paper-figures.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nengo\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gym\n",
    "from nengo.utils.matplotlib import rasterplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_actor=40\n",
    "lateral_sigma = 3\n",
    "lateral_inhibition = 0.1\n",
    "\n",
    "lateral_weights = np.zeros((n_actor, n_actor))\n",
    "for k in range(n_actor):\n",
    "    for k_p in range(n_actor):\n",
    "        if k == k_p: continue # no self connection\n",
    "        lateral_weight = (1+lateral_inhibition)*np.exp(-((k - k_p)/lateral_sigma)**2) - lateral_inhibition\n",
    "        lateral_weights[k,k_p] = lateral_weight\n",
    "\n",
    "# plt.rcParams.update({'font.size': 12})\n",
    "fig, ax = plt.subplots(1,2,figsize=(8,3))\n",
    "\n",
    "ax[0].scatter(np.arange(n_actor) - int(n_actor/2), lateral_weights[20])\n",
    "ax[0].set_xlabel(r\"$k-k'$\")\n",
    "ax[0].set_ylabel(r\"$w_{kk'}$\")\n",
    "blah = ax[1].imshow(lateral_weights)\n",
    "cbar = fig.colorbar(blah)\n",
    "ax[1].set_xlabel('Actor neuron index')\n",
    "ax[1].set_ylabel('Actor neuron index')\n",
    "# ax[1].set_title('Lateral connection weights')\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'a(t)')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "values = np.load('cache/values.npy')\n",
    "actions = np.load('cache/actions.npy')\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,4))\n",
    "\n",
    "ax[0].plot(values, label='value')\n",
    "ax[0].set_title('Value')\n",
    "ax[0].set_ylabel('V(x(t))')\n",
    "ax[0].set_xlabel('t')\n",
    "\n",
    "ax[1].plot(actions)\n",
    "ax[1].set_title(f'Action')\n",
    "ax[1].set_xlabel('t')\n",
    "ax[1].set_ylabel('a(t)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'Trial')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "reward_history = np.load('cache/reward_history.npy')\n",
    "\n",
    "fig, ax = plt.subplots(1,2, figsize=(12,4))\n",
    "\n",
    "ax[0].plot(reward_history)\n",
    "ax[0].set_xlabel('Trial')\n",
    "ax[0].set_title('Total reward per trial')\n",
    "\n",
    "h = 200\n",
    "ax[1].plot(np.convolve(reward_history, np.ones(h)/h, 'valid'))\n",
    "ax[1].set_title(f'{h}-trial moving average of rewards')\n",
    "ax[1].set_xlabel('Trial')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11b173550>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iW4LXRaZkrHg8PRW4qfe7EjVffi/FivJ/PbX4PY33VSFrLljXfoeprqXL19eRAiD4/fKzWP3BJdNtC5fFDC118WveC8DZmrmudNeDlVvcwcvPRWioSvLZI7fzgx+89vee31RAD5WNDnLF6x9ISInFaaMHVj7fDgqs3a+Wi+u58cFyz0fC6N0LB0FjJ+pWPCc5ufCd+iZebWsqWxOXfyf2lmiPXt/7vVHekJnBjWK0Noqmv/wlIsECyYwX6aIfyZFwNYFVY9HQkGBsqILQOY1cs0vbljHm/qORzN05ShkdSXd5c3cfz5xeizZTnpbveu9Jjkw24TNKTPRUI3wOWlwluCFOJuvFdWXWN04wmIhRHcwyeKEOOVpCP+/F8QqshhLSnMY9+04wkKlg7JEW5L1zpCeC/PTug3ztR/vwzEo4OsgOBIddJt/k8uD2Izz8+E4iG2cJe4pMpoMEPCaOKzM7GMOYVSiFXJT6PJ01s/SM1VBdkaZkKySmQ0h5hfquGeKHanE8AgQ4hkBtyFMVzjJ5thpXF/gbM+TGgty78yTrfBN87kv3Y1YI9DkJeUeS7GSAUF2G9GQQ2W9hXPBS+aYJUgUP6Vk/0eoMD7Sc4d+f2k+sK0H+xUrybRY1jXPkn6jGuHOWTN7AnPOAImhvnWZ8LowQYKY84EjIeZnK4xKJDeA2FBEJg5ruGSbHo+xdd5EDR9YQbE5TOh7F8Qrato/Q218HApSAjRBQXZFmLuOjNO1DeB1CFTnyPRHqtkwyOhFDG9GxWkzkaZ39+85ycrqe5Jyf5roEEy/X4bYXsIsq6kxZsR9bGyc+F8DNq6hzKg1bJ5h4uQ7bJ3B9LndvO81jRzciByzWNE7hU0ucn65BkiA356W9ZZrhIw341ibZWD1B1jK49I0Oss0CIyHhnRZI744TMEyGJioui/vUqgJWXicYy+E4MvuaBnjixc0IXeAdU7ACAmRwPALX7xA6p5HpdCBgIcV1tMYc7kAA2+8iNIEStHAyGgjQY0Uqw1ncL1Yzfb/JmoYpLpxrQo0VsWc9hJtTJKeDNDTFmThfjYhaKLqDz2fivhhl47vOU2lk+f65jeBKyLqDcCTcvIoeMbEnfPzu3Q/zj71vIp/z4MZ1qjvj+PUSiW83ktmbR0x6EAoIv43scXBNhcoXNdIdsP2O8xz74XrsDWXRK7UmTlpDi5hI/T5sv8AN2SCg4qCG+84Ec+NhJFti08ZLeFSLI4e62bS9n5NDjVRXppkcifHBnQf40qE9oAk2dowy9O12XA2ybTbGrMIDbz/Et57ZSaRHYm6Di1Jp4swaeOpz2BeDdO4c4sLZJoQs0FJKWWQjIDggk20VqG1ZrJKKG9eRYiU8Z7203TPI2Z5G1nWPcfFgC7INdkcRMWXgel08kyqSA+ptc4Q8JhOzYb6977P8/qV3M/SDNswKgdtcwOczMc9GcBVYt3uQMydbMBIKVsDFOy3D7iT5SyGkahNpxINrCPS5sjjTnPYR6lHI784h9ftwVbBrXjFyCTpsXzfAue+vwTMjOP7Pv/3jLSa6URYokJdQMI5+c8Mi5Vv4hQpOPdW9ovLwauan2bC06CT5wd1EvlS+nhIJM/Bb6xdcf5HYaAkl9DxXXn/4j/bQ/CcHXhUzPdkId40y9PWNtPzk6Wvme+I766i7DlFH7z/fRvcvLhZFlB5vQX/r8kYH8wrJayl3Ex/eTexfVlDWviJumlfEK0/X49wxviDNtURWN8pyopV5rlTk32xSP72L8P85xNzP7Sb6xYNLiiAmvrOOiLfI2ExkgUJxKZarD/OiinkRyXLixYH/2EL7B06w7qjK+VuXNnm+kiu/h9dL+pEOQm/rX7Dv0p/vRl2bpjAWgIh1XaKrpbj45a1IUwYdv3NokZHH1UjbNyIOl59pqe9z4C930/6J5etL/aEgz7604bKIZrlvCkBtqMceG1+0f/iP98AtmQVle6379v/HFjo+cOLy9uBXNtP2/pPAQhHS/lNFvvDimy7X6R97MdHrwQgutkE3l5gaHu5tw+2+Mfvf0oj/8u9iYfE1jZ9+VezkJFO8/53PLjgu687CE9TlZaCtsVendsGdMwBU1ZfFJhdPNQJgjfoXn7gE7267PmsGxessub/Cs3I5Rc+Uq4E2pa2YLnffymIFz6FXRDKzZdFN/wsti9IU+0MrXuNGSe5e2WdBzSg39X5XYnvLoh/ZLg+oNO9isWEu62G0pxq979qiOHt46fqgdmQBmJ0pl932pqX1Onau/P4efvb62oaqUPa60l0P6edqFufHLxBCQnhdbmt/7RaQyqgH6su+Bb6TK5fjfEewHHZk5U6y2sggxV6tU3J2eev7pToCgPoXFtdJ3/g1xIRjC5/L633VJ8O0Xs3DFw7vA8/S3/nr5ceuM5CWKDPf0cUVQIlrOBO+G7p20y2vNvaus/hGU0dqF2x/6fRCOaeYuk4ZP3DuUv3l3zNjEQCSp8oyfammXLH37r4+s75Ds23XlS4YWNr56txj3SueJ95e7rjuvXtlBWcssLIjk/mKrLVz0ygASmlxGTuha49Yb4Rr6TG09psnF7+aQnX5+QqV5c/I713cCFRXpJFcCeU6/KScZRoqXSvvX99abnxcsXTD8sEdZaWnd/r6PuvlrvNaiLx5sf7ODdhEvhGgs32SsezKequVsCpt3FcGGNnO668/cnFxOdQ3ryxvP5Fo5E1dFy9vGzM33kTmajXs/oW6qkLtyvXU07FQxzCvdytn6ooBlC0hpVcetL1Wfuw6g2JycYNbrFxckL6uJErttb13ryRjrqw8im2bXrDtFheOKts3LbRakYorjDrTr/bm+lT5t1VVHjnGHit3bs+fWbNyhl+hf2ppxfDVJONLK0tL4ZWtONKZcqf6+ODaFdNV+1ZuWKPfLo9s/Vq5UXQ3Lk7viV2f9+j1ooyt3EE3RFM39X4L7v3Ko/gny+Wb7o0uShPSTfQ5Gfk62rDAhaXrp66WR4LnzjQDcOS5pd/TNy5uAcC9Lb3k8avxazfH+xggcbB20T49WGLiHouLQzVEPTf2rV6JNqMSulj+1iJnrt9P1je+uHmbPrl4BnMlBVvj8Hc2Xt6WX8Mg3Ddl0bp9dME+Y83K9bBwaaECefZU9eXfa+9+1XrKmNQw6t8Yj/ofC52Bv7tONHzwd3E8ULe5rDg0/CXMcT9ES/iDRXLDIYTu0tIxTUA3OdvfQH1Dgqmz1TghB6koI9kSTRsmCRlFRlNhkiMRPJMKZncBbcCLFXbxtaThYIR8nYu/LUWhJ4JoKuCYCoGzBvlGFzUjQXcOv9cknfHhOhIir1LTnCCeDGB4LH5t3dN8c3wbQy80s+muHjyKxYEXNuB6XJoeE8x+qKxINDMGisdB022skko4lCMxEWbzmmFOXmhGySjorVkMzSZ3PkpwQ5zERJjQWY3MVhNSGkiC6s44UyNRtm8Y4PC5dnwVedTnw5T2ZigmPLS0zTCVCmIN+1GbckSDeSYnovgjBWxbxucpkUwEUKZ0PLMS2S4L2WsTDucJGCUSOR/50QCSKyE54ERtKp/XyNVLFJps0F28AzquBmaNzdfu+d984MBH2dPez8mv3YJyZ5x0xseutkFmCgGGXmyiff8QPaM1BIJFCgWdwLM+co1gNZv4X9lXE0szfqmSdWtGuThZhVVUUXQXw1OiWNCJPO9BfiBOoi+GiFoIR0Kf0LCiLqELCoUaQWBznNzxCtRb0uTTHiTVRVEdvAcDZG4t8vb1p3n4yFbUUAk7pSOVZKq7ZkkfqCa0Z5r4mSpkE+zWIrIieHDdCb5+aAfGtIpZY+OtzFMaCqA156iNpBk6X0ukNYntyhQuRLjzzhM8dvwWAn0axW15nIRB+LxCcotFoFdjw7sucOTAGvxdScRzUeruH+bieBWuLSPJAnm6rJzEBeF3uH1DDy8OthMMFEim/DRVz5EuGvh0i7HRGMa4Rini4qnPYRY1vGe8mBGBHbWJ1GZIzgYIxPKEvEXGRypQfDbSiAfH70LQRlJcFM3BHfHj+lwqWxNYP6wiucWivinOzLGasrK7sdzbzVveSZKgtipF6plaxPYUFYE82ypH+N6ZTQB4L3i46z2HOTTVSrynAjfo8JZN53jqwEaMpiylksodHX08+9Qm/OvnSA2FkU0Z13AJX1BI7Syijhm4rQXckkJX01S5nDIaeB00r4U74ucj9z7B547vR5rV+djdj/PvX7iHz/zqF/iVAz+NrLo4SR05ZFEVyzDTW0msM0HxuUp2veckzz+6GbOphDqt4dSb+M55yK8rImsuwRd9vO9jj/N8vJPJL7eS6gI74lB1UGH2VpdbNg9xaS5KZjJIsFcls9YiWJ3FOhbF8Qgkp1y3o7Fsud1wJWS5LCpzTIXYSxo17xtiJFmWFFQGcuRKOjPjEXwDWtkXxV+CAT/1t00wdLEaLWqin/Sz/u09jGXDJDJ+gj8I0PwLfRztbWX4w5+8aToD5aGHHroZ13ld/Mln/v4h7307ECpIPhshg+gNULNpmkzKh+3IqEELI1gif6CSdG+Ujh0jjB9oRDElKo5JZNtdEBL5gRCFl2LIXXmKc168a1N4PBaVbXMkCx40rw2NJiVLJfCcj/AdUxTOR1CqTIpREB4HqkrsaxugZ7QWadpA+B0k1SWb9iKSOlZW5/mJToxAiVhrkrN9TUwWg9iSTLQhRdoJII96KKoqgX4Nx1JRLxmUfGBO+lErTGZPVIMjQ0MR0e/HTHlw6004FkbvyuIkPHjbM2jhErLfQXkkSqFWMFUI4u01CHWmiHsMNraMMmv6aYwmCXuLFH1gaDY1gSzJopd3dJ3m3JEOSrNetm+9iLe6wEwyRGBQQVuTJT0aJpXyIca8uGEbOWzxX/Y9wZzqYyobRilJaF0ZuhumGRd+hJCobE9wqtjE+EyUgqKSlDyYcS9SwGbmhXqKJ8Pkm1wSIxGE30U6H6QUEBSrwPEK9m/oxavb1IQyDExUISc10j1RLBRwJfQRg62bBrH+o4b0fTlUxcVMeBFIGJNq2aql2sRqtrh1ywCJgo+f2vUSx19Yi6OBcGQkTVAMSEh5hd7ROrSUglxRoqkhjidaJP1iDVZI4IQcTFVi/87zDKeiCFfi4oF21KyMUoI77jjJwNEWgoMSZrNFIh4CVdBWGyd+sI6qEy4Tx2rJ10oIRaK6PYFjCL793n/g6/GtWDU2Xo9FcjiMNOClFBXMFAJ0tU6RuhhDjloEz+gEhiWELLNv3zkOPHELUkLDHfCjT2iUzgfJ17kUj8Vwq0to9QUcDaSLfkTUxgxC07ophCHgqRilCAjdJT0WBt1F1lxqnlIQezPUxDKkR8I4JQViFo3NcSaGY1TsmCGT91DoDxPZGKcQ90HQpvoHBtlG8PUbiJKKFLWoWhsn/2w1xVqHM+daEY4MloySl6nuSDCUjJVFqhUlRrNhfKc95GIgq4L+4VpAwl+Zp+CquEhUHVHw/8QkeUtj26ZBNMOheDzGFF4qqzLkCwZbu4aZPVqLVVfi9KFupJoiblHleKYBpTNHSglwabCWiud0om+aIZ3ykS8aUJIpSgpWjU38+42oRTArBLHOOeyeIKWYQE2oVLbNkaqWOD7dxExvJWZE4vY7TjF0sRbbJ+P4BdMzEaxZD9HTMqlbHLRQCelYiEKTjZZWMJISJa9EMeNFndTxNmYxJ/34enUwZfINguzZKFKdifxCBKvRIj4SYf/mHtJVEj5/ifylEEIHDobwbp3DORWmFHUZM4Noj4fJGRpmRCLSkCHjGMS//szEQw899Pmb0Q7/WHQG/+Nzn3loxy80kjpdQc4rY6d1RHWJbMFDJJpD0x2k4yH8bRnSjoHjhelsAOpM9m6/QI9Zg6sLgg0Zwk1pUjk/9pgfVwWGfNR3zTCZDqJ6HPJpD7atED2sk2uCZMlLw7OC5rvHmMkGELaM4bMYPtKIPlU27Qu2ZGipShCfCSNHSmC4hI8ZhNYnmcn6qatK4TUscpZOqaSx8dZBJiZiyCUJs9NEBB3W3jbMzHiUHdv7GB6rQqoxcXSorMhg1BQoJL2QVSk1WCAL9u88z4WxWt7WdY7BVAVZTefX7vwRL51aS2AECmMB7MYSiaIPXXfw6RZFWyOeDPIQna5fAAAgAElEQVTnW77DUzNrcSSJ4xfb0OryiJROMQQjQ1UELypktplYloLkcRCmgtGUQxr2Ejyj8qzUQt7VCDVnKFa4FDMeEnk/61smsPyCVMZHquQlEs6Tf64Kz4YU1qwX74BOrqvEu+8/yJlLzSCDnFaxai1CsTzeoIl0wc9AuoL4QIyJYoDqJ3VsjwwyhAYkWm8fYbLkJyXp5Es+3IyOE3ZQwha2qSJkCW9nmi2NY0xmgozMxChOBDgx0MrmPRdJHa3C0SUq6lNsbB1jfLSScJ9CvruE4bWYGY2SzXuQbJmNey4ymwvAiI8Zj06pqPG2tec4X6hEaijga8twdrCxPBtqt4iG8xSKGp4hg1xUINWU2HjvRYaHamndN4JWXSSR8VNIefjSpZ1Iios96yU+FcbTkSHUmcLqD2BHHArHYggZ1m4cZbQYJrfGwvFIDF+qxq6yqV83g9pUQG3NUxrx4+Q1Sq0m8rRBxeM6mToZtSnPLQ3jJB0P0rcryDkeCvUuf3fPl3n05e10bhwlMRYGj0v0TbNEvQV0xWFT2wjDF2u5f8cJDve1IRUV0gUPckqj8iSkSz78YxK2UOGOFCVHwWjPUtMyx9RolGTRi5JWseMG3imZUq2NJ1rEyegMTlfhO+yltKmAk9JRhz3k22yknEpdU4JM2gdCoromhemqbGgdx9icZnCiEidjUF89x3AyijPlxYnZ5As6kgxzR6uxwgIhSbiVFtKol9CgjN1qIoTE2LPNuKpE4M5ppmbDGIMeRKWF60qoXottzaPkWx1EWxHOB4i2J8mOB7HDDkKBbNFAGfPwE7tf5qKIIA976DUrQEj4pmRcVQJXQuiC2995gsFclFCwQEYYbNwwzKxHI7x+jtycD6Mmj1RRwuoLsX/vWfqNILZa7iytqEusOk0uHiBnGygRi0uTVci6S3I8hAiWB2SFSkEx7SG0dg5Tl5FksDtMbEemYd00BVsjm/cw942nb1pn8Lp0BpIkXZIk6bQkSSckSTryyr6YJEmPS5LU98r/xYLUqzAtFdNWueuBo8i6gxywiDzvYU3DFO7jFaTnfOQbbVKnK5BjJYQCxoSG0u+lN1mFU2uipsoyxamBStTuDJ44eGZk6veN8o+dX6UwHsB1JXSfRSSSI7VW4LQVidaneODPnqT3iY5yZiwZy1LY/OZerKCg1GCRnAoycKgZyWejKC5t9bOk1tsMnqsjOxZiJuNHACJusLZhkqOn2xEdeSKbZwmGCrx7wwlODzTQ2D3NZC7Eh3a8iDvlAVMm92IV8YEo3uYMVJt4giayLHji6Aa0EYMu7xT5aT9Chr9//m6MWZnEdotCjUCk9XJoCX+e2byPkXO1eI97+a0f/gxjg5UUL0SQswr2SNmNPt5TQei8Rv2DlyClETrkRVFcaloSeHQLK+aQWiOQFJc/3fQ9fq/rR5hZA81r4T3ppc6bRpIEdkEl7CuQPlpJKSLwfS2ME3TI17kEYnm+cnQH9+45gbc5Q/CSRCCWJx33UzoUo+HZApGWJJIA2XBw3pfA1UDLQuGtGRIFHw3NcTKTQczqssBWCPAaJSSPgxsuf+B+pYTrKCCBWpBwwzZt/jilkECpKWA7MnNFH2u2DJPcWmL/2j44EsYzqaJ6bWKbZhj4RheSVA6sF/m3IHVVKX705DZq6pPIsiCb86CPa3jHVNRRA8eVkGd0irU2rluuJ8/1ddL9rl7GHmnBERIVwRz+Xh3vRQMx5EcEbJAFilzWKxTaS2ihEsUql9DWOGePt7Ju2xCVVRnkgkTn5lE8wzqjF6uxHJn4XAB1f4LgIDTXJXDCDpNvKoc4KE35OH6xheKMF/fB+CtB1eDXn/gZXF3Qf6IR4XfwnPXi00oMHm4iUzJ46uQ6AsMSGdtDQ0OCPbf2IGfLYRSmdwrMOhtXBavGIj3no+oJAw5GmDxUR0vbDLgSjkcg2xJv/qmjeEIm5rQPq9rifW86gPWWFPppHw/uPEKpuYQRLWLMySReqKW2KUG0ZY51kSkKaQ9nX2pn6istqEMeNq8b4vgL3RQuRCi2lBCmjOaxEa5Esd4uB/IzHJAFTp1JKQSlnI5VUsk32cTWz5LM+pAUgXRLGllxaGqZRVzyc2SgBY9q87vdj7PpbRcYP1pH8BLIOYVQn1Kuix7BNx7dSz7u423vPgQuELbINrt41ydxDRf/sMJLk82ofT4SYxG8tVmGvtWO79kA2aLBfbtO4PQHcPoDfOi+p3j25fVIw17kvIxnRkJLykz3lIOM+uqyyLJLVU2K93ccJXRBBVuGCQ+19XNIhkvQKGGcKQcSdB2ZisMqI2MVjI3FsJIrO1zeKDdDgXyHEGLLFXKrTwJPCiG6gCdf2V4ZV6LvXAMuEk5ax81plN6W4uJkFWvfdwEpo1J9UOHutxxD1WyUgsTue07j35xgfDyGKCo4tSXe2tSD0F2kYyFyu/JwW4qBwRp+b/hdRFvnALAnfMyNh/GNymg9XuZGw3yxdxdmdwFJhkhdGkVxOXKskw27BlASKqgCu8FEHTV4//ojTD7ZiL8mh1AFH97/LJtrx8kUDZSqIj2HWqlqmcOZ8BLvrcA8GUWWBLrPojk4x8hUlO9c2lSOOqgKCjUu/qYMm2rGESkdtyeAZar8xV3/ye13n+BzvfvwjqqoeQklJ2NvzBI5piM1FFByMi3t04ydrSGV9iPZ4O5OIXQXuSijd6cJd8xRu2EaJDCas6TX2JzvaUSpKpLcYHNHRy/ZosHcWBg9WkSoAkV1+aPTD/CJH34AWXOw8hq+ScFApoJUxkdDQ4KJ6Qi+LQloy2H5JCKnVUSsRCFvgARPf38bheEgRtKlMpCjtXmGjnsHGPt1i2TSz6Z9fTTVzJE6U1E2Ed4/h90bJObNI4CP73ui/L4aTCxTJTnnxxcw0abKH/8zfV3ohkXwqAcraiOnVL5xaDsVm2Zor5mlZKv0nW2g52QzGztHebG/g+D+acxKB3fSQ6GkEXn7OBtrJ4i0JEm2q4xfrMK7NknySBWlGR/BQIFSrc0vvu9RrIhDMulHbiiALMjNeXFGfXjOe5nOB9GygtnT1Yz3V1HamqXQabJ1by+VVRnu3XUSXXWYGqgk9pKGlTIIXpKxflRJ7dpp0n/bxFzKz/rtl+jtq8fVBDgwlwjgDxSpC6XJtML99aepe7IcZRPNJdo6hzqtgafcaMgBCz0hU9mUpLIzjuNz0Sc0/PtmON/TiO1z+dnml5DzCqmtJU796y2kCh4upWM0PuEi5HKLoMVVSmH4ia1Hqf+hyuw9RcxYuZOYyfjxBouU6ksUW0r84NRG3AsB9mztoeERhcfH1pKb9nPHg0f5zrM7UD0WFaEcpc4CxTqH2ZPVJJN+nujvRvXYiIYic3tNSvUWZ8bqsGtKeNcm0cc05JyC6Pej+UqoKYW1G0cIRfO8Y8MporEs3Jpia+cQTkqjqnmO5OlKzAkf/pd9aKqDejZA/Jk6AqMS/lMehiYq+KNj7+ClMx04XsG7Pv40elomfVsRt6AiWxJOUxEtZHJguo2t6y6hThi4QZtM0kdlRwLbB4npEGoBgr0qn9/6ZbKtLtXvGSab9PLDs7ew9/YzhPrgn17ej9AEd955gnXbhiiFBPqGFIEhGbOpxPrqScSIj8T5Ch6fWkt6nU3siIqQYCYRQkpqjExFybfY2MmycYH19iS4sKFjDNSbHFfu9SiQJUm6BNwmhJi9Yl8PcLsQYkKSpDrgGSHEimYzC5zOlnDyWMoZyvtsDReebb8hp7MrY/z0/c+ddH18YeTHq2MPXe20crXz11Ixk+ZJfL+b2NvLUQrno4HOx5gZ+m97aPnjA9ft9HN1tNTlWC5W0lLPeiXzzmHXioF08d+3rug4NR8naD6/S8XImY/nc7MY/PTuBTFgrubSn++m9b++MVFLLzvz7doEh04t+T4v/u0utIyElpaWrSuXecUZ8WrmY1nNO5Vpz9Rh3T6xKN18WQz+9920/f61n3kpR7HXylIxfAY+sxs9KfHmB48hI+jb/trWsRj5wz2Uwi4dv3NoRUfPq5mPlXUlvZ/bTvcvHV7mjLIDpvA5l9ug3n+57Zoxpa5m+I/2ENw5syCG0tURkK9m0zGJU9tebYuvdGDsOOyhf3vxcv5D5zRq/778XD9OTmcCeEySpKOvLFYDUCOEmK+pk8CStlySJH1UkqQjkiQdsXi1krS2zCxOW1hswnmyvwktc2N20uFjr06rlrJB/vU9jy/YNmILzeGK5yILthvuW96RJn2y4vJvs738Ih1/WVTgdJXt9d/atnzQtysJrJ27rnTCXNrUVXhWNi29eKjsHBY6ufK0U1FXtrMbnCk/c8XGV97hEjGVjMTNtWYObljZbjy6dXF9ullkvlr2JTEryuW2lBOXbElEt08vaR59Nb1Di80zAXqfaQcg/GTZJPnSI0v7nVih8vtpfOr6TEaT2Rvz01mJ6MbZRfvk1hz+fTM80d/NDw9sfc3Xbr9rELeq/EzSEv5ByyHvXvzdBGtWdrSTLYma+sUxtW4E1xAEjYUdnza7sknsN08vLJ+2xlfr7dPf33b5d+iChu/eqdeVv+V4vTODBiHEmCRJ1cDjwMeB7wkhIlekmRNCrKg3MBqbRNs//CKFWR/B2gwxX4HcV+rItEqYjSX+YM/3+auvPkjF7knGLlWiphTkEhgbkwQ9JlNnqtFTMoU6G8nnoE7q2AEXoQrUUAm1x0exqXQ5PKySVXjnnS/x2Fd3UawUSC4gQOrIYRVVbmkd5+zRVmrXTxN/qRbrleB5rRvHiX+3kWyzwInYdLdPMPpEM4VGB/w2Iq8ghyzcnIoRLWIPBYiui/PJ7kf50//1M6S3mZBV8UwqFNpKRKsyWM9XoOYhtz+LlfCgFGTUrMzH3/swD09uoqe/ns3dw5w824ISKcGYFydq0d46zcDFWuSijLc5g3QwTLbdRo8Vscd8eFoyFEbLtstGfQ7Xlaj8hhf3w7NMDlUglSQi52XmNjsEazNYx6PU7B1naKAafUZh51vOcuiZDahdGfRnQ9zz4QN877t7sLrzOHkVf59Ofl25woePGCRvKceeqTqkMLPXxjuiUWg30SZ1JBc8MxLmngzBRwJoeUH7r5cXPandNYHtykifryLZqWB7oBR1UWoLGB4Lz8NhjJ+cIvvDWqreOcLA6QaEJtAqCyinAlS/eZzkw/U4HsjXuegpGf+YwApKFCsEVsTFmFVw1uRgyIfbXEDXbayLQWQHKk8KsnUy6XU2nkkVVxE07h5j8Hwd1R1xpoZjyHkZpSRRtWWK+Ms1+LfGyR2rxDEEzbeNMZP1Yx+OUugsh5O2m4u4GQ09odC4a4zpRxvx3znN1FAMVIFnVOPWe86RLHnpe6GVUq0FMmWT2YYS2phOYAhyDRJCFbhtBTY0THDqdCu+UQVzU57GqjnijzTg7E2Rn/YjF2Rcn4saKuHzmRTOR/BOSqTX2fiGVAr1DkIt6xo2dI1y4UgL/lEZa18av6dEIlk2YR05V4uIWMSe10lsdRGaS2VDirmUHyerlhX9ZzSyLS5qQx7PgQDpdTb6jIJrgB1yMKbLg5Lb3nKew0+vw3fLHObhWDkseoXA8Qpcr0Nn5ySa7ND3cgtatuyYd9t7TvPM6bXoIRMhwMrqSDkV4St3dFJeQa/LIZ8O4njK7xcHvBMK+W4TLBltVi0HvAxZhI54iJ0zGf0FG6nfB105JElgpg3qHldJtcuYMUHVLdM4rszMZBikss9HttNCDVhouk0h4WXjmhHOjdaVTc1NBW+0gNUfRG3PYk6VF8fZ/NYLTHy6k/F9CnalhT9aoNQTQmrPoZ4IUKgtl6lnSqXrzgHOH2zDOyNhBaAUcfGPyuTrXTwzMsquORrCKcZSYZyDUWQLittzuKM+tNYsZk5n6Oc+9eMXm0iSpIeALPARXoeYaKlp/9VxO6Aco+P/fPPOFRdjuZorxSWzv7Sbys8tvM/wQ3tofujV610dW+fquCiJn99N7F+Xno5fKXKaFwtN/doeav7hAP3/Yzcdv3uQkT/cQ9OfXjv/y4WmvprlFnq5VmjqebHCteKnzIdqXo75KfX8VH6pKfnIH+yh6c9uTthkuGKBmWV4LYuTXC/z9SXzvl0Ev3poyRhavf98G7UNc/g0a8V3ACwbc2i+/szHWVouRHXVgQgze5LXXPBnnpspsrvlqLwoRHXfl7YhyYK6yhRzOe81F/dZjv6/2oVUX6T9AycWxOy5FkuJlK4V5yv8QgXHX+i+LPKa+K09l8NZXy9DX9+IOetdEF7/mvHBrnqnVy5odeXCOP1/vQvH574hsYlec2cgSZIfkIUQmVd+Pw78CeVFbeJCiE9LkvRJICaE+MRK1wqtqRGx3/hd5Hx5dCMUQWvHFLsrB/nP89uQhr04uqBq7SzTAxW8Z8/LJC0fLzyymVJHAUV1kXv9mLUWm9cOM/C9DrItDkpBZvOePo4f7cQ7KWMFBGpeohQWOLUmypSBW19kb0c/xycbyaW85XDDwLf2fpaf+OpvYlVbZSVynYluWFQGc4ydrwEBG7ZdYld0kH869Cb0aZVb77jAVCHIdCZAdsYPmktTfQJDtRkYrywvhdkX5Y59p9kSHOavH7+/XACqQImaaLqNORKAKhNpykAoULd2mrGJKJVVGbZVj9CbqmY6HaAwGsTbmCE/40f2W4iEgTEnY0ZdjPocxaxO8zcV1v7RaR4/vAkEVLYlmJkMo85q3H77KY5MNmFoNjMXKlELEnZzkfCLHireO4orJDTZYfjJFsL7psgWDQIek2zRoDAQ4pfueZx/+c97MCsd/I0ZsjN+5LyCVGXiZDQaWmdpDs7x0mArbrbsODc/Ejzf08jtW85zR+Q8/zqyl6GxSrQRnfpd41zqr0EOWIikjmyWRQKuLpBsich5iY4P9nLi+W7skEvH2nEGxivLEUqDFhsbx8hZBgNHmnBqTbQRg8qTLql2hVyHhVSU2XVrL2dmarGOvxJGPOxiJGQ8s4J8PdidBaKhPHMXYmUFbEURMeAvRxPVBWpawfG43LHjLMemGkmOlUMF+EZVlB1zFC9ELjtrtdfNMvpcE9LGNOJUCHlzisJoEBGykBSBqjmXlyNtiyRo8SXwKhbf+sZ+kMDsLmD0erECgsCwVJ59GS5SRkWELBTDIfqEFx6MkzxbgR1yuPvW0zx2bCMYDpVVGeL95dmIkAX6rILdWkSkdKSSRKy7HIZkdiJMQ1Oc2ZdrcFXwrE+SHQ3hqc1hjgZwgzZqXGP3/rO8NNyKYVjksh7cklJeXtaUqe6MM5sM8Ob2i6iyw2OnNmCEzLI1mq9ELJRn7lgV+oYU1ukwrgaSDRtv7+PYqQ6kUImuhmnGHmnhkY//Jbc//3Hu7T7Hkw/firw5hWmWQzAYhoV9IYSWlhAyqHnI1wnuuesYPzi8maaOGWafr0MxobClQCiYR1cdZhJB3LRGoC6L+mQExwBzR5ZSwgMeh8BZg9wtJtqITmAUso3gtBeRhzx4ZyS2/dRpDj52C2adTUvLDBNzIUppg9qnFSbvtAn06sh75sgkfURiWVojCQbnKjAtleJYgMrOOLODMcJNKdqicVIlb3k50soC0WAe0yobSXj8JQqp8hKm7c3TDJ6vQwRsampSNAaT9MxWY1kK5qwXLanQ/6kfg6ilkiS1A99+ZVMF/kMI8eeSJFUAXweagSHgJ4UQiWUuAyycGSy1tOJSCtTBr2xG6vetuITd1VwZwXKp6IdbjsOJK0R388sNzrNoabqrjl/JlaPxeUXdvDJzfkR2vaOO610es/jADjwPL87PtZSo88sfXv18V5P6YSfh+y4ue3x+ZDpfLn3/aydd/2Wh4vpayw7eKNdavOZai9+8Hi7+3S46f+PQ5cWLlhr9TX93LZmcB3fGczkS5nIstwzlfNTK+ZnbctFN540Wrnc2dDOXBF3KeGH8E3v49Ef/hb+5dDfD0zHar5rdXy+DX92EldXp/sUjlyMAL4daW4M9WZapN7/kZ3jnwtAN11qsJvH9bpLnKi7PkOdn8TfCxb/bhVDFgroffTHG3N7lm8Grl/O88tu5UjLQ+9kdqOHS5bL8sVAgCyEGhBCbX/nbML+IjRAiLoS4SwjRJYR4y7U6gqv50FufWbSvZvNihYk04KN+59JRA5fFeHUaq2iLlaFJa2FAPKXwf9s77zi5ruu+f+9r09v2XrG7RCEIQCCaxAqKpros0bYYO1ZsJZYTx3aK4rgkH9tJXOKPYlmWLdtxXOQ4kezIskiRkiiKFCWxgegEsMD2xdbZnZ3d6e2Vmz/eLHZRCBZTAk3M7/OZz87cd/e9+84775Zzz/mdy8UzFL6cu+i+t738svfjXRuMp/Fpd2O1bZ/b3vZvu+2ovEruruDsqxuwZx98GaKzbdfnRemMuhtmMyvXDwlJv9h03ePPnXStgUrI5WBSYldvZJqdr8+j5OVQvAb77GaMnut4Q6+3GXra1Y+ux125T09c7SuRWgniPe2n45ZX3vRrCl17czNb5TzSet3jL8dumi1UHQAmXx0b7t0/8Po652tf/GrytHy3zX849SAXT7Wxtf21JaLajICvwu5Bd5ANzF//XVgfCABGUtfQ167rcyStZfwYm0njlNc+WW44IZCey01mR4b7r/s/lfDl1+nfutG3ZR7bSIzTeETFOPPGbfxvxpuKqC77Iwf4sxfuwPqmS8iVf9BlDW3yZ1n6uUOX6qlbeqm0mJQsd4dera+7+mTXgMhteNusZ2ZStw5cKntieNul7+X33H7J+0frcdvz1ed3XXa+40ubOhrlck+e/z7yA0x9/jYA2ntdT4tE1iWSm/+w21k2HVhk9M/3onW0b7RR3+jc1O1DOO/YReKdZdd98RpQGza8lpqbNjp9rXeDPjro3eiAKw/cftn/L/zCIdb+tAt16wBa1Vuo8IOu3Nc+enDjfB3tmCFXHta9b7tmW7yLrgxk1Sc6EHDNJeO/e+BSnXWeG62ni7lfPsToX1x+rvlfPET53bdfdX1w7ajrKH5gHwu/cOgS1e/ma2yG1CXqtqtZW5f/1aHL673dfbaK/9W/aFZVP6Z/0H2N1GvQZesJHdsH8eMbnkLKrm1orRu/R//MndhdXL62HisdBVI/fpD1Rbxd9RoSmgZCXNJ/s+y+D93757j46+795X74wGW6oPV0ody21b1u+urOMvuRq+U4/qkDjH/qAAufOHTVsXXEui/33FG3DbL3tnGKKS91W5MMH+1h6WcPobU0U3rfPjiwE+vwxrMX2tXeNmrYNcEVT8c4OebeQ/HdGaZ+y9WLwof2o7W2MPrH+wA3PeTyv95oY/x4y1Xn9R91n+/UFy5/nxI/fZDlf3UIw7DQv+3O0la+Mojtcy6TSfZHDpD8F+71xVPtl9py2bnuraBcoQvdVS/J0nv3XVUfwKxzn2n5Pa7uTx/f6Fvseza8m7I9wnVYAbS+nmue6/Xi1VMAfh8Q+psXCH98Y1kf+KK7TBpdaaL99zeWUPb4FFgNlB5rJswEdvLVLT7EppmqP+rOEOzzG77A2uJGR+y7mCU46b5k1rRr7hF1l890sxfqaFz/4Vy+0iibOr0/6uaptRy3s6gLuC6lPdW0hKmil8GfPMbm+bw0N65hnxtBAZp/pR9euLY/uL2y4VoZX4ixTnZrTW2YKxJroUvlxtcv39Bt+53nWPvoQezzY/TWt2IC/r935R773Mby2JqbR8u5L6T21LVjEd734ec49d+AqvdfpcrDvuXfbZhH/KPu7NWanqHjN6/eUN28VN58feDShhqA7+EX8eGargAGf/kU13KgbelOYg9fLbumz15uahDPurNkp3B9mu7NWO+UfXPufd5y+zRXZjQwm0y0cAkrvjFbd04NX9bWSzE089fm6lcvBIj+1XNku9yOzjfrzsKl5WrOuv57qvkUpk610/+r7v0F//aFy/RrXZcBip9rxeBys1boC1ebsl7NJvPqfJTN3Lr28CjHjx0gOqYQ6S1hTQga/+g5LMD7FXf2vrnzWb+XzbAz7gxdCvBPuO+mNRymt2ra8n/pCBYw+NPuqqP7ERvf1Arrb6LsKV513my/TQtcZUZr/GP3nKM79+G8Iwu/Dw3vGyX117sJfWHDnBz6mw1ZKA+Z9C5dbUJS4wZWw+WasE617n302mbl0KgrDc9j7vvZtWfDtJlb3GAjLnVWCNe7pi9rcvqa53q9eFOwlno6OmXn7/4UbXUZFo63olQEehaCh5eIz9QRaCyQXw4QaMpTKho01mVIpoJu4ox5H3adiZLVcPw23lgJzoYI7UuwkgwhVg2kIpGGBAfXfdAj0bOChnsXmInXEYvlyJyvx26qIG03LaBQwC5oiIqCUlCwQ7b70u/KUMx60BIGdksZWdAIt2Qpltz8CoFZBdsD1q4cjLv8SFreDd9HgG9riuxsGK2hhJkzMOLapY3TLQOLjE+20NaZZPX5FrRdKby6RcCosHikFTMsaR1aZnGsEWk4bNkSJ1X0kRquJ3TLKqm1AL5gmeJiEKlK/E15OB7Bui2HdioIe9O8rW2Wsc9so/yRNTTVgb9tIHlfCW3ay/a7xpnLRlmZqsM3r1LeUUQueVDbCpgFA1+4hBCSu7om+NpLOxjqXWQhEyY3E8bTWqCU9KHmFQY+l2L5NxyyZ+sxIzZaVuVXPvB3/NevfYi3HxgmVfFzZrSTzq4VZmcaCIzrbHvfCEfP9nPHbRc4+vgOtAIYd6ywOhelbyDOnY3jfHFyF+2RNBPHurj7rpd45rHb8O1NkpqKoTaWsLI6WlpzXYV78ljLPoQjCPSkySaC9PQsMz3ZxNtvHePFmS6koxAMlCiUDDwek/LZKJU2k1BdnsrpGE0HFlk83YJVZ+K7aODZu0o27yX8HR/ZHvAMZjBNFY/HRFdt1qZiIMC7rFLsrhBrypIreDCz7gCoZlT27B/j7ONDFDtNPNEScsAGTbQAACAASURBVCRIcFeSiqVhfC1C5u4ijiPwn/JhH8hQXPVB1akBW+BZ0HEG87x7YJiHj+0m1pohPe6ake59+xm+eXI7/osa5m05fEeCqEVJrgs697kusKapUcp60OM69OURYwF6Ds0ycaqDXfvGyZkeZp7qpthm4W/OI09GCB5MsJoO4Kx5UEoCp6FC/dMeVu4w8U0amNsK/NTO7/Knj9yPkLi0GLqDf9yguLWEuuBBz7szBDMoCW9Lkh6pQ88plLrL7B+cYjJVT180SeI/9zJ/t0G5xQLbjbrfsmeW+XSE0mgEK2qxf8cER05vQSkrGB15OupSZMseVoYbaN6xzOJEI1gCtSQwUoJDHzzNZLae+FMdFDosdu+YYnSlicJcECImwZe8ZLdXXPoJS0FLq3S9bZ7JqWb6epdYfLqD+z54lK+c2IXw2nhHvfQcnmY6WUfYX2JpIYq+rNN3YIaL3+lGbstSzniINWVZS4QIjBo0vGRi/XySRDqIrtv01ScJ6yWeHd6CktUwUgrRA0v8RM/z/M7J+zHO+2l4xyIrz7Ribc9RH8mz8lITVr0JpsLu7VNUHI2v3fWZtxZr6W9+9pO/Vtd0D8mSH707T7g7Q65OkEkF0EMVrIkQ1FcQowGC3RnMxxvx3JLFHg6z844xFleiBNuyVNIeLFvFEYICGrKg4VlRsepsBrcssroUJjijUNlVoKyrpIo+MBV+4tbnOPf8Fip1DsaCjmioYOV0go15tnYvspgKo0fLWI0WlTUvoqARmFfo2TtPxvKgqBIhIHTEQ6lRcM/7TzD/jW48a4Jiu4MdlNg+B629SHkqDA1l7JIGqqRlaIXZ2Qb80zrLhgfPnEE+IPB25ikUPexqnadsa5SikopQUHWHkq1x744LHB/pxdHAGPMwtHuWZDlAec6dRagxE+/zQfK3luhoTJH0GMgZP8svNbO206ZU8NDWlGJeCyJV18SxkIpSnndpjffeMUKiGMBOGyhJA1FWMEa8DO6e5ZkTWxE+m5W1MMqFIPfee5rRkQ78cxq2TxL5Z0nmJxvxJFVitybJ57189/w2HL/DbDbKymgD9b1rZL/djNafR+kqMpesQ2Z0ll5qxgw5/MyDX+XUWgfFvIciGqaqsXS8lWXbT+ykyrDdSP3OFdJZP8JvEw4VkBdCyP4CakMZ5VyIgf0zpKaj6Od8GNszJBIR9IDJ9EIj991ygZlMDMtWMUs61mwQX0KgZVQq9TbhngzxmXruPXCWyXgTvrhKNqywpS1BqkMQe8qg5645FhJ1WPN+imgoQYtgQx5n0YcUgoJl8GO7XmTkhT6clgrSVplfjeIbyGALQW/jKvmwYG/LHPNPdPNffvZzDJtt5CsGdnuFgK9MX9sK6bP1YCkIS0HPKugzBqMX29m1b4JEIYic8yE1mMjVo+RUtILgZ9/5OE/bPZTCAtpKpDJ+TFPDmfMjTIV/9sDTnDwxiFYWrAVUnLxOyQ+5shetvYgRqtASyVIYjVBaChDtT5FPe5EhGy1hkOuzwRGIrgKOpXD0/AB6VqHcaBNuzWKhoMd1tN48lYqOlleo3FLE9joU1/xIQyLai3Q2rzGx0oCm2kyd6oD3pvnQ/mNMPdHHB977AumgztSFVuSiD60/h+0oLIw30TaYwDkdohCFnGmQWQrhaS1QOF2HMAWytYy+pDP4zgleOO7yHf3Lhx7DicKp5wYJHvHgPbBKaSmAui2LteIjOK1RiTlIHXJTEaQGt/dNMbrWxNhUG0rMxCmr6GsqcdWHx2cS8ZXIzkRw2stkSl727hlnarIFpaBSSfrwxjUKvSbZDpWc0LDyBn1tCQ7WT5G3PUwmGlGiFfS4Tibn5zvzA+jzBr5lQWE2hO2XVHyC3EqAwJxKcMca+rEgyYl64qkoqW98463FWvobv/l7v+Y/fAB/XOGet5+lYBukX6rH0QWHd5wn4fVQWg6g9+Zoi2Qod1ukL9Tz0+9/nEeeuR0lVkHTHCoFl6teyys4UkEg8A+mMR2Fu7vGmP1OF8U2iWmAGjZpbkyTTwQ4luxEzWh07IyTvxjGLmsQspECfqj3BC+OD8CaQfi0jlJWsX2SSkySTITxjnsolTw0Pq6Tb1Ow/JLpkTYqOwpUOi36epYI1+VJZQIohkOwJYd6LIytCfbunGTihW6UkkpgTxJVd9BbSpSWgrS3rWJKBUcorOQDtEUyhIIlGgJ5luMxLB8UHRVFASflIeE3UFWJErTQLnrxduXQ+/P80NBJFkpRMmMx7KCD7YfQpIrcUiR3pBFjKItlaojGCvqCgVoRBAdTzD7fiWisIJMeHF3iBBzqdieYW4vhaBL/OR+hrWsU13yMrTaixirIlgpOQadgKFgFHUcH9USQSreJ43NobEuj6g7M+sgVvJhdZRyp4H0mhN1dxtEkoVGVoQcm+PuX9lAq62zvX2Bxro7leIz77zzFxEQbu997gcXRJgrLAXyteUxTo1jwEBpRKXXaiDE/5S4Tj98kreiYhkJLe4ryiRjlAMSaspyb7ABFYJY0dvfNsoyP/r1zLDhBUNx0lUpeY3KhiZbeJGvSB4ZDMhlGTPvo+fAU55eaEbM+tLxCx44lLAR1/iKVRoty0UOsLc1ouom85aGhNY3/iQCFDgdlLAB5jTXNQD8WQustkIhH+fr4Lko++MDAGRb/dx/JqEHa9uBtK1DWFAYHFvB356CrRCEIh9qmOHN0ALUscLySxhdUcr0OdBd5dm4LSsLACdkEwiUqZR11wYvjdzBaC5x6cQirwURfVTFtd1VdSvpRApa7utEtFoabcQbz9GyLMzfq5jjwLugY29L8wNZzTBViWEkfnkgZ31kvVlDiaAI77kOtq6AsGXRsWya5GsKK2siiCpaCd1EjunWVzroUU8v1dDakWFqNoKzqMBbg/JkeKjHJ/Dc7iXt9dA8ss1r0IxMeWvuSFBaCZDJ+tIKCvqpRcTSkJhnqjrM6HePQO88yNduEFbO5q2eM4XQzsVMqT5X6mUvFCI+qZO8sUq7oSN1dseszHrR9aziaxCnoCFPgRC1mclFXJmkdchpaQ4nuHXGSqyE+vf8LPJvawu2DkyyXg8iTEWaMAL5oCRn3YgckSIFjSGTQxvCZoMLqaD3zvgBTj/SjFFVuuW2W1GgMsS1HrDGHORvgfT/6DKdWO+jfN0vB0YnW50n5NcyLQcoxiWdnimhbhvm/fvatNRj818986tcaBw4hFTifbSYjdUxVxd+cZ6humbMn+pBCIAIWxaebSIdUHENyrthCW+cqHo/F6myU4ISG1ARm1EbLq65feNDEmQkwpYTJGRqB3gzObAAZtNGeiFCOAj6H0ECKdMFH+1CCpOlDWzIwGksc/8atODp4k4LMdgtjVeVt914gqXgwKxpdj5ukhlTsu7IE+zOUAtAxtEzxaD1bd88Q1MucH+5iy+AiPo9JpuCDzhIf3/9tHv+7A5gxidTAEyvRG1vDRiH45SCJXISCY5A2PURCRS7ON5IpeVkt+FHiHtKaRktdFgQ4034Gb5tjYTWK50SA8vYijZEcq+kgp8/2468vkl0O0rVjkbWSDzWjIlMGlcEiqurw0PZjnD7XD1Jg9ZQwp0MuMd60h3JfGRGy0BcMKhGHctHgnbdcYMyIYFoqPQPLlE7GMFUFWwU1o+FvLlC2NBqf05DvXUU9GqJtzxKLE42U8wZ2o4mxrOHUW5DwUGqzaWtdI7MYZu+7hjmz1Iaz4PLph4Ml1oo+pAIz+SjeWImJiy0IUyG8dZXGYJ5kIoy2bKDuT1Fa9eHtzWGteckvBqGo8uOHv8MzL2wneFsSezII436CW1Noz4QxO0yytodSxsPKXAwRtBCrHrAV1IYy+ryHjKYjVUn0uAfLUNCHMsyPNuNkDYw1BeHAwLZ5JiZbyZQ9qKdC2F6w5/wUPAp7t05x8Ugn+S6JDNh0fs0h9ENL7GqZJ9GgspwJu5vzZRWrqDOSa+ShD36XE3Pd9HckqDga4liYeDnEWiZA0dJRFMn5k33YTRVEWcWTVMgMSAIXVUTKwLEVpAY/dPBF/B6TjO2ht38Jx+9QmIygdBdQZ7z441Dss8BWaOlLEvGVSEzXU4r7UVpKyDk/WUPFd9aL01dC5jRKUmN0sQXHVvHEdZyYhWnpGGlBpc1ERizsioojVZK2l+hpg3I9KBWXqjwyLki1Kqyeb0BJ6ZgRh5ZYhlXHi2dZJXI4Ti4RpOHwIpmVoEsQWFSguUwsWKSnd4mcoRJ+xoP/A0tkKh5wBHV1OfJRmH6xCz2t0rVzkblCFM+XoiT2ShqOK9z1/tOMrLRgaQL/sBfRXSQWKhLoyrIyF8ORgg/tPc65tRaXQVSXsOjDDjoopsA34iER1gm85OU7oS4WLjaQeKod31CW8lwAu9FE121+9a4vk4/opAMqzU1pKiejyJYKdtwHAtSIiZjwYfthoRDCaq+g6Q4hb5liVHIu0YrM6hTOxrCyBowEkN1FtPoKvoYi+byX3EKI9FefeGsNBr/xPz71a62dByi0AxLqv2sgKip5v8pIohl9TUPpLKCMBCn0V9jSvcwdvWMk/7iHBT1EV9cKWQzKzTZaQxlbKu6MMWfQ0Z4kPxEh2JWlkPfS35ZgdSGKLQTmUBnHUQg15DEtDSFgeaoeYSr4FxTyEYEoqwgJpXYLLWiiLenExxsp1ztEjnvJfCSPv7FAPu9D1RwQkH2hCdsryYQUBmMJZs+2spIJYp8OIzvKWKbG81NbuP+eU8ycaSc0BWnFi7+xSF84yXBzmI8ffpIjM310dSVp8udYKfqx0h6ctIEnqWBLFRm2qA8UqDRbDMWWUT0OK8kIoTMaa5kg/+2d/49vTuwgnfUTHldZa1S4a2CMxTPNFNtslLSGEjEZSTdRrrgmtR+8+yipkE5mJYhvBcp+FUcqSCHQx73YzSYYkFyOQNJDX/cSSyEP9S0ZbKFQ0QT6sSBWZ4ViUKO3dxlff47ZRIw7to1S8QmU70TJD1Ugo/OZ93yOZCDAyIlukIL5ySa0+jLRtgwPDpzi6fFBZEGn9SmVXEjHf9RLpa+CiJiYY2GSmSCDA4usLUQItuRRTgYRXSWcBR8du+I0t63x7OO7eOD+Y5we7cH2SLa9fYqLF1opt7mDnLXqJdKbRlwIoOTclZ9SFjgeSd+tC6Qm6qjvSZHP+DEygoHb5gk2FEiW/O5M2C/JejSM4wGszgoVn0CYArUssEIOpqZgTQdAAc+SRuJ2SUlVWSqGSC1EuPuWUSqqSibp2rADwRIvzveACi2xDMlvttNw3wLeWAlLFbTVp7Gerse3L0kx62VgxzyLih+pSWRfkd9+1xf4xuhOhnbP8Nx0H9PTzVQSPjKazp7WOZKGh0LGBw0m9pYS9wyOMRlvwvNUiPRSmC0HZlguBvCf9dJ0ME7ubB3a7Sn83wxRvLVEU3MaxWtTyXqQzRUaYjlKSwHKfWWwFPq6EphH6qj0lRGKpFgHwQkNa0sJfd4ge4uFd9yD44V33XeMiSf6SeAjWF+AeS9JjwGWgu8rQbL9Et+8a37Eb+MogovzjViLfrgjzcpiFFFW2TK0yPhsM0wECM5AqR5yF8Os5gNU9hRxCjoHP3iGb399F/64wPQJrIEiZlGnkAgiT4QxQy5dx/KXurC2FfFMujke7I4SEoFWX4KU4ZoWo5Lygp/6gVW27Jmj4mgU6yWWqWGWNb4VH2IxE0GejpBeCSJsQVlTCcyoOAYUpM573nWUM6kWUMA/adC3c4F4JkwpbyATXtTWIiYadtDB2JqBs2G0aQ/FkpfG76rkt5mk//7JN2wweFNsIG8OOlv+14do+oPLPT2uFbw08X93IWZ9r4rNcx2bqRDWGSA348rw/CvZIdeDf9ZxPdbSzcFO6wE5ge80kr8zcSm8/NUGB10vuG0zXi5UP//g/kueWdc8/zqNxCtc55WC19bD/9eDsa5FB7AeoPVG4UoKkSsx9of7GfiZl7/3fwjWA5LK77kdz2NHGfv0gasCy8b+YD/+thzlsfAr6uq1AiFhI1Dvklw3BU9uxjoFSvznD11itbweXolJ87VgnVl1Mxa/vJXCWNR18Mhor5v6YvSP9xFqydL6wfOvibV04pMH6P/E5de8kpn4Svi+3cxLp3sY+DlXZ67FfPpKmP/FQ1Si8jJanVeS9dRvHaT3lzbV3/SMN/eJzpOdTJxtv6Rn39egMyHEnwshloUQZzeVXTOBjXDx+0KIcSHES0KIPS9/5mvDOnw1Y2BD79Wuo9IRdOx5bUFnpeYN90/bvPrW/YuXl50buzxgKZe/PPl6bsvLZzkfndsIQAqEXX/7ka+7MQ3pjOtmaBavDtS5Foz6V5dE3lq+tmti/OD1mR5VjyuXK4PsroQZvT77qRJ03emcasDNB26/mj8nOPPGhrZE9i1fv8LrSGj+amFH3edv+t17MlqvTlQudYfmcPaSu+318IcH/++1r9PrPn8n4N5M+MK1PcJF1euo2PzqJnjC+8YJZ+qxvqvKsskAdkOF+3acR/wDLhU9q5GNu6SL73nbq+MlArAjV19UeK7fkHNH+i57VoW26+v8tVCuk24e9U1Q/C/fVwAo/ZcHHOrRjdgg+76NGI6LRzrchEnfA7ziykAIcScuAd1fSSl3VMt+B1jdxD8Uk1L+RyHEu3GZS98N7Ac+LaXc/0qN8Dd3yi0f+Xfk2yVORwmnojLUs4iUgnTZixCSTMFLYTnAlsFFpk6240m6NlH9bWtk4iG0tEr7bpcBc+XFZsodJvqyjuwq0lqfZvFkC3QVETM+AlvXcKTAshVKBYMf2/kizyT6Wf56B7YXKtsKdDetYjsK6aKX1HKIg9vG2RFa4E9fuBMkdD8MM+9W+Ll7HuczTzyAnlawBgroo35KrSaoEiWjEbyokLm1gua3sNIGzd2rOH/TiKOD8/5VYv4i+YrBUjyKP1LEeSlC150zjE60oocqtNRlMB0F+wtNZN6TIxossjxZz45bL1KxVX6y4xl+47M/SmZHBW+kTDRYID4fQ1/RMQZdP+3KSBgzYnPvnmEWi2Emnu0msnuFxGwMhERPavjjgnyHxDEkakHg9BS5p3+Mp8YGkWkDKSSxl1Qq92cwz4Vp3b9I1FNk/i/7WN0pUYsC0Z/no1uP8JfD+1FGgpRbTXx1RSpTIYzeLJwK03N4mpGTXQjp0hFbDSZtbasUv9yMVAXBBZuP/dbf8/u/92HW9ldobk5j2gp7m2d5cnwIj8ekNBNCbSmCcL247u4d46mndoHAzZ+tSbbsnGNmNUZ5yY/WUMTndQcq60gMx4Byf4n+9gQTw20QsojE8jSHsoyMtOOpL2LOBZCGRCkqePoyOI6CEJItjSuMJxoopr3UNWUolg0igSLZp5vxvGOF7DnXnVbNKzgNJp5AhdKqF6RgcGCBmdUYP7jlNJ8/dTveYIXQY0HS78pTSXtQCipIt9Ova027OXzPhVym2JxOtDnLXR3jPPLC21BjZYbalkj9QReJ3QqVBgsMB/+oB2dPltCjQVLvymNbKupcNQGQ38J73kdlR4E7+8Z56vwQPR0rTM80EogVyS8HOHjrGC8ttWGeD3PrnWOcmOwieNpLeX8OszrZ2Lt7nOFHhyjc4rLwiorgR+55jpliHc+eGSB8Qcc4vEKu6MEyVeqjOQzVZu2JVg4/9CIPv7gHpaggbMFXf/iTfGL6wwzPt8CCF9+yguV1+agq7RWC0SK5NT9KSsObUKjcWmB7u7taP/9MH5W2CjgCJa2h5wTb7h7n/DcHLrkc6zmBf8camakoTtBCyWn4uzPU/XmQ1SGNXL+Ft6GINRXk4B3nWC6GWHi0G+5cIzcfRqsrYWYNtFWN6I4kW+vjHH9kB4Uui8YjKv5/sshMvI4Htg7z5Nd3U2m0CTTlsU9H2Hp4jPHVBrLJAKrXJvSsD9sH+V0un5pxOoAZlDTfHmd2tp72jlUWJhqRiqt3elbByEChReLpy9AZS7H4cDf/8uMP89f/+b0svl0w/W8/8f3lJhJC9ACPbhoMrpnARgjxJ9Xvn7+y3vXOHxpqkXs++2PEU2FMU8V72k+p0eHAoQscfWorHQfmWXm0g0KrxArZYDioaQ2l7GYm8p/2kb+1RDBSpC2cwZYK45MtqBmV5u3LLJ9uxg46RDrS3NUxzsNH96Cvqdx330me+NZuvMuCQrtD69Zl4skIctkDEvbvH+HcF7ey50fO8O0j24n0pCiciaFYAhxQLChvL+L1VfAZJvnn3TSQagmUW1yKWSnh1j7XXHR2ug1FkygXvVhhh46BZdYKPvKzIVBg3+4xRpONDNYnODLSRyBapFLWsJJeRLSCiHuInROs3FkhctJDpt+BaIVItEDhTAzZX6A+miM+VU9j9xqFsoGu2qQWw0RbM5SP1aHuSbmpHo/E0IqQv72IOu11U0/2ZakLFlg+1YzV5sZQKCUFJ2CzfWCO0Rd6sDtKCAF2SaWtfZXF0UakJvE1FSgtBvC15XDORGB7FnsiiLEmOPih03z7WzuxDUmgL0121c1eZaa8aGkVq6mC4TdhNIDVW+LB7Sf54tndBE75yPZbCFug1JexMwbCFkhVYsRK2LaCAKyyyv3bh3lieBv/dPcLPD6/FUO1iR9vwbsiXNrfeR+OVyI1SV17irXJOmTAQlvVETb4lgSZWytQVugbjBN/soOO+2aoOCrTk02EL+jIu9b4T9u+ypFsP5ri8Lcn96JkNDwdOWLBgkt33lckcNKHetfqJZIyYYNwwA657pj+WQ1Hg6HDE5yZacOxFHd1Nu+jYXuC7Heb0HNQaoA7HjiNR7GYzNVT5ykw/tlbSO4UvOfwUR4+sRuhO3jHPHj3J2kK5hiZaiVcnyd7MQKxCkrCIDwmSN3qoNWVsC2F9qYUJUsjMROjb8BNRJ9+tplyg0PLLcssnWl26a4byqiaQ0tdhtn5erAEoqgiAxax4zqpA2X6OxJcPNKB1VbhwMAkpx/bSqHLorlrlcpXGol8aIGQUSZV8pEtechdiGE1VVx//qRGw23LJIYbcepNwic8/PA/f5I/ffEOtBUdu6UCQqJoEm3CS7nZAk0iNIfQKS/ZHofe7QtMXWh1M6sI8CRU7FvyOFLgPe2nsL2Ed8yLZ+8qIW+ZxPOt2F53wmNGHIQpXBbQHXOMTLair+iYDSaR0wa5LonjcWg8qiAeSrC0EMUbKVNa89LXt8TkdBOxYzqpQ2W8o17q3hFnaTXMwd5JXpjuRRsOEDyQIDEXRXhtettXmJp3