynD82Pzu8hOcW2fZ2pn0nG1849/exbYDndi6rwNnTRqKn39yimWx/32xM3NksXzsJOxlSlDPxQvLd9jey+1TtuRfXGHfr91D2HV88/GlgfZbsmm/VhjUG+eR+Rtx2LB6544msmU+tF8C/WuqsGbXQceNIXzuzy/bjo172nHL08tcyxSWWr/qKkAR9uq4U1SvPWcCqiIMBzqS+MoZ4wAAJzUOxEvKBC8A+P2VJ2BwfcKxGIeKm7D3q67CbR87Ek8v3up5vI7qqoirm2nRxn2YPKJfTuXpBgxvPn8yfjlrFQBg90H3BF5D6+O47WNH4rwjhuHsX77m6oqZfdNZeFOZ/NTenUZdImb52HWDp/e8uhaAYRUHiTCpijKPhG72OHYdXamMLXJnZP9q2+f/WJKdMPjaKiMFsTACdOR63+ULuWLKlHzzqtsGTz0et0UY2h2XHoOBtd7uhaCI0MugKzt5uRRki/3u/zoOV0wdqz1GxKUHSQYmLHaxsLZMdcwZyviVM8bhpo9Oxg8vOdqyps+YOFRb9oVTRmLquEGuA8LiGqsToQT9qmMYUBvHaRMG27Yf7iPKx48dgBPGDrTeHzq41vZ5a2fSM9+6Dp0L7/yjnDHrbp3Ul88Yh0MH1yEW0bsDAWDisH4OV4ywjNW5BjpXTNCwwTsuPQbD+unnB+xr904PARiGgxir6l9T5fmU6bVyk8Av11BYkLCXKfmOsciNyiuhlLAcoowFSp0ahEQsgq5UJvBarF43p2wlxWMRy8etHiM6gCA5R4RVK8eaC3ShjFWaQQDVfeAox2W7mGLvFvkifPfqgLefeybCmO2YaqXjaO1MWdfMb4BYoDMIdMmrqjTlyb+OX7tSr6WwoK1xIuvjYHHsOiKMWXVX6yMv3OEmxqlMxoqmqk/EPF0pqQDCfrArFUoGVj9I2MuUfJMSyQ3P22I3GmssyhALKUl0IhZFdyqj7ZR0EQ1eA0lt0iN8Iha1LCXV+hSP2UHysosbtM7lUVkVfN1gpt8gq1s1hEC7iauY2KNauLUaF5HtfMoxap1bpZWpgnbgujkOuuvrN1jp15Go7a7FstjdBk89i9MSYcxqO2p9WqU25irsksVel4h6GiPi3vPKB5PO8KK4Ywq+oxljX2eMrWSMrWCM3RFGpYj8s8DJ/sTutHsD6jBdMdEICxyN4Uc8FkFXKq13u2i2tXqEwslT0RNVssVuv2lyccWIjq5W44oBnIKvFfY8065mhd3NFWNY7GqsvV9aW9ViV10FLR1J69q7nVtFlwpB1/fr5gXIP7Pf+ZydkGGxiw5DuLUsH3seyh6NMMtC9qqP29NtMp2xOs7auI/Fnva32IHiuGMKGjxljJ0L4BIAx3LOuxhjw8KpFpHPIg1AVugY87bYhXDGIiy0HNHCFaNr17rHz9bOFGqqotpp5bLFHo9GrJvc3RUTwMcuPVLrqFGsY70rJj+LXbhg3AdPjTqprhi1Ts4T2gVW/ikZExZ7xvPcKupAbDwa0bYRvxTD/q4YvcWuhjsK8rkn5Gp7fX+3AdZUJhseWZ+IeT6liHsvzTnisYjrTOfWziRGKIOwYVOoxX4tgNs5510AwDn3X5mWCES+g6cbdmdTvoqFrnWIcMdoJBLaOoxxFx875+5WjNuEnzVSKmHGssKt3lgrthnhl0Hm9mw3I2HcohZUt4fuKcDv6cYtZ7iw9N187OI6FOqKkXuWATVVaOlMWtcsuLDbO9pETN9GdB2y/Nv7u2L0FrtuzgNgpM7NlQhjlsXv1dEs33pAu92w2LOD7h3JNOau0S+gnkxz7GrtxNpdbZ4BCS2aGalhU6iwTwJwJmNsAWPsdcbYSW47MsauYowtZIwtbG525rom7OQ7wPLg3PUADFG/++XVrvsJN0gswnyt3SATYkY0VCMRi6IrmdHOHHQzdI48pMG37PpEzHXwdMOedmzZ1x7IdfXHOca1Gd6QjZI4xLScJg6r9xVRQG/Fy4xVolIE4onDLSXB8AajHh85crhtu58rhjH7MRQ/6dwAACAASURBVOdOzkbt9K+pysvHrgr2x487RNvJHT2qv2Pb4SOzv6dbJzZhaB0A5+DrQcVit1IKmJ//8F/usfduRBjznCkr+J9/LNdul6NihKvuygcWaM5j/J/605fRkUxbeZTEdwWAsYOMtqGbkRo2vq4YxthsAM5YJ+BW8/hBAE4BcBKAJxhj47lGlTjn9wO4HwCampqKM6+2glG19OqzxuOLpzfi1J+/knNZz3/jTEQjDD+Z+b61cMLeNiMioL466zfsVx2z+f+mHzEMb3y427rhPnvKWPxlvnNRh3m3TEP/mipc8YcF6E5ntL5Qt47qzsuOxdvr99pysQuW3PYRdKcyGFyfkCz2rHU6/YhhmP3BLjS3drk+IkcY8KtPH4cbH18CDo5BdXGceKgRHlhdFcGb35mGXa1dGNG/GjUe8ccC2WJ/7utnYOzgWtv3nTC0HvNvOQ8Hu5KY/isjh/fi730E1z26CICRc33S8Hp8uDP7RPKT/zgal580BgBw28eOxMPzsrngRZ0iDJjznWn4n2eW4dVVzThl/CDMX7cXDAy3fexI/Pe0iQAHhtQn8JvZRofeUFOFDbvbrNm5YtbrVWeNx/3mQhQ/+PiRWLe7DQ/P24gpo/pj2dYDtjbw5nfOxYiGatu2Z68/HUP6JTBqQA3+fu2p+NTvjTzm826ZhpH9s2mJdS6vpkMH4pGvnGzUR3nMEk8elo/d3O7VaXu5PADTxw77E0ttPIrvXnSEq5jLpCQfuy6aCgDOO3wYjhk9AL+enc0Bn4hFMPumszGoLg4GYwZufSKGNbsO4rDhuc0ryAffLpxzPp1zfrTm71kAWwA8zQ3eBpABMKSnK90XUF0xowbWYGT/Gt9wOx2jBtZg8oh+OG7MAGvb7lZjkkm/6pjVcMcPqbMfN6AGHNx6dDxm1ADoGFgbR208ZvjYk2l9SgEXq39QbVybo7wuHsWA2jiGmZYs01jsE4cZN0hrZ8o190mEMYwyF/xIpjkOG1ZvDaINrksgEmGWv7MugMUuC/vRo/qjoboKA2rjtn1G9K/GkPrsU8GgurjVMUUYw+Ej7E8px44eYEXbqFE3wmIfYF4n8e1HmNclEjGOGdavGsMaqm2Wdf+aKhzsSlkpmqtNYZNj3UcNrLV+L2FRyoweWItYNGIr99gxA6zfTP6esqgD+qeTAbVV1riB/AQRkXIbZdP2moOnHso+pC7u+plRRrZjiEvCPsjnOEEqk7XY3dxwx40ZoH3amzisHoPq4hhYF8fogbUYUBtHU+Mg9K8JZ96IF4W6Yv4B4FwAYIxNAhAHoHdAETmh6pTwewZNFCUjLBX52D2mxd6vusoSdnWgLhqJIJ3hlrXm5g+Xw8m60xnt+IDbmEHEJSpHdRuIm1zuIMTU9tbOlOegljzwl6iKWtdDjXDwHaiEvyvGOo8SgSGuUTTCHJaf1+C1EAwhzuIyinU1vdYBbaipQoZnfbrCYh8kdUSxKLNcZ14x82519HLj6duL9FtI1ygRi1q+bMcEJdcz+LuXbL+9dL6gAQNyVIxbVE40yhwGly6yqJgUKuwPAhjPGFsO4G8AvqBzwxC5owqhiH3NZzKREDLZ5ymiAAyLXR/fHYsyZHh2UMvNPyxuEsNiDx7HLtBFmjiF0fifsgm7YS22dnpn4JMH/hKxiHUNVX99EB97kLBKQNcxmcczhnol3NJrQFakOhDheKJdCCHxGh4Rk572mqsACYtdtlarIhHLIq6Ju7cttwF2r+uhS5gmFyO3R9ml4pyg5I7f/eA2eBo0bFWOY3eLyoky5njSKlbqADcKCnfknHcD+GxIdSEk3ITdL9xOB7Ms6qiyHaiPx6yG67TY7elH3S12WOXrLHbOvaN8dDeZesPqfOyDJYvdKxROts7isYirxe6V4yNXVIswb4vdtKLVugaxOMUjvyXswmKvUyx2s4Pz+v5uDypC8PW52Z3lydWWjxGhsoDTx64z2YWLxW9gXz94ygJ30GLmaVWUuYZ3RiNOi10XMVRMaOZpmaK2V5HbJR9hF6hiWZ+IIRJhljWoWuwiX7ZIbNXg4hsUHYeYoBQkba/tPFqLXW/xylb2oLq4GauddJ3FymG3iGWLXRXLID72fImw7H91UNFr4o14ihC7ZF0xWZ+9G0LYxRqf8VgEjAH9pVC8KvOpDPBxxbicR2zW5d/RWuyyK0ay2BNVEXcfu+a84mnNK20GIK67Gu7JA1vsSTPNbzyqX8YPMAyTsGZvh0V51aaPc9/ra3HNI4uw/UCH02LvEhZ7/jHnqlj2MwVGnEq12IVV84c5RgSF3yITiVgEm/d2oDudcdw4XoZVEB+7EDCRYRAwhKg+EUNLZ8pz9fdYRC/sql8+TItdJWINBDqvo9dju3odRISHEHYvd4UQ9rlr9gAwrkN9PIZENPs7xyIRq2PxCq10e0Lo8si/o2svzMVij0cj2NfejZ889741prO3rRuPLtioHTwdagq73yQp9WlNt92LAx1JzP5gJxJVUctvLsZ2rLKikdBmb4cFpe0tE9IZjp+/sBKAYWF9+4LJts9vmH4YgMIs9mNG98cxo/vjPTOn+lAz691DXzoJTy3agqvPHo/lWw/gc6ceiheX77BuhLXNbZh2+DAMrI3jpo9MwgfbW3CwK2WFTgpkiy8RiyBlChaH0yq9eMpInD7RCKBSrZ1xQ+rwi08dY9ums0wTsQgaqo1JOMJi/M4Fh+OeV9dY6WIjzH5sIhZFPBrB+UcNx5UnH2or74IpI/CXBRut61MoV0wdg1PGG9kahfWZ4RwXThmJpxdvxZdOb8SjCzbh8JH28LffX3kCrn10MY4dMwCTR/TDxceMxCePHwUg2wmfPmEIVmxrwa0XHeF6/sOG19tcHOdMHob6RMwmQrEoww3TD8OWfR24/KQx+MWLK7VluWWtPGRANc6dPBT/Pe0wx2e6MRm7j90+ePrqqma8uqoZk4Zn0znf+sxy29hHbTyKC44agVPGD8a/39+Jq88ej9dW7bLS+erqrUsC5mVhjxpQgzGDajC4LoGZy7ajtTOFi6aMwBdPG4f27jQOHVyHx97Ohv1GGXPcl/d97kTX8osBCXuZILsFkmn7AOQp4wfh0MHmpA6zAd1w3mGeE5B0TBzWD//87zNw3I/+jf3tSVx37kQAxg1/zmQjG8RT154GALjkuFG473XjZjltwmA8+EVj7tk3zsvewI0zZtrKb5AstJp41JbvRX4C+ewpY/GT/5hivVefQh7+8lSMUULvdAZWPBaxYu+H1hurz197zgQcPrIfvvSnd4x9ohHbTVwVZWCM4b7PNTnKa6iuwtPXnoaJt77gPFke/PyT2c5J1D/DjfM8fvWpAIALjh7pOO7CKSOx4faLrff3fOYE67W4jrWJKJ4wy3CjoboKD395Kv7r/vnmuUbggqNH2FIQVEUjOHRwHZ64xijrq2eMwwNvrg/8HWPRCP70panaz4TFXp+I4fsfPxI3P/WezRVjs9gl0VVdHrLB/v6PLrBef9qM/T+pcRBuPv9wR3sEDMvceBLrUqJi3L/ToLo4/nbVqfjZ8x8AMNI3X2/eK3/92im4Q+n85El+048Yjge+4GxbxYZcMWWC7DuORphtUMgmTOYNUIhLRjy++rlWgvhxZWQLTR2olb+P6npQIwp0j8m6KiRiUVPYk0hKixTL/uBYNIKodK28XDa6uoRF1hVTWNCYONwrzDF7Tn3UiByyqbrMgq7iFATRvjqS6Wwbkk5XpYx9CNQMnl5L9fkRYVLKZJsrxv13Fk80Iu2GOvaiXlOjvYqcRXlXNVRI2MsEefAvGmE2C9fmJzQbXSECJAac/NfvzEZyBKGfYrELOOc2q0tdlk/tpHTn69YIciIWQb/qKjPneMa6IeWOqCpqX0DDb7CtpxA6UuhaxuLwIH0tY0yb0VCOCFFdCGHl5geyHX06k13fVq627N6Rz6uGoXotQu5HhDHLlSNfC6/BU2EYiCcHdexFvaZGZJF5bJkoOwl7mSBbklXRiE0I5cYirPd808cCWevZ12KXIjmCIFvs6kBcmnNrW5sq7Ir1pBMt3YLC8WjWFZNMc+vayMXFo/ZcOH6DbT2F7GMvCPNwr6eomDSw6pavxdpX6VSDJgoLguyaE08Ybr56WSzD7HwjjFnrsMp4CbCoohB2ddlEncUuftegT7c9DQl7mSDPtoswd4tduGLCaEBuE46seuToipEHuVRhz3BuLQTdruRhDxJTfLDLmREvEmGWKyaVzliWf0RxxcidoC6HeDGIhCTsQSbvyL+b30xldRCxJyx2Gbdqyx3Q/vbwsh9GI8yaCyD/9l6Gkeh8Ok0DRG3LDmGX7teg8fE9DQl7mSCH3sUizPbILjfCKuumLfycfhZ7JEdXjHzXqlZOR3faCq/0nZWn0b6DLqvdN5iumH3t3VY9bR1hlNl87F4Jo3oSUaWQDHZPD7twJUQCWOyqGyxMYa+Wzs19fNBqQrCwiLDswipyemMvARafCIvdIewa16F4Ci6XqEcS9jIhqfjYv/Dg27b3AuETLcQyEAmc/EInrcRVAc8l5yCplgRibXMbth/otGY8Ng7Rp7YV6MRlsEvSpn7VVUhlOGZ/sMt6YpCrWxW15xIfPdD73D3FoWaUz8CAyafcEIna3CaLAZIrBswWs65DvdaHaBKyyeQyaG+lKk7ErJQSbq4Yt1WtckGXAz0SYTjMTBZnm3EboE2PMdvKUGUxbPUpJxZlVtljB9sT6ZUKCncsE+QBI9XvKQu7EC/5BvntFcdj1IBqcA58sKMV3zPTkX7/40fivMPt+b0B4JnrT8Pmve2O7SpC991cMXNnTMOeg9kFgY8dMwADa6uwrz2pTaj