diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..3dc1e5e 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -28,17 +28,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -103,14 +95,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/andrew/miniconda2/envs/cedl/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + "D:\\Anaconda3\\envs\\asus\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] } @@ -152,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -214,6 +206,7 @@ " Sample solution should be only 1 line.\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>\n", + " a = r - b\n", " # <<<<<<<<\n", "\n", " p[\"returns\"] = r\n", @@ -258,98 +251,87 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 14.85\n", - "Iteration 2: Average Return = 15.59\n", - "Iteration 3: Average Return = 16.61\n", - "Iteration 4: Average Return = 17.43\n", - "Iteration 5: Average Return = 17.08\n", - "Iteration 6: Average Return = 17.24\n", - "Iteration 7: Average Return = 21.3\n", - "Iteration 8: Average Return = 21.42\n", - "Iteration 9: Average Return = 20.62\n", - "Iteration 10: Average Return = 26.82\n", - "Iteration 11: Average Return = 28.0\n", - "Iteration 12: Average Return = 28.41\n", - "Iteration 13: Average Return = 28.96\n", - "Iteration 14: Average Return = 28.15\n", - "Iteration 15: Average Return = 30.64\n", - "Iteration 16: Average Return = 36.2\n", - "Iteration 17: Average Return = 38.13\n", - "Iteration 18: Average Return = 34.5\n", - "Iteration 19: Average Return = 40.37\n", - "Iteration 20: Average Return = 35.78\n", - "Iteration 21: Average Return = 47.81\n", - "Iteration 22: Average Return = 47.21\n", - "Iteration 23: Average Return = 43.34\n", - "Iteration 24: Average Return = 46.1\n", - "Iteration 25: Average Return = 50.25\n", - "Iteration 26: Average Return = 51.02\n", - "Iteration 27: Average Return = 59.81\n", - "Iteration 28: Average Return = 57.49\n", - "Iteration 29: Average Return = 61.39\n", - "Iteration 30: Average Return = 62.26\n", - "Iteration 31: Average Return = 61.98\n", - "Iteration 32: Average Return = 62.16\n", - "Iteration 33: Average Return = 59.89\n", - "Iteration 34: Average Return = 73.46\n", - "Iteration 35: Average Return = 78.51\n", - "Iteration 36: Average Return = 72.79\n", - "Iteration 37: Average Return = 78.74\n", - "Iteration 38: Average Return = 86.95\n", - "Iteration 39: Average Return = 94.08\n", - "Iteration 40: Average Return = 97.58\n", - "Iteration 41: Average Return = 103.42\n", - "Iteration 42: Average Return = 101.17\n", - "Iteration 43: Average Return = 112.39\n", - "Iteration 44: Average Return = 115.09\n", - "Iteration 45: Average Return = 134.65\n", - "Iteration 46: Average Return = 138.92\n", - "Iteration 47: Average Return = 147.15\n", - "Iteration 48: Average Return = 152.35\n", - "Iteration 49: Average Return = 149.66\n", - "Iteration 50: Average Return = 148.15\n", - "Iteration 51: Average Return = 144.82\n", - "Iteration 52: Average Return = 144.43\n", - "Iteration 53: Average Return = 153.21\n", - "Iteration 54: Average Return = 163.66\n", - "Iteration 55: Average Return = 154.28\n", - "Iteration 56: Average Return = 155.07\n", - "Iteration 57: Average Return = 161.53\n", - "Iteration 58: Average Return = 166.28\n", - "Iteration 59: Average Return = 174.05\n", - "Iteration 60: Average Return = 172.8\n", - "Iteration 61: Average Return = 170.78\n", - "Iteration 62: Average Return = 179.58\n", - "Iteration 63: Average Return = 174.84\n", - "Iteration 64: Average Return = 175.74\n", - "Iteration 65: Average Return = 174.99\n", - "Iteration 66: Average Return = 187.7\n", - "Iteration 67: Average Return = 178.94\n", - "Iteration 68: Average Return = 182.74\n", - "Iteration 69: Average Return = 181.42\n", - "Iteration 70: Average Return = 182.19\n", - "Iteration 71: Average Return = 184.58\n", - "Iteration 72: Average Return = 181.9\n", - "Iteration 73: Average Return = 184.29\n", - "Iteration 74: Average Return = 188.8\n", - "Iteration 75: Average Return = 190.46\n", - "Iteration 76: Average Return = 188.89\n", - "Iteration 77: Average Return = 187.9\n", - "Iteration 78: Average Return = 190.19\n", - "Iteration 79: Average Return = 186.28\n", - "Iteration 80: Average Return = 189.1\n", - "Iteration 81: Average Return = 188.16\n", - "Iteration 82: Average Return = 191.32\n", - "Iteration 83: Average Return = 192.03\n", - "Iteration 84: Average Return = 195.45\n", - "Solve at 84 iterations, which equals 8400 episodes.