Q/M8E15KPRU+ceBxPvn0u1DzKo7XIdSZwWeYrJ1oxAxJ9JaC685eVlAbSxzsneT4Izuov3uRuaUYF//pL9/wwSC1nrNACCGANSllVAjxKPDbUspnqseeBP6jlPKqVEHVZDjrCXF2AGevrHOTogG4OlPIzYmaLDZQk8UGarLYwJCUMvRGnOgfTEchpZRiPdb6tf3f/wT+J4AQ4tgbNbr9Y0dNFhuoyWIDNVlsoCaLDQghXltOzuvg9e7mLVXNQ1T/ru/izQOdm+p1VMtqqKGGGmp4E+P1DgaPAB+tfv8o8PCm8h+vehUdANKvtF9QQw011FDDjccrmomEEJ8H7gYahBBzwK8Cvw38rRDiY1QT2FSrfxXXk2gcKAA/8Srb8YYETbxFUJPFBmqy2EBNFhuoyWIDb5gs3hRBZzXUUEMNNdxYvKmS29RQQw011HBjUBsMaqihhhpquPGDgRDiASHESJXC4hdvdHu+1xBCdAohviWEGBZCnBNC/Hy1/HtG8fFmhhBCFUKcrMaoIIToFUIcqd7v3wghjGq5p/p7vHq850a2+42GECIqhPiiEOKCEOK8EOLgTawT/7b6bpwVQnxeCOG9mfTijaIAEkJ8tFp/TAjx0WtdazNu6GAghFCBPwTeBWwDHhJCbLv+f/2jhwX8eynlNuAA8DPVe/5F4Ekp5QDwZPU3uLIZqH5+Cvij73+Tv6f4eeD8pt//HfiUlHILsAZ8rFr+Mdzgxi3Ap6r13kr4NPB1KeUtwG24MrnpdEII0Q78HLC3GuSqAh/h5tKLvwQeuKLsNemCEKIO19lnP7AP+NX1AeRlIaW8YR/gIPD4pt+/BPzSjWzTDZDBw8A7gRGgtVrWCoxUv/8J8NCm+pfq/WP/4MahPAncCzyKSxG2AmhX6gfwOHCw+l2r1hM3+h7eIDlEgKkr7+cm1Yl2YBaoqz7nR4EfuNn0AugBzr5eXQAeAv5kU/ll9a71udFmovUHv465atlNgeqSdjdwBGiWGzEZcWCd9vStLKPfA34BLuWHrwdSUsp1WsbN93pJDtXj6Wr9twJ6gQTwF1WT2f8SQgS4CXVCSjkPfBKYARZxn/Nxbk692IzXqguvWUdu9GBw00IIEQT+Dvg3UsrM5mPSHcrf0j6/Qoj3AstSyuM3ui1vAmjAHuCPpJS7gTwbZgDg5tAJgKop4wO4A2QbEOBqk8lNje+VLtzoweCmpK8QQui4A8H/kVJ+qVp8s1F8vB14vxBiGvgCrqno00BUCLEeDLn5Xi/JoXo8AiS/nw3+HmIOmJNSrmfh+SLu4HCz6QTAfcCUlDIhpTSBL+Hqys2oF5vxWnXhNevIjR4MjgIDVU8BA3ej6JEb3KbvKYQQAvgz4LyU8nc3HbqpKD6klL8kpeyQUvbgPvenpJQ/CnwLeLBa7Uo5rMvnwWr9t8RMWUoZB2aFEEPVosPAMDeZTlQxAxwQQvir78q6LG46vbgCr1UXHgfuF0LEqqut+6tlL483wUbJu4FRYAL4lRvdnu/D/b4Dd4n3EnCq+nk3rp3zSWAM+CZQV60vcD2uJoAzuF4WN/w+3mCZ3I1LkQ7QB7yIS2ny/wBPtdxb/T1ePd53o9v9BstgF3CsqhdfBmI3q04Avw5cwKW1/9+A52bSC+DzuPslJu6q8WOvRxeAn6zKZRz4iVe6bo2OooYaaqihhhtuJqqhhhpqqOFNgNpgfzCvqgAAADtJREFUUEMNNdRQQ20wqKGGGmqooTYY1FBDDTXUQG0wqKGGGmqogdpgUEMNNdRQA7XBoIYaaqihBuD/A5wJPu9r/ETmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(np.load('cache/place_critic_weights.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x11b0635f8>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "plt.imshow(np.load('cache/place_actor_weights.npy'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.51168221, 2.5713115 , 2.42747747])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# W = np.arange(15).reshape((3,5))\n",
    "W = np.random.normal(loc=0.5, scale=0.1, size=(3, 5))\n",
    "X = np.array([1,1,1,1,1])\n",
    "\n",
    "np.dot(W,X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
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      "/Users/nolandey/anaconda/envs/tensorflow3/lib/python3.6/site-packages/gym/logger.py:30: UserWarning: \u001b[33mWARN: Box bound precision lowered by casting to float32\u001b[0m\n",
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      "text/plain": [
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      "text/plain": [
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
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      "text/plain": [
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       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
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       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
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    {
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       "\n",
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       "\n",
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       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
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      "text/plain": [
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     "metadata": {},
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    {
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       "\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
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       "\n",
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       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
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       "                fill.style.animation = 'none';\n",
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    {
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
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    {
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       "\n",
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      "text/plain": [
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    {
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       "\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
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    {
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       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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       "\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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    {
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       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
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       "                fill.style.animation = 'none';\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
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    {
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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       "            fill.style.backgroundSize = '100px 100%';\n",
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     },
     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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     "metadata": {},
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      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