1jfMOQ4QxHDvGmcdb8NevnuzIlggAnz3lUIzsX42rHjHS3z74RSOkTH7qEOGF6uBpXSKGv371ZDQf7ML5R+kyUNuZ+Y0z0C/hLpwvffOsnDvWb5x3GI4ZPQBnTxrqv7MHP7rkaFx8zEhM8kj9Kurm5WN/7GunoKUz6RgYPGfSUDzw+SYMa0hgQI39d/jH9afbctkHQRzzpjnnQb1qM79xBhqqq8CYkfL4Fy+uzHuwdNaNZ2Hzvg586vdvWdsijOGqs8ZjwtA6TBxWb01wk++ph750Ev4wZ501kUv4+H/wiaNw4ZQRmDzCfq2d92cEZx02BA98vgnnTC7s9w0LEvYyQbbYVSGVG6GY4SdH0Xzi2EOs102Ngyxhv6xpjDYn9rB+1RjWz39pLiY90usYNaDGkXL3vCOG46lFW1CticY46pAGDK73FobTJuqzPkcjDB+VRPmEsUZOdSHsjMHKs273sTPPcnUcdYh7xwMgr3zasWgE0490drK5UhOPWnMO3LDCVCPuPvZTJwzWbmeMudZTTvscFHGMZbYobUm+1l86fRzeXL0bL6/MbyG2YQ3VVppnGdF2Nu3JGjOy1X3O5GG2dQZE1JbbtVZdk1HGPK9bKSBXTJkg+9jVxEbyY6MYgGzzWAhaUGiEg+Vjz2GgVlRVZ7H7Ddbmguh0RMimWxRRITN1KxUr3QDKZzBPKLtf/L3fuE8hyGMtahi7fJn8Uu6qbpxyCXGU6XutvkyRo2LUwUV5MoWwwN0GE2UKCYkE5AiMHPyq5o2rm+gS5sCcuJn0GQTluP++18RFR1wmkXcA/AdPBWF2/oB9Qph8P6h+crnN+GVm1PnYy42+1+rLFDmOXZ3AI2uTmAUXxGLPRZB1iJsiF20Up/Rbq7NQxD3ql4+kkBm6lYro9IKsVVssrBmzvsLecxa7fG5HWmWpjftFbUWVNlWOK1CQsJcJsiumrdsu2rKFUGdZ7D2/IK5w4+cSM2/lfvcJsysUUSe/DIJ92RXjtbhJscmGafq5YkK22KXX8rnVp1nZCPJzxagT6kqde11H32v1ZYq8Zqc6QUO2LoSQFUPY85l0IXbtKUHN5jU3feyasD/5Bu7Lwq4uMVdKglrsDTXFiedwWwgFCJJPyH5sR3fP34u50vdafZkiW+xijUqBbF0cb0aDXHbiaNQnYvh002hHWZ88YVQoi0Zkp0kHP0bcILEIw5D6BKqrIogw4D/NtLNuTBhah9Mn6iM1ZP7f+UY6Y3Fjiu958/nZNMeyeIgMgH2Jq88aDwBWhMjI/tU4/6jSRmwIH7ufe3Di0HrPz4NwuBSeOFyKkhGdxnXnTHQI+2UnZu+jG85zpiCWOWyYvY5+UVSlgMIdywQ5fLFLmR0pW8zDG6qtlK7Lf6iPyf7Vp48DPl14nTLWbLpcXDHGf845Fnz3PKMc7r9izcvfOidQ+dedMxHXnTNROh+zpbiVmTisvuC48Urk8qljcfnUsdb7ebecV8LaGGQCWuwnjx+M1T+90HrS0qXi9ePFG8/Sbk/EolZbUdcHOGvSUNd2pDK4PoF7P3sirvnLInzkyOGOFNPlAAl7mZCUGpo67b3Q6JZ8SVs3Yy5RMQYZnrWqowFSzIaJOButq15GiAirALsWw31WaBiocHOV6+A8uWLKBGGx68LzShUnm42KyX3wtJSSaj01lLAOhJ1c0g1XAsJ1Wm5rnQrKs1Z9ENFQdNEkJbPYM/n72EtrLfcS9ehF5LJASCUgZoqXYww7QMJeNoioGN3EnlLNHhTeoXyiYkqp65ZVSCZ72cADpBuuJERuJzX0sVwoz1r1QYTFXl1GFns+iweIXQteUKIASNfLjyDphisJ4TqtipXnN6LB0zLgjhdX4nevGQtH65Yy81qfsScRA0O1OcwijZSBj134PWtDCPkkwkGkk1Dz9OdCOVn74im2TpNkrxwoz1r1MYSoA/aZlGdPGoqR/asx/QjvbH49xedPbURLRwpfM+OigxDaEnAFMGZQDWZceLgt6yVRWi47cQx2HOjEtedMyOm4Bz7fhFiUYfO+Dpw8blCodbrzsmMxaXh+cfP5fp9iQcJeZsjpbv/vM8eHPsU6p7pURa0JQUFhZeFjZ7jm7PK84foq8VgE3/pobm0JQI+mwr30ROfkvqDk+32KBfnYywzdwtWVBMWQE0TpqTzl6OXIwl6OeZ79yC7aXOKKEEQfpiBXDGPscQDieWQAgP2c8+MKrlUfo7oqYi0HZs8ZXYnCbvwng50gSkdBws45/y/xmjF2F4ADBdeoD1JdFbWEXbbSy2b1mxwoh8FTgujrhDJ4yoy7+dMApoVRXl9hbfNBdCUztjS9YiZbBWo6gHKZeUoQfZuwomLOBLCTc77abQfG2FUArgKAsWPHuu3WZ+hOZXDeXa/bth05ssGaxl+pPuopoxsAAEePKr9UpgTRV/AVdsbYbAC6/LC3cs6fNV9fAeAxr3I45/cDuB8AmpqaKlS2wqMrZV915dITR+NHlxyF97e1YNaKnSWqVeFMO3w43vzOuRg9sPxSmRJEX8FX2Dnn070+Z4zFAHwSwIlhVaovkFJWaRnekEBtPIaBdfES1Sg8SNQJorSEEe44HcBKzvmWEMrqMyQzas5146dIxCgClSCIwghDRS6HjxuGcKJa7CIvS5yEnSCIAil48JRz/sUQ6tHnUIU9FhUWOyWuIgiiMMg8LBFOV4xhsZMrhiCIQqkoFclkOH7171XYfbCr1FUpGNViFxN7dEvjEQRB5EJFqciC9Xvx21fWYMbf3yt1VQpGLK0lEKumV+JsU4IgyouKEnYxeae9O+2zZ/mTUmYgpWmmJkEQIVFRwl4Oub7DwmGx94YvRRBEWVBRwt6bcHPFEARBFEpFCXt2keLKF0F18JR0nSCIsKgoYUcvcsWklHDHNCk7QRAhUVHCzkxlD0sC0xmO7lTGf8ceIKlY7JTmliCIsKgsYTct9rfX78XiTfus7W+u3o3GGTOxfOsBNM6YiT+/tQHPLtmKxhkzPWPeL733LRz9g1lo704FOv8Fv3kDF/92Dtq7U2icMRMPzV2f93dRXTFDG6rzLosgCEImrHzsReffK3bihLEDAQD/XLoVADBz2XYAwINz16N/TRUAYPPedgypT2jLeHfTfgDA/vYkauP+l2LljlYAQEuH0RHc89pafPH0cXnVX3XFXDk1m6P+3988CxTNThBEvlSssOtcFyJkkAFo6zLEty7h/xW7cnTHmIkY0d4VzNLXIbtizjxsiG1i0qTh/fIulyAIoqJcMXJIoHaw0dzEGEOHOYkpiOWbq5/dmiiVzH+iVCpdGt8+QRC9n4oSdnl2poeugyEruuoMTx3qakZ+CP94IeOdSYqCIQiih6gsYc/Iwq5xxYjPWTbtQJAwwnwt9kIgi50giJ6iooQ9w72FXVjnEcYssQ5msecmskHK9C0jTRY7QRA9Q0UJu2zk6qzmN9fsBmD3q8v7Ldq4D4s37cP8dXtsg6+5Wuy6TmXO6mbsb++2beOc48Xl27XW+Rurm3M6J0EQRFAqTNizAqkzmtfsOgggG+9uHJPd8VO/fwuf/N1buPz++bbt+frYBa2dSXzuj2/jaw8vtG1/cfkOXPOXxbjvjXWOMhas35vTOQmCIIJSYcKefS1HyKgGNJNsdjVePLtdFvbCfOydSeP4dc1ttu3N5uSoHQc6HWUwABOG1uV0XoIgiCBUlrBLCu6Vv9zNYpfplnqJ3H3s+jwvUWWRDGvxDE3MZYZzVFfR+qYEQYRPRQl7xicqRveZm7B3JfMXdvXcIgVvlbKsnRWkw+zKzjlHMs2tBawJgiDCpKKUxRbu6BGZ0taV9Zm7CXunNLko18FT1ccufPQOi51no3RkRJ2qaBk8giB6gMoSdp8JSoLWzqT1WvjS1RQEHZKw5zp4qnYWwuKPRVXL3Piv6reok7o/QRBEGFSUsMtWutugKAAclHK4CBFWY887uguw2N2E3c1iV7a7uW4IgiDCoKKSgMkW+/PLduCZd7egtTOFJxdtse0n6+51jy7GoYNrsXFPu22fS+6Za73+zezV+M3s1fj6tIn45vRJ+PKf38GQ+gTuvOxY3PzkUjy5aAv++IWmbD0UYb/+0cUAgFgkgp8//wHue2MdfvGpKZKP3RDzKx9YgG99ZJKV5EsMntbQICpBECFSUSaj6lefu2YPbnt2he9xqqi78b+vrEFHMo3XVjXjKbOzEJ3GV/6cjVFP20ItObab4YxVUWbFrH/n78tsPvZt+zvw9vq9uPmp95A0nzbOPGwIvjFtIn7+ySmB6kcQBBGEihJ2WVDHD6nLOZoFAI4Z3d/2/oyJQ2zv5YFRdcFpax+XLJNBwx3FOeLRCG766GQMdskXTxAEkQ+VJeySwR6PRdCVR9pcNXZ8UF3c9j4p+e53tepXX5IFXxZ5NXwxYw2e2pVdCDuFOxIE0RNUlLLIrphELGKbZBQU1Z/tEHapzG37O7RlyIOtNmF3GTxljNlmx3Zbg6cUFUMQRPgUJOyMseMYY/MZY0sYYwsZY1PDqpgOefA0EYvaJhkFRRX2gbV2YRfL3gEewi7PWpWeGlQLnPOsK0aO4hGvY5GK6lcJgqgQClWWOwD8kHN+HIDbzPc9huzPjsciOcefA0BNXLXYq2zv97ZlMzRuDWCxd0qvnRa78T/CmG0pPOGKIYudIIieoFBh5wAazNf9AWwrsDxPVFfMhzsP5lyG6mNvqLEL+z4p9e6rK3dpy5DFX57BurPFnuwrJQ2eWqsugePdTfsAUBw7QRA9Q6Fx7DcCmMUYuxNGJ3Fa4VVyR3bFxKLMNhEpKKorZogSkSKL9jsb9mnLuPvl1dZr2R20YluLbT+xoDZjzBqU3by3A98zQzRp5ilBED2Br8nIGJvNGFuu+bsEwLUAvsk5HwPgmwD+6FHOVaYffmFzc36LTAiLffH3PmJzbQDAiIZqPPf1M7THvXDDmdbrmrj9Kw9vSGDujGlWLHlbjp1Fp4c7SKQtSGe4dsUk8rETBNET+FrsnPPpbp8xxh4GcIP59kkAD3iUcz+A+wGgqakpr3Xh0pwjGmEYVBd3pAEYO6gWR4/qrz3uiJEN1mvVYo9Hoxg1oAaTRxizQYWwD6mPY/dB+4pIOjo9Qi7twu4c6CUfO0EQPUGhJuM2AGebr6cBWO2xb8GkM0DUjAlXB07jsWBfRfWxJ6qM4xLm8W1mDpnaeDAvlVdkTrvZSWQ414ZmUhw7QRA9QaE+9q8BuJsxFgPQCeCqwqvkToZzCO+FarEnAgq7GhUTjyrCbopxXSKgsHu4YkQn4e6KIYudIIjwKUjYOedvAjgxpLr4ks5wyWK3C3tQi111xWQtdmO7EOO6eLDEXJ0eFrvoJNKca7NRUlQMQRA9QUUpSzrDrRS4+VrsagcgLPZ4nha7l4+93ewkMhnuGOwFvFeBIgiCyJeKEvaMOXgK5G+xqyl3Y66umGAW+4ynl7l+tn63sbj1H+asx5zVzkigjjxy3RAEQfhRUcI+sDaOQwfVAgDuvOxYAMBJjQNx1CENuO6ciQBghS1ec/YEXDxlpLXf9edOwLGj++PMw4bixEMHOsrOumIMYReW/PFjB1j7CDfOoLo4qqtyu3RPLLTnjD9uzAAcKUXrEARBhAVTl4wrBk1NTXzhwoX+O/YgjTNmAgA23H4xAMOSn/Dd5zFuSB3W727DpSeOxlOLtuD6cyfg5vMPdxw/d81uXPnAgrzOXV0VwcofX5h/5QmC6JMwxhZxzpv89qsoi70niUYYYpHsbFa/GHM193ouiKcDgiCInoCEXSIei1ix52JWqNsDTSHCHnQ8gCAIIh9IYSQSsYgV7uiXx0VdPCPX8xAEQfQUpDASsiXtF2PutmxeruchCIIIG1IYCdn37TcrtKM7/1BF8rETBNGTkLBL1JqzTRkDRg2sAQAMb6jW7qvmnMkFcsUQBNGTFJorpmJ54YYzHX7yH11yNBZv2odDB9Xi/KNGoH9NFS48eqT2+FPGD8LvrjwBp08cgvnr9uDqRxYBAH58yVEYVJdA/5oqbNnXrp3ARK4YgiB6kj4r7EdoJgdNHTcIU8cNst5/7JhDXI9njOGiKYbo+OtTQAAABp5JREFUn3/UCGv7ZU1jbNa8TtjJYicIoichhQmZIIm9SNgJguhJSGFCJkh8O7liCILoSUhhSkAhMfAEQRB+kLATBEH0MkjYCYIgehkk7CFx2YmjtdsnDqvH5OHGQtn9Ai7eQRAEUQikNCHxy8uOxS/N3O8ys28623r99OItuOmJpcWsFkEQfRCy2ItIIRkhCYIggkLCThAE0csgYScIguhlkLATBEH0MkjYCYIgehkk7EVELLdXSMpfgiAIPyjcsYicf9RwXH32eFx79oRSV4UgiF4MCXsRiUUjuOXCI0pdDYIgejnkiiEIguhlkLATBEH0MgoSdsbYsYyxeYyxZYyxfzHGnMsSEQRBEEWlUIv9AQAzOOdTADwD4ObCq0QQBEEUQqHCPgnAG+brlwB8qsDyCIIgiAIpVNhXALjEfH0ZgDEFlkcQBEEUiG+4I2NsNoARmo9uBfBlAL9ljH0PwD8BdHuUcxWAqwBg7NixeVWWIAiC8IdxzsMpiLFJAP7COZ/qt29TUxNfuHBhKOclCILoKzDGFnHOm3z3K0TYGWPDOOe7GGMRAA8BeI1z/mCA45oBbMzztEMA7M7z2FJBdS4OlVbnSqsvQHUuFm51PpRzPtTv4EJnnl7BGLvefP00gD8FOShIxdxgjC0M0mOVE1Tn4lBpda60+gJU52JRaJ0LEnbO+d0A7i6kDIIgCCJcaOYpQRBEL6MShf3+UlcgD6jOxaHS6lxp9QWozsWioDqHFhVDEARBlAeVaLETBEEQHpCwEwRB9DIqStgZYxcwxlYxxtYwxmaUuj4CxtiDjLFdjLHl0rZBjLGXGGOrzf8Dze2MMfZb8zu8xxg7oQT1HcMYe5Ux9j5jbAVj7IYKqHM1Y+xtxthSs84/NLePY4wtMOv2OGMsbm5PmO/XmJ83FrvOUt2jjLF3GWPPVUKdGWMbzIytSxhjC81t5dw2BjDGnmKMrWSMfcAYO7XM6zvZvLbir4UxdmOodeacV8QfgCiAtQDGA4gDWArgyFLXy6zbWQBOALBc2nYHjMyXADADwC/M1xcBeAEAA3AKgAUlqO9IACeYr/sB+BDAkWVeZwag3nxdBWCBWZcnAFxubr8XwLXm6+sA3Gu+vhzA4yVsHzcB+CuA58z3ZV1nABsADFG2lXPb+DOAr5qv4wAGlHN9lbpHAewAcGiYdS7ZF8rjApwKYJb0/hYAt5S6XlJ9GhVhXwVgpPl6JIBV5uv7AFyh26+EdX8WwEcqpc4AagEsBnAyjNl5MbWNAJgF4FTzdczcj5WgrqMBvAxgGoDnzJuz3OusE/aybBsA+gNYr16ncq2vpv4fBTA37DpXkitmFIDN0vst5rZyZTjnfLv5egeA4ebrsvoe5uP+8TAs4LKus+nSWAJgF4w00WsB7OecpzT1supsfn4AwODi1hgA8BsA3waQMd8PRvnXmQP4N2NsETOS9wHl2zbGAWgG8CfT3fUAY6wO5VtflcsBPGa+Dq3OlSTsFQs3utmyiytljNUD+DuAGznnLfJn5Vhnznmac34cDCt4KoDDS1wlTxhjHwOwi3O+qNR1yZEzOOcnALgQwPWMsbPkD8usbcRguEF/zzk/HkAbDDeGRZnV18IcW/kEgCfVzwqtcyUJ+1bY872PNreVKzsZYyMBwPy/y9xeFt+DMVYFQ9Qf5Zw/bW4u6zoLOOf7AbwKw40xgDEmUmPI9bLqbH7eH8CeIlf1dACfYIxtAPA3GO6Yu1HedQbnfKv5fxeMldGmonzbxhYAWzjnC8z3T8EQ+nKtr8yFABZzznea70OrcyUJ+zsADjMjCuIwHmH+WeI6efFPAF8wX38Bhh9bbP+8OdJ9CoAD0uNXUWCMMQB/BPAB5/xX0kflXOehjLEB5usaGGMCH8AQ+Etd6iy+y6UAXjGtoKLBOb+Fcz6ac94Io72+wjm/EmVcZ8ZYHWOsn3gNwwe8HGXaNjjnOwBsZoxNNjedB+D9cq2vwhXIumGAMOtcqkGDPAcaLoIRwbEWwK2lro9Ur8cAbAeQhGFBfAWGb/RlAKsBzAYwyNyXAbjH/A7LADSVoL5nwHjMew/AEvPvojKv8zEA3jXrvBzAbeb28QDeBrAGxiNtwtxebb5fY34+vsRt5Bxko2LKts5m3ZaafyvEfVbmbeM4AAvNtvEPAAPLub5mPepgPI31l7aFVmdKKUAQBNHLqCRXDEEQBBEAEnaCIIheBgk7QRBEL4OEnSAIopdBwk4QBNHLIGEnCILoZZCwEwRB9DL+P+4OjG47fch1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(stats.get_fitness_stat(np.max))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbb92ced150>]"
      ]
     },
     "execution_count": 55,
     "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.plot(pd.Series(stats.get_fitness_mean()).rolling(10).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "105000000"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "700 * 50 * 20 * 150"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using python-neat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import neat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fc_config_filename = 'fc.config'\n",
    "num_workers = 10\n",
    "eval_episodes = 20\n",
    "population_size = 20\n",
    "\n",
    "actors = [Evaluator.remote(env_name=env_name) for _ in range(num_workers)]\n",
    "pool = ray.util.ActorPool(actors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "config_initial = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n",
    "                             neat.DefaultSpeciesSet, neat.DefaultStagnation, fc_config_filename)\n",
    "\n",
    "config_initial.genome_config.num_inputs = env.observation_space.shape[0]\n",
    "config_initial.genome_config.num_outputs = env.action_space.shape[0]\n",
    "config_initial.pop_size = population_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "game_fc_config_filename = 'fc-1.config'\n",
    "\n",
    "config_initial.save(game_fc_config_filename)\n",
    "\n",
    "config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,\n",
    "                     neat.DefaultSpeciesSet, neat.DefaultStagnation, game_fc_config_filename)\n",
    "\n",
    "# Create the population, which is the top-level object for a NEAT run.\n",
    "p = neat.Population(config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add a stdout reporter to show progress in the terminal.\n",
    "p.add_reporter(neat.StdOutReporter(True))\n",