\n" + "Iteration 1: Average Return = 21.07\n", + "Iteration 2: Average Return = 21.19\n", + "Iteration 3: Average Return = 22.78\n", + "Iteration 4: Average Return = 23.49\n", + "Iteration 5: Average Return = 26.73\n", + "Iteration 6: Average Return = 28.43\n", + "Iteration 7: Average Return = 31.26\n", + "Iteration 8: Average Return = 31.66\n", + "Iteration 9: Average Return = 35.19\n", + "Iteration 10: Average Return = 33.59\n", + "Iteration 11: Average Return = 37.98\n", + "Iteration 12: Average Return = 43.08\n", + "Iteration 13: Average Return = 37.68\n", + "Iteration 14: Average Return = 41.63\n", + "Iteration 15: Average Return = 44.11\n", + "Iteration 16: Average Return = 43.79\n", + "Iteration 17: Average Return = 47.23\n", + "Iteration 18: Average Return = 50.12\n", + "Iteration 19: Average Return = 44.47\n", + "Iteration 20: Average Return = 49.58\n", + "Iteration 21: Average Return = 51.69\n", + "Iteration 22: Average Return = 54.15\n", + "Iteration 23: Average Return = 56.41\n", + "Iteration 24: Average Return = 55.97\n", + "Iteration 25: Average Return = 62.94\n", + "Iteration 26: Average Return = 66.16\n", + "Iteration 27: Average Return = 68.86\n", + "Iteration 28: Average Return = 66.3\n", + "Iteration 29: Average Return = 68.34\n", + "Iteration 30: Average Return = 64.17\n", + "Iteration 31: Average Return = 74.4\n", + "Iteration 32: Average Return = 73.79\n", + "Iteration 33: Average Return = 80.71\n", + "Iteration 34: Average Return = 86.18\n", + "Iteration 35: Average Return = 81.38\n", + "Iteration 36: Average Return = 86.97\n", + "Iteration 37: Average Return = 94.26\n", + "Iteration 38: Average Return = 94.9\n", + "Iteration 39: Average Return = 102.79\n", + "Iteration 40: Average Return = 102.27\n", + "Iteration 41: Average Return = 102.63\n", + "Iteration 42: Average Return = 104.65\n", + "Iteration 43: Average Return = 111.59\n", + "Iteration 44: Average Return = 102.15\n", + "Iteration 45: Average Return = 114.96\n", + "Iteration 46: Average Return = 107.59\n", + "Iteration 47: Average Return = 119.18\n", + "Iteration 48: Average Return = 121.63\n", + "Iteration 49: Average Return = 120.84\n", + "Iteration 50: Average Return = 122.97\n", + "Iteration 51: Average Return = 127.32\n", + "Iteration 52: Average Return = 131.71\n", + "Iteration 53: Average Return = 131.77\n", + "Iteration 54: Average Return = 139.71\n", + "Iteration 55: Average Return = 148.7\n", + "Iteration 56: Average Return = 147.63\n", + "Iteration 57: Average Return = 158.66\n", + "Iteration 58: Average Return = 161.01\n", + "Iteration 59: Average Return = 163.85\n", + "Iteration 60: Average Return = 167.88\n", + "Iteration 61: Average Return = 173.21\n", + "Iteration 62: Average Return = 175.32\n", + "Iteration 63: Average Return = 182.73\n", + "Iteration 64: Average Return = 176.16\n", + "Iteration 65: Average Return = 183.69\n", + "Iteration 66: Average Return = 188.0\n", + "Iteration 67: Average Return = 186.67\n", + "Iteration 68: Average Return = 189.56\n", + "Iteration 69: Average Return = 190.52\n", + "Iteration 70: Average Return = 191.62\n", + "Iteration 71: Average Return = 194.21\n", + "Iteration 72: Average Return = 192.15\n", + "Iteration 73: Average Return = 198.04\n", + "Solve at 73 iterations, which equals 7300 episodes.