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   ],
   "source": [
    "import gym\n",
    "T = 1\n",
    "\n",
    "class Master():\n",
    "    def __init__(self):\n",
    "        self.env = gym.make('Pendulum-v0')\n",
    "        self.env.seed(0)\n",
    "        self.state = self.env.reset()\n",
    "        self.done = False\n",
    "        self.stepsize = 50\n",
    "        self.dt = 1e-3\n",
    "        \n",
    "    def get_state(self, t):\n",
    "        return self.state\n",
    "    \n",
    "    def step(self, t):\n",
    "        if int(t / self.dt) % self.stepsize != 0:\n",
    "            return 0\n",
    "        \n",
    "        action = self.env.action_space.sample()\n",
    "        self.state, reward, self.done, info = self.env.step(action)\n",
    "        if self.done:\n",
    "            self.state = self.env.reset()\n",
    "        return 0\n",
    "\n",
    "\n",
    "n_neuron_vals = np.arange(100,1100,100)            \n",
    "rmses = []\n",
    "saved_place, saved_state = None, None\n",
    "for n_place in n_neuron_vals:\n",
    "    rmse_trials = []\n",
    "    for trial in range(5):\n",
    "        model = nengo.Network()\n",
    "        master = Master()\n",
    "        with model:\n",
    "            step_node = nengo.Node(master.step)\n",
    "            state_node = nengo.Node(master.get_state)\n",
    "            place = nengo.Ensemble(n_place, 3, radius=5)\n",
    "            nengo.Connection(state_node, place)\n",
    "\n",
    "            place_probe = nengo.Probe(place, synapse=None)\n",
    "            state_probe = nengo.Probe(state_node, synapse=None)\n",
    "\n",
    "        with nengo.Simulator(model) as sim:\n",
    "            sim.run(T)\n",
    "\n",
    "        master.env.close()\n",
    "\n",
    "        rmse = np.sqrt(np.mean((sim.data[place_probe] - sim.data[state_probe])**2))\n",
    "        rmse_trials.append(rmse)\n",
    "    rmses.append(np.mean(rmse_trials))\n",
    "    if n_place == 600:\n",
    "        saved_place = sim.data[place_probe]\n",
    "        saved_state = sim.data[state_probe]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x133ab1978>"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(16,5))\n",
    "ax[0].plot(n_neuron_vals, rmses, label='Place neurons')\n",
    "ax[0].plot(n_neuron_vals, 0.3+200/n_neuron_vals, label='1 / n_neurons', linestyle='--', color='black')\n",
    "ax[0].set_xlabel('Number of place neurons')\n",
    "ax[0].set_ylabel('RMSE of place representation')\n",
    "ax[0].set_title('Error of place representation')\n",
    "ax[0].legend()\n",
    "\n",
    "end = -1\n",
    "ax[1].plot(sim.trange(), saved_place, label='decoded')\n",
    "ax[1].plot(sim.trange(), saved_state, label='ideal', linestyle='--', color='black')\n",
    "# ax[1].plot(sim.trange()[:end], sim.data[place_probe][:end], label='decoded')\n",
    "# ax[1].plot(sim.trange()[:end], sim.data[state_probe][:end], label='ideal', linestyle='--', color='black')\n",
    "ax[1].set_xlabel('t')\n",
    "ax[1].set_title('State and place representation')\n",
    "# ax[1].set_ylabel('RMSE of state representation')\n",
    "ax[1].legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
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       "                  <div class=\"pb-text\" style=\"\n",
       "                      position: absolute;\n",
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       "                    0%\n",
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       "                  <div class=\"pb-fill\" style=\"\n",
       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "\n",
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       "                  var text = root.getElementsByClassName('pb-text')[0];\n",
       "                  var fill = root.getElementsByClassName('pb-fill')[0];\n",
       "\n",
       "                  text.innerHTML = 'Build finished in 0:00:01.';\n",
       "                  \n",
       "            fill.style.width = '100%';\n",
       "            fill.style.animation = 'pb-fill-anim 2s linear infinite';\n",
       "            fill.style.backgroundSize = '100px 100%';\n",
       "            fill.style.backgroundImage = 'repeating-linear-gradient(' +\n",
       "                '90deg, #bdd2e6, #edf2f8 40%, #bdd2e6 80%, #bdd2e6)';\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
       "            \n",
       "              })();\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
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       "                    border-radius: 4px;\n",
       "                    text-align: center;\n",
       "                    position: relative;\">\n",
       "                  <div class=\"pb-text\" style=\"\n",
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       "                    0%\n",
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       "                      background-color: #bdd2e6;\n",
       "                      width: 0%;\">\n",
       "                    <style type=\"text/css\" scoped=\"scoped\">\n",
       "                        @keyframes pb-fill-anim {\n",
       "                            0% { background-position: 0 0; }\n",
       "                            100% { background-position: 100px 0; }\n",
       "                        }\n",
       "                    </style>\n",
       "                    &nbsp;\n",
       "                  </div>\n",
       "                </div>"
      ],
      "text/plain": [
       "HtmlProgressBar cannot be displayed. Please use the TerminalProgressBar. It can be enabled with `nengo.rc.set('progress', 'progress_bar', 'nengo.utils.progress.TerminalProgressBar')`."