    "stats = neat.StatisticsReporter()\n",
    "p.add_reporter(stats)\n",
    "p.add_reporter(neat.Checkpointer(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "def evaluate_genome(genome, config):\n",
    "    network = neat.nn.FeedForwardNetwork.create(genome, config)\n",
    "    net_policy = lambda obs, network=network: network.activate(obs)\n",
    "    p = ray.put(net_policy)\n",
    "    pool = ray.util.ActorPool(actors)\n",
    "    R = compute_rewards(pool, p, total_episodes=50)\n",
    "    fitness = np.mean(R)\n",
    "    return fitness\n",
    "\n",
    "def evaluate_genomes(genomes, config):\n",
    "    nets = []\n",
    "    for gid, g in genomes:\n",
    "        net = neat.nn.FeedForwardNetwork.create(g, config)\n",
    "        nets.append(lambda x, net=net: net.activate(x))\n",
    "    policies = [ray.put(net) for net in nets]\n",
    "    policies = [net for net in policies for _ in range(eval_episodes)]\n",
    "    rs = rewards(pool, policies)\n",
    "    rs = np.array(rs).reshape((len(genomes), eval_episodes))\n",
    "    rs = np.mean(rs, axis=1)\n",
    "    for (gid, g), r in zip(genomes, rs):\n",
    "        g.fitness = r\n",
    "    return rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " ****** Running generation 0 ****** \n",
      "\n",
      "Population's average fitness: -9.66667 stdev: 0.82999\n",
      "Best fitness: -6.00000 - size: (18, 415) - species 49 - id 49\n",
      "Average adjusted fitness: 0.083\n",
      "Mean genetic distance 2.372, standard deviation 0.477\n",
      "Population of 180 members in 60 species:\n",
      "   ID   age  size  fitness  adj fit  stag\n",
      "  ====  ===  ====  =======  =======  ====\n",
      "     1    0     3    -10.0    0.000     0\n",
      "     2    0     3    -10.0    0.000     0\n",
      "     3    0     3    -10.0    0.000     0\n",
      "     4    0     3    -10.0    0.000     0\n",
      "     5    0     3    -10.0    0.000     0\n",
      "     6    0     3     -8.0    0.500     0\n",
      "     7    0     3    -10.0    0.000     0\n",
      "     8    0     3    -10.0    0.000     0\n",
      "     9    0     3    -10.0    0.000     0\n",
      "    10    0     3    -10.0    0.000     0\n",
      "    11    0     3    -10.0    0.000     0\n",
      "    12    0     3    -10.0    0.000     0\n",
      "    13    0     3    -10.0    0.000     0\n",
      "    14    0     3    -10.0    0.000     0\n",
      "    15    0     3    -10.0    0.000     0\n",
      "    16    0     3    -10.0    0.000     0\n",
      "    17    0     3    -10.0    0.000     0\n",
      "    18    0     3    -10.0    0.000     0\n",
      "    19    0     3     -8.0    0.500     0\n",
      "    20    0     3     -8.0    0.500     0\n",
      "    21    0     3    -10.0    0.000     0\n",
      "    22    0     3    -10.0    0.000     0\n",
      "    23    0     3    -10.0    0.000     0\n",
      "    24    0     3    -10.0    0.000     0\n",
      "    25    0     3    -10.0    0.000     0\n",
      "    26    0     3    -10.0    0.000     0\n",
      "    27    0     3    -10.0    0.000     0\n",
      "    28    0     3     -8.0    0.500     0\n",
      "    29    0     3     -8.0    0.500     0\n",
      "    30    0     3    -10.0    0.000     0\n",
      "    31    0     3    -10.0    0.000     0\n",
      "    32    0     3    -10.0    0.000     0\n",