\n" ] } ], @@ -371,14 +353,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\nDTX+jM4LWgjRo/i85iLaToVFoC66Cv3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vDAAul4snn3ySL33pS+Tm5vb7muLiYoqLi32f19UNb1HdOy04Evriy+E3r9Hw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\n+xkulwuHw8FXv/pVZs6cSVtbG4899hhvvPEGN95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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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wq4/x+Ez0oV/R675CvznXOhRHSCOMqX+TZySEEE4lxcKJ9O6frXNC\nhIZhTP8HKigYdXULdP/bIHEXeudPqFtG2D0UhxBCVBcpFg6iszLgxBF0ZhpkpEFaCnrLOmjW3Dra\nq9+fMwEqw4D216PaX+/ExEII8ScpFg5gblmPfn8xFBVaFygF9QNQXXqhJj6C8vF1bkAhhKiAFItq\npIuL0B+9aR3xtUVbjJHjIbghBAShPOWjF0K4D/mNVU30qSTM12fD78dRt96Juv0emf9BCOG2pFhU\nMX0qCZ3wKXrzWvCpi/HYi6j2XZ0dSwghrohTi0VqaiqLFi0iMzMTpRRRUVEMGTKElStX8v333+Pv\nb+30HTNmDF27uvYvXH1gL+aa1bB3O3h5o3r3Rw0bjQoKdnY0IYS4Yk4tFh4eHowfP56IiAgKCgp4\n9tln6dixIwBDhw5l+PDhzoxnF22xWPsl1n9l7bS+bQzq5iEomVhICFGDOLVYBAUFERQUBEDdunVp\n0qQJ6enpzox0WXRBPuYbc+B/O6zPQ9x+jzw8J4SokVymzyIlJYWjR4/SokUL9u/fz5o1a9i4cSMR\nERFMmDABPz8/Z0csQ6eftU5ZeuokavxUjL6DnB1JCCGqjdJaa2eHKCws5IUXXuCOO+6gR48eZGZm\n2vorVqxYQUZGBtHR0eX2S0hIICEhAYCYmBiKi4srncHT05PS0tJLbmMW5FF67DdKjx4k77/L0UUF\nBDz1MnU631Dp814ue3K6AslZ9dwlq+SsWtWd09vb267tnF4sSktLmT17Np06dWLYsGHl1qekpDB7\n9mxiY2MrPFZycnKlc4SEhJB6gaG/9cmj6PVfo/fvgbOn4NzH1agJxsN/RTVpVulzVmVOVyM5q567\nZJWcVau6c4aHh9u1nVMvQ2mtWbJkCU2aNClTKDIyMmx9GVu3bqVp06aOzVVaYh0afN1XcDgRvLyh\nXVdUr36ops2h6bUQFIxSyqG5hBDCWZxaLA4cOMDGjRtp1qwZTz31FGC9TXbTpk0cO3YMpRShoaFM\nnjy52jLopKOYb8wl1bRgKSyEkmIoLoTSUggNQ901CXVjFKpe/WrLIIQQrs6pxaJ169asXLmy3HKH\nPlPh7QPhTfHyq49pamsrwssLdV0Ha2tC5pAQQgjXuRvKWVTDxnhMeZYAN7l+KYQQziB/NgshhKiQ\nFAshhBAVkmIhhBCiQlIshBBCVEiKhRBCiApJsRBCCFEhKRZCCCEqJMVCCCFEhZw+kKAQQgjXJy2L\nPzz77LPOjmAXyVm13CUnuE9WyVm1XCWnFAshhBAVkmIhhBCiQh4vvvjii84O4SoiIiKcHcEukrNq\nuUtOcJ+skrNquUJO6eAWQghRIbkMJYQQokK1fj6L3bt3s2zZMkzTZMCAAYwYMcLZkWwWL17Mzp07\nCQgIsM1BnpubS1xcHGfPniU0NJTp06fj5+fn1JypqaksWrSIzMxMlFJERUUxZMgQl8taXFzMCy+8\nQGlpKRaLhZ49ezJq1ChSUlKYN28eubm5XHvttTzyyCN4ejr/R8M0TZ599lkaNGjAs88+65I5p06d\nio+PD4Zh4OHhQUxMjMt938/Jy8tjyZIlnDx5EqUUDz/8MOHh4S6VNTk5mbi4ONvrlJQURo0aRWRk\npPNz6lrMYrHoadOm6dOnT+uSkhL95JNP6pMnTzo7ls2vv/6qf/vtNz1jxgzbsvfee0+vXr1aa631\n6tWr9XvvveeseDbp6en6t99+01prnZ+frx999FF98uRJl8tqmqYuKCjQWmtdUlKi//rXv+oDBw7o\n2NhY/eOPP2qttX799df1mjVrnBnT5vPPP9fz5s3T//rXv7TW2iVzRkdH66ysrDLLXO37fs6CBQt0\nQkKC1tr6/c/NzXXZrFpbfz898MADOiUlxSVy1urLUIcPHyYsLIxGjRrh6elJ79692bZtm7Nj2bRt\n27bcXw/btm0jMjISgMjISJfIGxQUZOuAq1u3Lk2aNCE9Pd3lsiql8PHxAcBisWCxWFBK8euvv9Kz\nZ08A+vXr5/ScAGlpaezcuZMBAwYAoLV2yZwX4mrfd4D8/Hz27dtH//79AfD09KRevXoumfWcvXv3\nEhYWRmhoqEvkdH5b24nS09MJDg62vQ4ODubQoUNOTFSxrKwsgoKCAOsv6ezsbCcnKislJYWjR4/S\nokULl8xqmibPPPMMp0+fZtCgQTRq1AhfX188PDwAaNCgAenp6U5OCe+88w7jxo2joKAAgJycHJfM\nCfDyyy8DMHDgQKKiolzy+56SkoK/vz+LFy/m+PHjREREMHHiRJfMes6mTZu48cYbAdf4ua/VxUJf\n4EYwpZQTktQMhYWFxMbGMnHiRHx9fZ0d54IMw2DOnDnk5eUxd+5cfv/9d2dHKmfHjh0EBAQQERHB\nr7/+6uw4l/TSSy/RoEEDsrKymDVrFuHh4c6OdEEWi4WjR49y33330bJlS5YtW0Z8fLyzY11UaWkp\nO3bsYOzYsc6OYlOri0VwcDBpaWm212lpabbq7aoCAgLIyMggKCiIjIwM/P39nR0JsP7jjo2N5aab\nbqJHjx6A62YFqFevHm3btuXQoUPk5+djsVjw8PAgPT2dBg0aODXbgQMH2L59O7t27aK4uJiCggLe\neecdl8sJ2DIEBATQvXt3Dh8+7JLf9+DgYIKDg2nZsiUAPXv2JD4+3iWzAuzatYtrr72WwMBAwDV+\nlmp1n0Xz5s05deoUKSkplJaWsnnzZrp16+bsWJfUrVs3NmzYAMCGDRvo3r27kxNZW2hLliyhSZMm\nDBs2zLbc1bJmZ2eTl5cHWO+M2rt3L02aNKFdu3Zs2bIFgPXr1zv938DYsWNZsmQJixYt4vHHH6d9\n+/Y8+uijLpezsLDQdpmssLCQPXv20KxZM5f7vgMEBgYSHBxMcnIyYO0PuOqqq1wyK5S9BAWu8bNU\n6x/K27lzJ++++y6maXLzzTdzxx13ODuSzbx580hMTCQnJ4eAgABGjRpF9+7diYuLIzU1lZCQEGbM\nmOH02xL379/P888/T7NmzWyX8caMGUPLli1dKuvx48dZtGgRpmmitaZXr17ceeednDlzptwtqV5e\nXk7Leb5ff/2Vzz//nGeffdblcp45c4a5c+cC1ss8ffr04Y477iAnJ8elvu/nHDt2jCVLllBaWkrD\nhg2Jjo5Ga+1yWYuKinj44YdZuHCh7XKuK3ymtb5YCCGEqFitvgwlhBDCPlIshBBCVEiKhRBCiApJ\nsRBCCFEhKRZCCCEqJMVC1EozZsxw2tPRqampjB8/HtM0nXJ+ISpDbp0VtdrKlSs5ffo0jz76aLWd\nY+rUqTz00EN07Nix2s4hRHWTloUQV8BisTg7ghAOIS0LUStNnTqV++67z/YEsqenJ2FhYcyZM4f8\n/Hzeffdddu3ahVKKm2++mVGjRmEYBuvXr+f777+nefPmbNiwgUGDBtGvXz9ef/11jh8/jlKKTp06\ncf/991OvXj0WLFjAjz/+iKenJ4ZhcOedd9KrVy+mTZvGhx9+aBvn6c0332T//v34+flx++23ExUV\nBVhbPklJSXh7e7N161ZCQkKYOnUqzZs3ByA+Pp6vv/6agoICgoKCeOCBB+jQoYPTPldRc9XqgQRF\n7ebl5cXIkSPLXYZauHAhgYGBzJ8/n6KiImJiYggODmbgwIEAHDp0iN69e7N06VIsFgvp6emMHDmS\nNm3aUFBQQGxsLKtWrWLixIk88sgj7N+/v8xlqJSUlDI5XnvtNZo2bcrrr79OcnIyL730Eo0aNbL9\n0t+xYwdPPPEE0dHRfPTRR7z99tu8/PLLJCcns2bNGv71r3/RoEEDUlJSpB9EVBu5DCXEeTIzM9m9\nezcTJ07Ex8eHgIAAhg4dyubNm23bBAUFceutt+Lh4YG3tzdhYWF07NgRLy8v/P39GTp0KImJiXad\nLzU1lf3793PPPffg7e3NNddcw4ABA9i4caNtm9atW9O1a1cMw6Bv374cO3YMsA63XlJSQlJSkm28\no7CwsCr9PIQ4R1oWQpwnNTUVi8XC5MmTbcu01mUmyQoJCSmzT1ZWFsuWLWPfvn0UFhZimqbdg7xl\nZGTg5+dH3bp1yxz/t99+s70OCAiwfe3t7U1JSQkWi4WwsDAmTpzIqlWrSEpKolOnTkyYMMElhi4X\nNY8UC1Gr/d/JroKDg/H09OStt96yzUpXkQ8++ACAuXPnUr9+fbZu3crbb79t175BQUHk5uZSUFBg\nKxipqal2/8Lv06cPffr0IT8/nzfeeIP//Oc/PPLII3btK8TlkMtQolYLCAjg7Nmztmv9QUFBdOrU\nieXLl5Ofn49pmpw+ffqSl5UKCgrw8fGhXr16pKen8/nnn5dZHxgYWK6f4pyQkBCuu+46PvjgA4qL\nizl+/Djr1q3jpptuqjB7cnIy//vf/ygpKcHb2xtvb28MQ36kRfWQf1miVuvVqxcA999/P8888wwA\n06ZNo7S0lBkzZjBp0iReffVVMjIyLnqMu+66i6NHj3Lvvffyr3/9ixtuuKHM+hEjRvDxxx8zceJE\nPvvss3L7P/bYY5w9e5aHHnqIuXPnctddd9n1TEZJSQn/+c9/uP/++3nwwQfJzs5mzJgxl/P2hbCb\n3DorhBCiQtKyEEIIUSEpFkIIISokxUIIIUSFpFgIIYSokBQLIYQQFZJiIYQQokJSLIQQQlRIioUQ\nQogKSbEQQghRof8PHRbfW4d0ZYQAAAAASUVORK5CYII=\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -428,6 +410,150 @@ "Modify the code to compare the variance and performance before and after adding baseline. And explain wht the baseline won't introduce bias. Then, write a report about your findings and explainations. " ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 25.97\n", + "Iteration 2: Average Return = 24.45\n", + "Iteration 3: Average Return = 26.44\n", + "Iteration 4: Average Return = 32.13\n", + "Iteration 5: Average Return = 30.1\n", + "Iteration 6: Average Return = 34.71\n", + "Iteration 7: Average Return = 33.39\n", + "Iteration 8: Average Return = 34.75\n", + "Iteration 9: Average Return = 37.2\n", + "Iteration 10: Average Return = 36.75\n", + "Iteration 11: Average Return = 42.17\n", + "Iteration 12: Average Return = 43.41\n", + "Iteration 13: Average Return = 43.62\n", + "Iteration 14: Average Return = 44.97\n", + "Iteration 15: Average Return = 50.98\n", + "Iteration 16: Average Return = 55.79\n", + "Iteration 17: Average Return = 51.39\n", + "Iteration 18: Average Return = 51.21\n", + "Iteration 19: Average Return = 53.02\n", + "Iteration 20: Average Return = 56.96\n", + "Iteration 21: Average Return = 57.15\n", + "Iteration 22: Average Return = 57.16\n", + "Iteration 23: Average Return = 60.94\n", + "Iteration 24: Average Return = 60.21\n", + "Iteration 25: Average Return = 65.87\n", + "Iteration 26: Average Return = 70.16\n", + "Iteration 27: Average Return = 77.5\n", + "Iteration 28: Average Return = 78.58\n", + "Iteration 29: Average Return = 79.47\n", + "Iteration 30: Average Return = 83.65\n", + "Iteration 31: Average Return = 87.0\n", + "Iteration 32: Average Return = 85.45\n", + "Iteration 33: Average Return = 90.43\n", + "Iteration 34: Average Return = 96.83\n", + "Iteration 35: Average Return = 95.87\n", + "Iteration 36: Average Return = 107.88\n", + "Iteration 37: Average Return = 92.93\n", + "Iteration 38: Average Return = 95.23\n", + "Iteration 39: Average Return = 99.3\n", + "Iteration 40: Average Return = 98.43\n", + "Iteration 41: Average Return = 103.88\n", + "Iteration 42: Average Return = 112.32\n", + "Iteration 43: Average Return = 110.31\n", + "Iteration 44: Average Return = 108.24\n", + "Iteration 45: Average Return = 109.35\n", + "Iteration 46: Average Return = 119.37\n", + "Iteration 47: Average Return = 122.0\n", + "Iteration 48: Average Return = 115.01\n", + "Iteration 49: Average Return = 121.9\n", + "Iteration 50: Average Return = 131.0\n", + "Iteration 51: Average Return = 126.73\n", + "Iteration 52: Average Return = 127.67\n", + "Iteration 53: Average Return = 132.37\n", + "Iteration 54: Average Return = 134.35\n", + "Iteration 55: Average Return = 138.44\n", + "Iteration 56: Average Return = 139.14\n", + "Iteration 57: Average Return = 141.27\n", + "Iteration 58: Average Return = 152.96\n", + "Iteration 59: Average Return = 156.98\n", + "Iteration 60: Average Return = 158.73\n", + "Iteration 61: Average Return = 160.76\n", + "Iteration 62: Average Return = 166.46\n", + "Iteration 63: Average Return = 166.86\n", + "Iteration 64: Average Return = 172.63\n", + "Iteration 65: Average Return = 180.39\n", + "Iteration 66: Average Return = 179.31\n", + "Iteration 67: Average Return = 177.36\n", + "Iteration 68: Average Return = 181.79\n", + "Iteration 69: Average Return = 179.09\n", + "Iteration 70: Average Return = 184.68\n", + "Iteration 71: Average Return = 179.52\n", + "Iteration 72: Average Return = 187.13\n", + "Iteration 73: Average Return = 186.27\n", + "Iteration 74: Average Return = 186.0\n", + "Iteration 75: Average Return = 187.97\n", + "Iteration 76: Average Return = 185.1\n", + "Iteration 77: Average Return = 190.89\n", + "Iteration 78: Average Return = 190.22\n", + "Iteration 79: Average Return = 190.16\n", + "Iteration 80: Average Return = 194.29\n", + "Iteration 81: Average Return = 190.83\n", + "Iteration 82: Average Return = 194.59\n", + "Iteration 83: Average Return = 193.51\n", + "Iteration 84: Average Return = 195.89\n", + "Solve at 84 iterations, which equals 8400 episodes.