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
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       "\n",
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       "            fill.style.backgroundImage = 'none'\n",
       "        \n",
       "                  \n",
       "                fill.style.animation = 'none';\n",
       "                fill.style.backgroundImage = 'none';\n",
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   ],
   "source": [
    "n_place = 100\n",
    "n_actor = 100\n",
    "tau_r = 10 # reward time constant\n",
    "v_k = 30e-3#50e-3\n",
    "tau_k = 50e-3#200e-3\n",
    "eps_0 = 10e-3#20e-3\n",
    "tau_m = 10e-3#20e-3\n",
    "tau_s = 5e-3\n",
    "tau_gamma = 50e-3\n",
    "v_gamma = 20e-3\n",
    "\n",
    "def get_state(t):\n",
    "    return np.random.uniform(size=(3))\n",
    "\n",
    "def outer(t, x):\n",
    "    X_conv_eps = x[:n_place]\n",
    "    Y = x[n_place:]\n",
    "    return np.outer(Y, X_conv_eps).flatten()\n",
    "    \n",
    "\n",
    "model = nengo.Network(seed=0)\n",
    "with model:\n",
    "    state_node = nengo.Node(get_state)\n",
    "    place = nengo.Ensemble(n_place,3)\n",
    "    actor = nengo.Ensemble(n_actor,1)\n",
    "#     nengo.Connection(state_node, place, solver=nengo.solvers.NoSolver())\n",
    "    nengo.Connection(state_node, place)\n",
    "    \n",
    "    \n",
    "    actor_outer = nengo.Node(outer,\n",
    "                            size_in=n_place + n_actor,\n",
    "                            size_out=n_place * n_actor)\n",
    "    # convolve place spikes with epsilon\n",
    "    nengo.Connection(place.neurons,\n",
    "                     actor_outer[:n_place],\n",
    "                     synapse=tau_m,\n",
    "                     transform=((eps_0 * tau_m) / (tau_m - tau_s)))\n",
    "    nengo.Connection(place.neurons,\n",
    "                     actor_outer[:n_place],\n",
    "                     synapse=tau_s,\n",
    "                     transform=-((eps_0 * tau_s) / (tau_m - tau_s)))\n",
    "    # Pass raw actor spikes into actor_outer\n",
    "    nengo.Connection(actor.neurons, actor_outer[n_place:], synapse=None)\n",
    "\n",
    "    \n",
    "    yolo = nengo.Node(100*np.ones(n_actor))\n",
    "    nengo.Connection(yolo, actor.neurons)\n",
    "    \n",
    "    \n",
    "    action_selection_node = nengo.Node(output=None, size_in=1)\n",
    "    nengo.Connection(actor,\n",
    "                     action_selection_node, \n",
    "                     synapse=tau_gamma,\n",
    "                     transform=tau_gamma/(tau_gamma - v_gamma))\n",
    "    nengo.Connection(actor,\n",
    "                     action_selection_node,\n",
    "                     synapse=v_gamma,\n",
    "                     transform=(-v_gamma)/(tau_gamma - v_gamma))\n",
    "    \n",
    "    \n",
    "    actor_spikes = nengo.Probe(actor.neurons)\n",
    "    place_spikes = nengo.Probe(place.neurons)\n",
    "    \n",
    "sim = nengo.Simulator(model)\n",
    "with sim:\n",
    "    sim.run(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Place neuron spikes')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(12,4))\n",
    "end = 10\n",
    "rasterplot(sim.trange()[:end], sim.data[actor_spikes][:end], ax=ax[0])\n",
    "ax[0].set_xlabel('t')\n",
    "ax[0].set_ylabel('Neuron index')\n",
    "ax[0].set_title('Actor neuron spikes')\n",
    "\n",
    "rasterplot(sim.trange()[:end], sim.data[place_spikes][:end], ax=ax[1])\n",
    "ax[1].set_xlabel('t')\n",
    "ax[1].set_ylabel('Neuron index')\n",
    "ax[1].set_title('Place neuron spikes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13188b710>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "end = 10\n",
    "rasterplot(sim.trange()[:end], sim.data[place_spikes][:end])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0.98,'Synaptic filters')"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tau_r = 4 # reward time constant\n",
    "# v_k = 30e-3#50e-3\n",
    "# tau_k = 50e-3#200e-3\n",
    "v_k = 50e-3\n",
    "tau_k = 200e-3\n",
    "eps_0 = 20e-3\n",
    "tau_m = 20e-3\n",
    "tau_s = 5e-3\n",
    "\n",
    "# eps_0 = 5e-3\n",
    "# tau_m = 5e-3\n",
    "# tau_s = 2e-3\n",
    "\n",
    "tau_gamma = 50e-3\n",
    "v_gamma = 20e-3\n",
    "\n",
    "def k(t):\n",
    "    return ( np.exp(-t/tau_k) - np.exp(-t/v_k)) / (tau_k-v_k)\n",
    "\n",
    "def k_dot(t):\n",
    "    return ( -np.exp(-t/tau_k)/tau_k + np.exp(-t/v_k)/v_k) / (tau_k-v_k)\n",
    "\n",
    "def epsp(s):\n",
    "    return (eps_0/(tau_m - tau_s))*(np.exp(-s/tau_m)-np.exp(-s/tau_s))*np.heaviside(s, 1)\n",
    "\n",
    "def gamma(t):\n",
    "    return ( np.exp(-t/tau_gamma) - np.exp(-t/v_gamma)) / (tau_gamma-v_gamma)\n",
    "\n",
    "ts = np.arange(0,1,1e-3)\n",
    "fig, ax = plt.subplots(1,3, figsize=(12,3))\n",
    "ax[0].plot(ts, k(ts), label=r'$\\kappa(t)$')\n",
    "ax[0].plot(ts, gamma(ts), label=r'$\\gamma(t)$')\n",
    "ax[1].plot(ts, k_dot(ts), label=r'$\\dot{\\kappa}(t)$')\n",
    "ax[1].plot(ts, k_dot(ts) - k(ts)/tau_r, label=r'$(\\dot{\\kappa} - \\frac{\\kappa}{\\tau_r})(t)$')\n",
    "\n",
    "ts = np.arange(0,0.1,1e-3)\n",
    "ax[2].plot(ts, epsp(ts), label=r'$\\epsilon(t)$')\n",
    "for i in range(3):\n",
    "    ax[i].legend()\n",
    "    ax[i].set_xlabel('t')\n",
    "    \n",
    "plt.suptitle('Synaptic filters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xs = []\n",
    "def yolo(t, x):\n",
    "    xs.append(x)\n",
    "\n",
    "model = nengo.Network()\n",
    "with model:\n",
    "    nd_input = nengo.Node(lambda t: 1 if t % 0.1 == 0 else 0 ,size_out=1)\n",
    "    td_error_node = nengo.Node(yolo, size_in=1)\n",
    "#     nengo.Connection(critic.neurons, td_error_node, synapse=None)\n",
    "    # convolve critic spikes with (K' - K/tau_r)\n",
    "    nengo.Connection(nd_input,\n",
    "                     td_error_node,\n",
    "                     synapse=tau_k,\n",
    "                     transform=((-tau_r - tau_k)/(tau_r*(tau_k-v_k))))\n",
    "    nengo.Connection(nd_input,\n",
    "                     td_error_node,\n",
    "                     synapse=v_k,\n",
    "                     transform=((tau_r + v_k)/(tau_r*(tau_k-v_k))))\n",
    "    \n",
    "with nengo.Simulator(model) as sim:\n",
    "    sim.run(5)\n",
    "    \n",
    "plt.plot(xs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nengo.Network()\n",
    "with model:\n",
    "    nd_input = nengo.Node(lambda t: 1 if 0.1 < t < 0.4 else 0)\n",
    "    ens1 = nengo.Ensemble(100,1)\n",
    "    nengo.Connection(nd_input, ens1, synapse=0.05, transform=-1)\n",
    "#     nengo.Connection(nd_input, ens1, synapse=0.05, transform=1)\n",
    "    p0 = nengo.Probe(nd_input)\n",
    "    p1 = nengo.Probe(ens1)\n",
    "    p2 = nengo.Probe(ens1.neurons, attr='spikes')\n",
    "    \n",
    "dt = 1e-3\n",
    "T = 0.5\n",
    "with nengo.Simulator(model, dt=dt) as sim:\n",
    "    sim.run(T)\n",
    "\n",
    "ts = np.arange(0,T,dt)\n",
    "fig, ax = plt.subplots(1,3,figsize=(10,3))\n",
    "ax[0].plot(ts, sim.data[p0])\n",
    "ax[0].set_xlabel('t')\n",
    "ax[0].set_title('Input')\n",
    "ax[1].plot(ts, sim.data[p1])\n",
    "ax[1].set_xlabel('t')\n",
    "ax[1].set_title('First Ensemble')\n",
    "ax[2].plot(ts, sim.data[p2][:])\n",
    "ax[2].set_xlabel('t')\n",
    "ax[2].set_title('Second Ensemble')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = nengo.Network()\n",
    "with model:\n",
    "    a = nengo.Ensemble(n_neurons=100, dimensions=1)\n",
    "    b = nengo.Ensemble(n_neurons=50, dimensions=1)\n",
    "    \n",
    "w = 2*np.random.randn(b.n_neurons, a.n_neurons)/b.n_neurons\n",
    "def my_rule(t, x):\n",
    "    global w\n",
    "    output = np.dot(w, x)*0.001\n",
    "    w += np.random.randn(*w.shape)*0.01   # learning rule\n",
    "    return output\n",
    "\n",
    "def test(t,x):\n",
    "    ensemble_spikes = x[:b.n_neurons]\n",
    "    learner_spikes = x[b.n_neurons:]\n",
    "    return np.sum(np.abs(ensemble_spikes - learner_spikes))\n",
    "\n",
    "with model:\n",
    "    learner = nengo.Node(my_rule, size_in=a.n_neurons,\n",
    "                         size_out=b.n_neurons)\n",
    "\n",
    "    nengo.Connection(a.neurons, learner, synapse=None)\n",
    "    nengo.Connection(learner, b.neurons, synapse=0.05)\n",
    "    \n",
    "    test_node = nengo.Node(test, size_in=2*b.n_neurons)\n",
    "    nengo.Connection(b.neurons, test_node[:b.n_neurons], synapse=None)\n",
    "    nengo.Connection(learner, test_node[b.n_neurons:], synapse=None)\n",
    "    \n",
    "    test_probe = nengo.Probe(test_node, synapse=None)\n",
    "    \n",
    "with nengo.Simulator(model) as sim:\n",
    "    sim.run(1)\n",
    "    \n",
    "plt.plot(sim.data[test_probe])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nengo' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m----------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                      Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-982594c11b54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mw\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mlearning_rate\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdelta\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0me\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLearningEnsemble\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     25\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\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-1-982594c11b54>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, n_neurons, dimensions, seed)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mLearningEnsemble\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\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      2\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_neurons\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m128\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdimensions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\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----> 3\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnengo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNetwork\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mseed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mseed\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      4\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m             self.ens = nengo.Ensemble(n_neurons=n_neurons,\n",
      "\u001b[0;31mNameError\u001b[0m: name 'nengo' is not defined"
     ]
    }
   ],
   "source": [
    "class LearningEnsemble(object):\n",
    "    def __init__(self, n_neurons=128, dimensions=4, seed=0):\n",
    "        self.model = nengo.Network(seed=seed)\n",
    "        with self.model:\n",
    "            self.ens = nengo.Ensemble(n_neurons=n_neurons,\n",
    "                                      dimensions=dimensions,\n",
    "                                      radius=2)\n",
    "        self.sim = nengo.Simulator(self.model)\n",
    "        \n",
    "        self.w = np.zeros((3, n_neurons))\n",
    "        \n",
    "    def forward(self, x):\n",
    "        _, act = nengo.utils.ensemble.tuning_curves(self.ens, self.sim, inputs=x)\n",
    "        print(act.shape)\n",
    "        y = self.w.dot(act)\n",
    "        return y\n",
    "    \n",
    "    def train(self, x, error, learning_rate=1e-4):\n",
    "        _, act = nengo.utils.ensemble.tuning_curves(self.ens, self.sim, inputs=x)\n",
    "        \n",
    "        delta = error.T.dot(act)\n",
    "        self.w += -learning_rate*delta\n",
    "\n",
    "e = LearningEnsemble()\n",
    "e.forward([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "env = gym.make('Pendulum-v0')\n",
    "for i_episode in range(n_episodes):\n",
    "    observation = env.reset()\n",
    "    print(observation)\n",
    "    for t in range(100):\n",
    "        env.render()\n",
    "        action = env.action_space.sample()\n",
    "        observation, reward, done, info = env.step(action)\n",
    "        print(action, observation, reward, done, info)\n",
    "        if done:\n",
    "            print(\"Episode finished after {} timesteps\".format(t+1))\n",
    "            break\n",
    "env.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x133a1ee10>]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def angle_normalize(x):\n",
    "    return (((x+np.pi) % (2*np.pi)) - np.pi)\n",
    "xs = np.arange(-5,5,1e-3)\n",
    "plt.plot(angle_normalize(xs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}