      "    33    0     3     -8.0    0.500     0\n",
      "    34    0     3    -10.0    0.000     0\n",
      "    35    0     3    -10.0    0.000     0\n",
      "    36    0     3    -10.0    0.000     0\n",
      "    37    0     3    -10.0    0.000     0\n",
      "    38    0     3    -10.0    0.000     0\n",
      "    39    0     3    -10.0    0.000     0\n",
      "    40    0     3     -8.0    0.500     0\n",
      "    41    0     3    -10.0    0.000     0\n",
      "    42    0     3    -10.0    0.000     0\n",
      "    43    0     3    -10.0    0.000     0\n",
      "    44    0     3    -10.0    0.000     0\n",
      "    45    0     3    -10.0    0.000     0\n",
      "    46    0     3    -10.0    0.000     0\n",
      "    47    0     3    -10.0    0.000     0\n",
      "    48    0     3    -10.0    0.000     0\n",
      "    49    0     3     -6.0    1.000     0\n",
      "    50    0     3    -10.0    0.000     0\n",
      "    51    0     3    -10.0    0.000     0\n",
      "    52    0     3    -10.0    0.000     0\n",
      "    53    0     3    -10.0    0.000     0\n",
      "    54    0     3    -10.0    0.000     0\n",
      "    55    0     3    -10.0    0.000     0\n",
      "    56    0     3    -10.0    0.000     0\n",
      "    57    0     3     -8.0    0.500     0\n",
      "    58    0     3    -10.0    0.000     0\n",
      "    59    0     3    -10.0    0.000     0\n",
      "    60    0     3    -10.0    0.000     0\n",
      "Total extinctions: 0\n",
      "Generation time: 96.843 sec\n",
      "Saving checkpoint to neat-checkpoint-0\n",
      "\n",
      " ****** Running generation 1 ****** \n",
      "\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-47-c30b26fe7e04>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mwinner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_genomes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m33\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/git/neat-python/neat/population.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fitness_function, n)\u001b[0m\n\u001b[1;32m     86\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m             \u001b[0;31m# Evaluate all genomes using the user-provided function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 88\u001b[0;31m             \u001b[0mfitness_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpopulation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     90\u001b[0m             \u001b[0;31m# Gather and report statistics.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-44-664f869b689b>\u001b[0m in \u001b[0;36mevaluate_genomes\u001b[0;34m(genomes, config)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mpolicies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mput\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnet\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnet\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnets\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mpolicies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mnet\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mnet\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpolicies\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0meval_episodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpolicies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgenomes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meval_episodes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0mrs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-24-0b5419b2b102>\u001b[0m in \u001b[0;36mrewards\u001b[0;34m(pool, ps)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;34m\"\"\"Rewards for an array of policies.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepisode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_rewards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_episodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-24-0b5419b2b102>\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;34m\"\"\"Rewards for an array of policies.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mpool\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mepisode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremote\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_rewards\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpool\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtotal_episodes\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/chai3.8/lib/python3.8/site-packages/ray/util/actor_pool.py\u001b[0m in \u001b[0;36mmap\u001b[0;34m(self, fn, values)\u001b[0m\n\u001b[1;32m     59\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhas_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 61\u001b[0;31m             \u001b[0;32myield\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_next\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmap_unordered\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/chai3.8/lib/python3.8/site-packages/ray/util/actor_pool.py\u001b[0m in \u001b[0;36mget_next\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_future_to_actor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_return_actor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget_next_unordered\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/chai3.8/lib/python3.8/site-packages/ray/worker.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(object_ids, timeout)\u001b[0m\n\u001b[1;32m   1465\u001b[0m         \u001b[0;32mglobal\u001b[0m \u001b[0mlast_task_error_raise_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1466\u001b[0m         \u001b[0;31m# TODO(ujvl): Consider how to allow user to retrieve the ready objects.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1467\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mworker\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1468\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1469\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRayError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/chai3.8/lib/python3.8/site-packages/ray/worker.py\u001b[0m in \u001b[0;36mget_objects\u001b[0;34m(self, object_ids, timeout)\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    303\u001b[0m         \u001b[0mtimeout_ms\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 304\u001b[0;31m         data_metadata_pairs = self.core_worker.get_objects(\n\u001b[0m\u001b[1;32m    305\u001b[0m             object_ids, self.current_task_id, timeout_ms)\n\u001b[1;32m    306\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdeserialize_objects\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_metadata_pairs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "winner = p.run(evaluate_genomes, 33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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