\n" + ] + } + ], + "source": [ + "sess.run(tf.global_variables_initializer())\n", + "\n", + "n_iter = 200\n", + "n_episode = 100\n", + "path_length = 200\n", + "discount_rate = 0.99\n", + "baseline = None\n", + "\n", + "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate)\n", + "\n", + "# Train the policy optimizer\n", + "loss_list, avg_return_list = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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5UCzA/77fA069DfrjZ+1533yOSZjVNTBazJ4BiY2BhIaqwmu0YN7oGAmvyQ1q\nFTkddZ1OlaejcQ84cxqgFOhQzzWOY3R6Dd6NoI6QQDOJOG+DpyN6MySoaJ8hVjbT6cnqbXk3Ajs9\nHZ+PXTQ2rGxpqSivDBeLp6Nc+bYir1MsMsPc4ZwOfWUvEAwhdOungEuvBv3XfwbVU1CagU9E9Xrr\nngt0Ic8MTT1Px0J4re5iqqgKr3mrDSMtl6qNjngPoIpO01Rq9Nm5Gh3AMTo9B00Y8XSqXWtaLsnG\npilPRwyhKXs2DUWYMVAbnfmE/He7sHN6qEZOh/Z6TkcREqKtkE3ndFrgcPpab3RoqQT66j6QS64A\ncXvg+uCfsL5+336seQ84n2VeRyNPh8ulY2NAaAjIZ+W2ShbVaw1n6ojHtao4tKySTCvDa/zzlZ7O\n5AkWOhxdZnzfWoBjdHoNvoLV67sGRQEmP+F4WIwQ4/PYNaBSeE02OoQQYGQp6Ey10aE8DGinkMDO\n6aHFRaheKxXlBUcrPJ2cdgscCa+NXcD1eH0CyOdALvs1AMzjJzf9MfDGq6Av7bH8tpRSVXitzrkw\ny+TSJLZELgngQh0eXTDj6RjpNF0TXlO3wVEZHZ9GTufkW8DIUhYG7SAdn6eTyWSwbds2zMzMYGRk\nBHfffTeCwdqV1J49e/DMM88AAG688UZcd911AIBjx47hscceQ7FYxMaNG3HrrbeCEIJvf/vbeOWV\nV+DxeDA2NoZPfOITCAR0LpZeIjnHLm691SZQG17jobXISPU8HLNwsUBA1SV6dCkwdar6uZR4EdpY\np2NrJ+NSEegXlUqLSb3GV+otMTp8lk499VoRlFL7CoJV0Ff2su944XrpOfLu3wT9t++yXM9V11t7\n42KB/f4DgVovQr0PM6KnMzIGkppj83DSKXZ9LeQBn08u0jQAGQiKM3Uy+vUzkpCAe+c+1uZGqLAZ\nWuUS0K8YXc+vFWWI/fgxkHPXGN6vVtFxT2fnzp1Yt24dHnnkEaxbtw47d+6s2SaTyeDpp5/Ggw8+\niAcffBBPP/00Mhl2ATzxxBO444478Mgjj2BqagoTExMAgPXr1+Phhx/Gl7/8ZSxduhTPPmtTsrHT\niIWhdS9q9chqnugcWwYU8tbDSJk085ZUxpuMLgVmJqvVcvMJIBgCUcaZm0QKLdihYCsV5ZqHRZPT\nEfNUwxHQRCs9nTpGhwotO460XAad+G+QSy6vOq+IywUMhWsUlKbgEQB/APB45HCZFrNnmFAgEAJC\n4qKK53UYYhS2AAAgAElEQVQ0RlU3xMj00JKGkAAAeBcKZY4S4jFRzNSh2QxTrq08z9y+tYCOG519\n+/bh2mvZ9Mlrr70W+/btq9lmYmIC69evRzAYRDAYxPr16zExMYFEIoF8Po81a9aAEIJrrrlGev0l\nl1wCt3gzWbNmDeLxFrYGaSM0OVe3GwGAmpHVvEaHjIkJRKshtuw84A+wlZWS0WXsRqO40dFkwl65\nNKBon2+P0SGe7lSv0Zf/C6U364yM0HsdTyaHY1W/hW37pTdLh9PqmTqHXgVyGZDLrq79WzAEpOdr\nnzcKl5gb8nSmgNgStvATPXmpFY6ZsQYcbsSz+iIfaQy4sjgUkAuCy2XZY+co6/VOvgkAICvONbdv\nLaDjRieVSiEcZjencDiM+fnaEycejyMalXMYkUgE8Xi85vloNKppXHbv3o0NGza0YO87QKJBYSgg\nK1eKqvAaNzpWFWyKFjhKyOhS9g+lmGA+od+Q1Cr8grMrvNalno7wrceQfeob5l8oysBJONrinE4d\nTwdomdGhr+xlN9KLNtb8jQRCcvjXCuJ3I0Yk07NngJEx9u/Bak+HWvF0xGtKyplqUaNeU3n95RJr\nE6VE0QuPHj/GnusCT6ctOZ37778fyWTtvO5bbrnF8nsSQgypVZ555hm43W68+93v1t1m165d2LVr\nFwDgoYceQiwWs7RPHo/H8muNQCnFdCoB/9LlCNX5nAqhmAUQ9HoxEIshXS4g5/Fi8NxxpAAM+7zw\nWtjPRHEBdDiCiOq1lQvWYhZAIJfGgPi3mcw8fCvOwZDG51g9TsWRUSQADPr70dfkcZ4ul9EfGsRg\nLAZaCGEaQKC/D4EW/n5GEPJZzOSzKB97w/QxShCADgzAt+wsZP/rOUSHh/WbwlogAwFZALEVKzXf\nNx+NYR5AODAAj83HkVYqmN3/M/g2/RqGl7HFk/I8So+MIf/qy5avv8JxD5IAhpYsQ3ZgALRYqDnP\nAfEanDuDgXf9unQNTvf74S8XEIrFEK+UgWBI87W63y0YYOcfBN3zL+fzIg0gumQpXIPDyIcj7FgH\ng/DEYpiplOELhqqut9mBADyg8Hg86Js+hWI4hpHzLI4VsZG2GJ377rtP929DQ0NIJBIIh8NIJBIY\nHKwdZRyJRPDaa69Jj+PxOC666CJEo1HMzckrurm5OUQicuhpz549eOWVV/CFL3yhbg5ky5Yt2LJl\ni/R4dnbW8HdTEovFLL/WCDSXBYoF5H1+FOp8Di8AzczNIjc7C+HMFDA4hHSZjSBInj4FMmy+4V8l\nEQeGwjXfkQoE8HiROXYYudlZUEohxGdR6BvQPB5WjxPNMUnp/OwMSJPHmRYLWKgIKM7OSrmo7HwK\n+Rb+fob2S2w9X5mZwsybx0yN9q7ksoDbg7LPD1CK2aOHQaL2NXYUZqeBPj/mNBaQAEALbNWdODMF\n4jO52m8AfeswhFQCxYs2SueO8jwS3B7QXBYzU1OWDK0wxep8UqUyBAogl9M8R2kyDhSLyAcGpWuQ\nBgeRPzOJwuwsKmI0wMz5TSkFPF5kz0zqnn+CKIGfS2dBimUIogeTmD4D4umDUCygUK5UfW7F40Vl\nPoVyuYyFw68DZ53T0vvTsmXGpNgdD69t2rQJzz//PADg+eefx+WXX16zzYYNG7B//35kMhlkMhns\n378fGzZsQDgcht/vx6FDh0ApxQsvvIBNmzYBYHmg73//+/jsZz+LvmZmuHQTfChao7CVSi5J0ymW\n8JQSlhbDa9l0dWGoCHG5gJElcoFoPsti4i0KrzU7PZRSWpV4JS43E0h0g3pNGRY7ftTca0sspyON\nMbc7xJbL6udzAHvDn2p4UfSYTmEjV1RaDbHxnI5/QCwO1cnpKOXSnMFhuf/aQl4W8hiEEMK6fNRT\nlkrqNfGc5TkdLngolasl04A0PZQWC8Dkia7I5wBdIJneunUrtm3bht27dyMWi+Gee+4BABw9ehTP\nPfcc7rzzTgSDQdx000249957AQA333yzJKu+7bbb8Pjjj6NYLGLDhg3YuJHFe3fs2IFyuYz7778f\nALB69Wr8yZ/8SQe+oY2ItS91uxFAHAzl9sh1OvNJVsSmmMduSdCama+VS3NEBRvbT4PG0Sx25Qwq\nZdbZQBkDd3u6IqdDE/JKlL59BGRtbf5CF0m91hqjQ3N1+q4BLc3pUC4S0PP8+GIok7Z23knqtSCI\nx8dEGVr7oZBLS4SGmDIMAAoL5jpMc4KhBjkdVlcmdYfmohopp6NqgwMwo5NNo3z8GCAIIF2QzwG6\nwOiEQiF84QtfqHl+fHwc4+Pj0uPNmzdj8+bNmts9/PDDNc8/+uij9u5oF0BTJqr8lYPc0imQZSvl\nBLAF9Rotldj7aXg6ABMT0Ncn2BRR3gLHbqNjl3pNmhqqMjrdoF4TVWeu6AiEt817OsTrk4wOTcxZ\nW1zo0dDTsVFdqIZ7EqEhzT+T4CCrdclaVLDlskxQ4vMBXo9+cSjvRhAdlT97cBj0zUPswUK+ul7G\nKMHB+l6acoAbIHs1pSK75iqVGqND+vpBCwsoHRP3bUV3GJ2Oh9ccTCBV+Ru4mYuD3CilrE5ncIit\nfFyuutJMXfjFHNA2Ohhdxm42qYTCOLZIvdZsG5yyhtHxdIeng+QcEAjBe/464O0jjbdXwiXTwRD7\nv92y6VymQVEyMzq0FeG1zDzQ769VaHECCk/HCmI3AkJYflI3vDYzBQxHq/cjNAyk51lu0EJ4DRDV\nd/U8HeWoakA+d8sleV/Vx0acq1V+8zALG8bG0A04RqcLqDz0ZxB+XFsUW0MqwW68fgMrKT7IrZBn\nJ2xomF1QRtqoa5HRaPapQJZNn25deM1rU85Aaimi9HTcXZHToYk5IByFd/x8YG7aXMGjGF4jhLBa\nrhbkdEiHwmuYT+l6OQCMyY7rkcvK7WjqjNCgs1PVoTWAyaapwMaEU8F8nQ7AwoaNcjo+bU9H8sq8\nqsCVrw8oLqD85iHgrHM6OrhNSXfsxTsYWiqxyZunjzfeOJkAhhp0I+CIrnVNWMLoPHY1WY1mn0pE\no0OnJ1mNjscrX8Q2wUZLe5sP3/CLVBmO8Hi6o/eaOBXWI44Ch5kQm3Jk8XDU/rHVBj2dluR0Mg2M\nTpNCAppXGJ16ns7sNIjaY+C1OjynacXoBEJANsNCZVr7V1R5Oh4uqlF4Oh4NT2dhAaW3joCsHEe3\n4BidTsNvDI3G40JsomnUe+Adf8XCUMIvDH9Ariw3A1+FBXUSuZEYy4tMT7K+a0Ph1vTf8tkwyK3E\nO/YqLmK3pys8HSTnQMJReMUeWdSM0VEq8sJRW8NrtCKGjjrl6aTrGx3S18fOjSbDawCk0QbqOkBa\nKrLrValcA0DEaaN0hinbrITXEBxkXpJeFKImp6MoDuXXg5Z6jQosF9sl+RzAMTqdJy5q/csGbqSm\njI7YAoP3XeNjeI2MxtWAZmsHuCkhLjcwMgY6PWnOOJrF22eD0eHhCMVF2gXqNVouMc90OArX4BBL\nVhvM61A+Z4h7b+EokIzbN7Y636AbAdBayXQ6xQam1SMwWD8vUo+8QiTBj6Ha25mbZqrHkWqjo/Z0\nrKrXAOi38ikVVDkdhWS6rOG5A1XGr1uUa4BjdDoOjc+wfxgJGaUSIEbn0/iY0eF913hjQmI1vCZ5\nOjrhNQAYWco8nfmk/X3XOD5f9YwQK0grQ6Wn4wbttHqN58J4nc3Zq0CN1uqok8nDUfY9FV6t8G/f\nZa1krNBolg7EhYfHY7vRYWKYeX25NCcQst70M58F8auMjjr6IIaqa0oWuDGcFj0dC0aHBBuEB9Xh\nNaWnI/72RO3p8O09HmnYYjfgGJ1Ow41Og9U7LRRYCMCgB0FU4TXpgh0IWCsOzcwDvj599RDkbtNI\nxkHsHFOtxNcH2qx6TSsc4ak/LbItiDU6vLiTnD0OzEyxDsGNUH8nXqsjhtjom4dAdz4J4f/7G9Bf\n1jbVbQjvTVYvvAawG50dXcCV5LNMzh5s4OkEGyjA6n5GTvZ0JGWY6nvwxY56Hs1AkC1aeE7HbO81\nQI4g6O2/ESGBjqfjWXmerd3em8UxOp2GG506XW0BKOTSBj0dZXhtICCfdBbDa3rNPqsYW8Y+M5tu\nnadTR1lkmJKeeq3D4TU+7TPM+meRs1exx0a8HVWIhYTF80TMGQo/fJrdHFecB+Ef/gbUbBdrA54O\ngNaMrOYhp8H6Roc0qnXRgQqV6pwOb6NTUp0P/HupcjbE5WIGcca6p8MFOlQvJ1UsgHhVOUhC2O+u\nY3SIaPw8XTBDR4ljdDoMFXM6DS9UswWXYp0OeAsczkAQKJdMt5Kh2bS+ck2EjCyVH9g5MVSJr/np\nlJSHMj2qOp1OCwl4NwLupYiKI0MhtpJGeA1Mgk1PvQ1MvARyw/vh+n/uA4bCEB79K9Dp08b3rdHU\nUI6is7FtiHlJ0ipPh4+Y9qs9neqFIOXF1lqTNweH5FY6ViXTQANPR/5cQog8PVS3Todt7z3PMToO\nSqTwmkFPx2jYqk+c4piKV6t+jAyM0iKb1leucUZlo2M492QWcTplU2h6Op7G3marSc6x7yf+RiQ0\nyCZJGlGw8e/EV7s875CYA/3R00BfP8jm94MMhuH6f/8SoBTC3/2lYSUjNeHp2F4cqhbD6BEIAdms\nruxYF5VBJcrQlRI+KkRLnaZc2FlRr/X52Tmo11FBXRwKsEVTSd/TwdIVwPKz4dtwhfn9aSGO0ekg\nlFJgzmBOx0wLHEA+8WfPVF+sfotNPzNpVjVdj+ioPJumF8JrNW1w2uPp0FwWwjf+Tu4nxhFrdKqk\n5mePgxpRsJV4Mpl9J+LxAqEh0COvgf7sP0Gu/W0pWU3GlsF1x5+xfNHEz4zttBlPx24hATc6DT2d\nBrJjPUQPhUjhNW1Pp2amjQKiHMtuRUjQqOmnOqcDyNdCWUONCRYVcf/lo/AsW2F6f1qJY3Q6ST7L\nOgYAjW+kqTi7oTe68XN4p+lEvOqCIAFxpWra05lvmNMhbrfck6pFkmniteGmpimZdrdPSHDkNdCf\n7gadeKnqaZqck5VrIuTsVcD0pDSuQhetG89wBHh9P+B2g/zGB6q3X3MxW4Acfd3YPucy7Bg1WsW3\nJKfDxTAGwmuA+Vod5ahqQK7sV0cfeNhQq2s9v8Y8HutJe52mn7RcBgSh1th5vUBJESrvIrFAPRyj\n00l4Pmco0ji8lmSTOA23suAXBhWqL1a/+fAaFQQgm20cXgPkEFujUIhV+uwIr/E28coK7/Y1/JQ6\nBbylSuYn5uSxBCJSJfnJt+q/qdaNR8zrkF/fUiPzJS4XMH4+6NGDxnY6yyr2Gxb8epvPudWQTrG+\naGpJsAoSaJAX0UPtxenV6RQLbHSEelw7IJ/vVpRrnKBOnZE01sCcp9OtOEank/B8zpLlDZtY0lTC\neGgNrMOshDLeLHo6proS5LPMeBnwssg5q4HRZa2TaHp9zTf8LJUAl4t5ZiKknV2mRZWa1JkYomFP\nxmURAYevoHMNVu9qIQEAEh1l3/M3f1fzJWT8QuD0cWPnQj7bOJ8DsctDK9RrRhY8Fj0dqpylA+gK\nCVBY0BYRAPI1ZkVEwAmEtPddykHWejq0quFnbxidjo82eCfDC0PJ2DLQN14FFQR9TyYVr62ErodP\nUY082KSQgOceGkmmAZD3/QHIb2nf5GzB67NntIFXdQG3sw0O93ROvQ1aKLCaqsw8M3oqT4ffxGg+\nX39MgcaNh/z2TSCXXgWic96Q8QtYXvHoG8C6y+ruMs02mKXDaUGdDk0nZeNbD9Ew0ey8uZEO/Frw\ni0bVU0dIoBNeJINDbLSCFREBf4/goHbDUimXpOPp6AkJuhTDns6BAwcwPc0GFSUSCWzfvh2PP/44\nkjqjax0MEJ9hcXKeB6kXYkslzM2nUcadq8JrPKdjwtMRax+I3gA3BcTjAbEyT8QoPh+TfAtNGIhy\nsXZV2MbRBlJ4TRCAE6IyTSziJGpPh6+cuaxXj1KtDJxERkAuvET/NeedD7hcoEbyOgY9Hannn50Y\n9XSk8QYmw2tSTkfRew1iM00l6q4ASgZt8HSCISCbrm1dJC6ySI16TSWZXmxGZ8eOHXCJq/Bvfetb\nqFQqIITgH/7hH1q2c4ue+CwLp/DVkU7/NVousQvJjAxZJ7xGvF524zbj6RhpgdMu+IVnoEGqLqVi\n7aqxreG1OYA39ORFmtwQiYWhEgaNDtUSRzSA9PUDK84DPWLA6GQzsgilHi0SEjTsuwYwo+F2WxAS\n5ACfj03cBXRzOrSwoC0iAOwJrwUH2UJErb4rauQgAdHTqSOZ7lIMG514PI5YLIZKpYL9+/fjjjvu\nwO23345Dhw41frGDJjQ+A0RHqltaaMHHE5jxdBThtZqkvj9oSlZKGw1wayd2TA8tFmvbwLdRSIBk\nnLW4iYwAYl5HGlMdVi0sfH0AcRn3dOq0KdKCjF8AvHmIKaTqoWz9Xw/R6NjVaJRSyhZcBowOmxcV\nNN+VIJ+VIwCAfu+1YqH6ulLCizubCK/pDqIr6YXXuKdTBNyerpmX0wjDe+n3+5FMJvHaa6/hrLPO\nQn8/O7jlTver6mXisyCREfkGqHcjTbLEs6mCS74ic7trwyIDJscbdJWn0/z0UFoqaXg67WmDQ4sF\ndlMcjgLnrpbFBIk4m+qqyl0QQoD+fllar4fVEMuqC9nN9NRb+vtMKQvHBgwaHUrtK7TlfdeMeDoA\nEBzUbyWjRy5bPRjRgpCAeLxAIGStwzR/D72uBDr1QUTydMo94+UAJoQE73nPe3DvvfeiXC7jox/9\nKADg4MGDWL58eav2bVFDhQprexKOgfh8LAmpFzLihaFmmmhy6WZoqFbmarb/WjbNbog2D2WzhB0z\nW0qFWo/A3aaGn0neWSIK4vGAvrKXFT8mZtmAPi05bp8fWMjVf18L4TVAFBMAoEdel3u9qSkWmMjC\nbyC8ppzuatLr0mTeYI0Ox0IrHKps9gnU93TUHaYVkFtuBxlr4n7IPR21p1bUV6+hLOZ0ekS5Bpgw\nOlu3bsW73vUuuFwuLFnC1DCRSAR33nlny3ZuUZNKsvhtZES/q60IlZp9WhASaF2sA0E5ZNcASino\noQNAdLQ1Q9lMQryigW4mvFYqaRgdNyAI9RWEdiDmbkg4AsTG2Hd567BmYahEvx+0VeG1yAgbwHfk\ndeCG39HeiHe5NurpAKyQ0o5wbEbsu2bU6AQG5QmeRlHO0gHk4lCNOh1SJ3zmuvI6c5+rhqvv0tXq\nO1q3TqfEfvse8nRMXV3Lli2TDM6BAweQTCaxcuXKluzYoofLpaMKo6N3I00lWEfZkPEmmsTjZTdS\njdcQv4nxBq9PAEder61o7xRSeK0Zo6OjXgNaLpumfJrncBQ4exwgLhZiE1vgaNLvN5DTKQGkuvbI\nKGT8wvpFovxcMeLp2D09VD2aowEkqFPrUg/lLB2Ic4Hc7tpzrF6djh0E9TwdnfY7HkVxaA95OoaN\nzhe/+EUcPMhOzJ07d+KrX/0qvvrVr+KZZ55p2c4tZqThbUpPR+9GmkqwMJnZG4qvv7pGhxMwFl6j\nlEL4wT+xEOCv/6a5z24VXjvCa0UNT4cbnRaH2JThtX4/sGwFMzrJ2m4EEkaMTjM3nvELgcQsKO8D\nqIbP0jHg6UiyXptqdWiGGx2DCy6xql8pZBD+4xlUvvin+q/JZWtrkDxejZxOHSGBHfgDLIytDg9q\nNagFFG1wSovT0zlx4gTWrGEyz5/85Cf44he/iAceeADPPfdcy3ZuUcONTjjWUL1Gk9bGP5Pr3wdy\n+btr/+Bn00MbKox+9XPg6EGQ9/1+wxYkbcNnj3qtZhidp11GZ44pnMTENTl3DXD4dWZUmjE6WobU\nIGTVhQAAeuQ17Q14NwQjdTp2ezpSYbIxTwfBEPsNFcIL+st9rPMC7+GmRjlLhyPe0KX3oLRmvIDd\nEEK0uxLoeTpeH/uuWp57F2PY6PAb1NQUG1R01llnIRaLIZu1MBDMgdXo+AfYJEZvg9WhyRY4HNfv\nfhhk/eW1fxgIsLY2dRRRlFII3//fLJfzazeY/uyWYYN6jRWHauR0gLpGh/7iJQjf+Zr1zwVqO0mf\nu1r+HXTCa6TVns5Z5zBDqBNio/waN5KjEVWtlgYFapFOsZ5vRr+bSnZMKxXgLbFL9+SJms0pz4mo\nRTJqT6dcYteMXp2OXWh1JSjq5Ov4Mcln7RFttAnDRuf888/HN77xDXz729/G5ZezG9nU1BRCoS6Q\n0fYgND7DQmuAfgU0x2w3gkbwUEK9BcMvXwbeOsy8nG5y3cWLizYrJNAqDgXqKtjo/p+B/nS39c+F\n2I1AoYAi58gDtuqG1xpJppsIsRC3Gzh3jX5ngqwJT2fZ2WKe6g1L+1JDOmU4nwNAGt8g7fPp45Kn\nQCdP1r6A16upw2teX7XR0ZkaajtiV4IqxLEGNUIebmhy2cUZXvvkJz+JgYEBnH322fj93/99AMDp\n06fx3ve+t2U7t6ipMjr6q3cqVJjSzMahaITfPPLaYgKWy/kOMLIE5KrNtn2uLdgRvtEqDnU3FhLQ\nfK75wsdkvNq4LFspG0A9IUFfa8NrAEBWnAtMadyUASYkIKQ2BKX1PgMBYMW5oId+ZXlflNB0yrhc\nGpCT8WJYTjJ+hGh6OlA3++R4vNXTdetNDbUTrU7TpUJtNwJA4enk5PBwD2B4T0OhED70oQ9VPXfp\npZfavkPvGOKzLJ4PKEJGGp5Oep659XXqA0zTqOnnW4eB48dA/uhP5dYg3YId6rWyRhscT2NPh3Xb\nptpTHA2g1UmaeDxsLPWR1/VzOv4BoFwGLZd0vU7arIIpOMgmzRYLtT2+shkW4jIoJSdrLgZ9/keg\npVLzucB0CoiNGd8+wJt+ppns+M3DLOQWHdH2dERlHqkJr6l68RV08io2Q4KDoMdUXV70ckkehafT\nQ+E1w3eUcrmMZ555Bi+88AISiQTC4TCuueYa3HjjjfA0cWPKZDLYtm0bZmZmMDIygrvvvhvBYK0b\nv2fPHkkpd+ONN+K6664DABw7dgyPPfYYisUiNm7ciFtvvbXKDf3BD36AJ598El//+tcxOGjcTW8l\ntFBgqxneZ8tT50aa4t0I7AyvNWj6KRajkhXn2veZdtGkeo1WKsybUU9ZdHtYzUw9IQFfFVtNKPNO\n0iqPhlx8GWguWytu4Cj7rwV1buLN1mooxwJEVN8tl5FGYhiBrFkLuuv7wNuHgVUXWd8nAEjPy4sz\nI6jGG9A3DwHnrgYJhEAP13pflHs/ilHrAGon1IrnG2l5Tickqe+k+5heo1F+vhTy3RUCb4Dh8NqT\nTz6JV199Fbfffjv+9m//FrfffjsOHDiAJ598sqkd2LlzJ9atW4dHHnkE69atw86dO2u2yWQyePrp\np/Hggw/iwQcfxNNPP41Mht0wn3jiCdxxxx145JFHMDU1hYmJCel1s7OzePXVVxGLxWres6MkROVa\ntDqno6nIksZU25/T0ZtGKc8X6YIOBGo8HhYqsZrTkYoo1TUPBtRrSqNjBWVhqALy3t+D6y8f1X+d\nkaafZY2CVxNIHcQ1+pbRbNpYPocjGhr6xgHL+wPwvmsmw2sDQXZ+ZOdBF3LA6ePMaC1dAcRna4ts\n3zrMjq+6k4BaSCCF11qc0wkM1qrvdLo7VHmRi1G99tJLL+HP/uzPcMkll2DZsmW45JJL8D//5//E\nT3/606Z2YN++fbj22msBANdeey327dtXs83ExATWr1+PYDCIYDCI9evXY2JiAolEAvl8HmvWrAEh\nBNdcc03V6//xH/8Rf/iHf9gVlfRV8MJQMadDXC7Rna+9kVKprqON4TV1q/cughAizmyxeOOXjI7q\nIuXqtXrhNd6KpmDxsxNyjY4SQkjdc5QYMTqlZsNrOoWJADtPTHQXIKFBYPnZmp6FKXJZ5pWaERK4\n3WyxlJlnqjVKQc49H2TpWWwDVd6KvnUEOHtVbehQJZlun5BA/K5pRV6nVNRWzSkN0WL0dOzqGqsm\nlUohHGar+HA4jPn52r5J8Xgc0ah8oUYiEcTj8Zrno9Eo4nF2Yb/88suIRCI455xzWrLfzUD5mGou\nJAD0h5NxT2fQRk+HGxO98Fo3ezpAbejDDHrtYowUh3JjbDW0l1R0IzBDnxGjoyGOMAMPn2kZnWza\n2FgDBWT1WuDIQRbOtEpabNUUNDn6XOxKIDVTPXc183RQrWCj5RJw4hjIORo95zxqo9MeIQHRMv56\nfeyUi4weMjqGkzFXXXUV/vqv/xo333wzYrEYZmdn8b3vfQ9XXnllw9fef//9msPebrnlFnN7q4AQ\nomsIC4UCnnnmGfzFX/yFoffatWsXdu3aBQB46KGHLIfjPB6PoddmFrLIEoLYqvOlRP1MXz/63G4M\nql4/XypgIRDCyNKlWm9lmemBAPxUQEhjf9OgyPl8tn8mx+hx0mOm3w+fi2DIwnuUCznMAQhFovAr\nXl+MRpEAMBgIoE/jfWm5jGlxUTDk74fPwmdnCnn2u5+3qqFAQ3mMikuWsn3r82ruGwDMCgI8wQCG\nLR7XCqGYBRAAxYDqPabzOfRHYzXnZj0WNl2F1J4fYnh+Dt7V1vI6xelTSAAYOmul5vfWO4/iwxGQ\n4gLIqbdRXnoWYuecx34/txv+1Jx0zpeOHkS8XMbgusvQr3qfZCCI8uwZ6f3zPi/mAYSXLIWnheH6\n4vIV7Ld2Eek7zwkCXMEQwqrPLcVHIfrO8A8OaV7LQPPXm90YNjof/vCH8b3vfQ87duxAIpFAJBLB\n1VdfjZtvvrnha++77z7dvw0NDUnChEQioZnsj0QieO01uVo6Ho/joosuQjQaxdzcnPT83NwcIpEI\nzpw5g+npaXzmM5+Rnv/sZz+LL33pSxgerm2nsWXLFmzZskV6PDs72/A7acGNcSOEydNAcBBzCkMs\nuD1YSM+jqHq9kJgD7fdb3ic9aP8A8nOzKGi8rxCfBfoHbP9MjtHjpIfg8aAwP2/pPejMGQBAZqGA\nrKmJRAUAACAASURBVOL1NMO8mPl4HETjfaki3JGaPgMyav6zhdMngMHhqt9dD+UxogvMs5qfPqO5\nbwBQKSxAqFDLx5XXiGWmTiOnPC6CAJpJY8HlqTk3677fEtaTMfGzF+EKjxp6jfCfPwZ9cRdcn34A\nxOsFPXkcADAvQPN7651HlT4/K8KdPwpy/jp5m5GlyB09JJ3zwi9+BgBIR8eQUV93AgVdyEuvFcT7\nTCKb1f0N7ICWBQBA6vRJuFayz6nksiDh2u+qzMnmS2XNaxlo/nozyrJlywxtV9foHDhQnQhcu3Yt\n1q5dW6WsOHjwIC6++GKLuwls2rQJzz//PLZu3Yrnn39eKjxVsmHDBvzTP/2TJB7Yv38/PvShDyEY\nDMLv9+PQoUNYvXo1XnjhBbznPe/BypUr8fWvf116/Sc/+Ul86Utf6hr1Ggr52gmDOiEjWmhR642B\noP5MnXyue0NrAOD1VddQmEFqE68XXtMp0FUOvWsmvGY2tAZI5wrN56Cb+Wm2TsfrY+eZ+pxYyDPJ\nvtnw2lAYGFvO8jq/9bvGXnToV8DRg6B7fgjyGx+Q29aYERKAhajoG6+y3+nc8+U/LD2rulbnrcMs\nhxLVMIpe1aiLdgkJVDN1qCCw36SeZBqQO2P3AHX39O///u81n+cGhxuf7du3W96BrVu3Ytu2bdi9\nezdisRjuueceAMDRo0fx3HPP4c4770QwGMRNN92Ee++9FwBw8803S7Lq2267DY8//jiKxSI2bNiA\njRs3Wt6XdsHG3qpOXq9X+0aqZaDsYCCgOz2U5jJdKSKQaGIkMv3VL9g/1DmyRl2m8/I8G1os6N/8\n65GMm6s54bRDSABIuZAqzHQjUEHWrAV95UVQoaI9J0gFFcdt0P/zFOjVN7AaHcCUkAAAU4BxifO5\nq+X9WbqCdZUQ653oW0eAc1ZrizjU6jW9/md24w+wSbHicac//b+sOHytxn1NuchYLHU6jz32WMt3\nIBQK4Qtf+ELN8+Pj4xgfH5ceb968GZs311bHj4+P4+GHH677Ge34HqbQNDo+7eLQQqE1ipmBADB7\nRvtvWg0Quwmvz5LRoSffBP2374Jc/m42LlqJqF6j5bK2QbHB00FiDsRKfsOoZLrZZPJAiMmjlUgd\npi20u1qzFvjPHwOnjgNGar7mk8DoMmBmEvRH/8KuB3/AfA0KT8Z7PMCK8+Tnl57FZlhNT4JGR4HT\nJ0A2XqX9HnwUNKe4wEZCt7hYmrhczKvMMMk3ffZbwHnng7zrGu195PSQkKA3hmovNgoLsiKJ4/Vp\ny4AL+ZYYHTIQrK9e6+bwmq/PtHqNlssQvvlVYCAA8sE7ajdopF5TeDqSksnM5xcL7HhbCK8Rj5fd\nQHWMDuVdEppd7WpN3WzG01nNwu70kMF6nXQS5PyLQa68HvQn/wf07SOmQ2sAZNnxivOqalmIqGDD\n5Eng7aMAFUDOWV37ekCjTqfQ+mafHK6+++G/AKkEXLfcru2NLXbJtIONFBZqT+A6nk69aYWWGQjK\nEyHV5LMgXezpED15eR3oj54Gjh+D68OfkGfRK+EXrU6dDlV6OlbqdKzKpTn1mn7yfW4yvEYCGs0m\n+cLEZE4HEAcURkdBD7wC+vp+CP/5YwjPPgl6/GjNtlQQxOaewyBb/5AVeB49aD60Bll2XNPJYAmr\n1aGTJ0DfOsyeO1dnRLfXyybJ8nBri8caVBEcBD1+DPS574Nceb1+R4YeLQ7tnezTYmIhD1Lj6Xi1\nV+9aoTg7CASBwoJ2P6+u93TM1enQE2+C/ttTIO+6BuRSnXCKNNqgcU7HUjcEsTBU3Y3AMPWafvJj\n0exqNxiqWYhQ/tiCpwOIfdh+uhvCgZ/LT86dAbnt09UbZtMs9DU4DBIZAdnyO6A/+p5FT0d8jepm\nTfr6WW3c5EkmjojEQPTq36RFSImdG4WF1osIOIEQ68Xn6wO58Y/0t3OL3Tko7SlPxzE6naBYkOeO\niBBvn76QoCVGR4x75zJVSXVaqbALrIs9HTNCAppNQ9jxFSAQAvngn+hv6DGoXvP6rIXXbPB0alq4\ncHgYqNnwWmAQyKar+35Jno61ESZk6x8Ca9aCREeBkSUQvv6w3GVDiSgiwCAraSDvuRn0xZ+AqHui\nGWHVhSB/8HGQy66u/dvSs0CnTrBFhF5oDZCPZbkE9PWz8GibwmskOAgKgPz2zfrjLiAKurxe7aGE\nXYxjdDpBIV+7alK33YAYq2/ViFypAr3a6EitXga62Oh4+wx5GzSbhvCVLwBnTsH1qfvkWStaNBpt\nkM8xD8sfsCYksCO8puvpcKPT5Go3EGTeRj4rezbZDDPIaom5QUhkBOTXf0N+PBwFPflW7Yai0SHc\n6AwE4Pqrxyyd+8TtBtnyAe2/LV3B5NTlMsi764xg554DXwi2M7w2fgFw6m2Q39zaeFuPGGp2PB0H\nPahQYSeJ2nvxaQgJSkUWBuhvgZAgEGJdlXXUSt0fXitUr8hV0GyGGZzTb8P1ic+DXNRASt9oiBsP\nOVqVayeqx1Sbpt+vn4OzM7wGMNm0shN5IGRf/8JwFDjwSs1vRyVPRw6nWVLMNWLpWdJvrCsiABQj\n5EWDXlhoTemCBq53/yZQzyAq4R5Ot40gqYMjJGgRdOqUdhdnnoRWGxItIYE0w6MFno6qBbyEmLuo\nmS/STXh9bEWuozSj2QyEbdzgfA5k3WWN37PRuOpclhkMXx+rszIJLwy1fPOu5+mI4bVmQyxyp2nZ\nuNFsxnI+R5PhCLuBq7+LKrzWKsiSFfIDtWxeiTKnA7TX0zEDN449FF5zjI7N0NkzEP7hbyDc9z9A\n/+27tRvwG5ZaSCC6yVX95LhaqQWeDr+RUPXqWW+SYjchTQ/VDrHRH34XOPkmXP/jXpB1mwy9JSGE\neTu66rUmPZ1kvKlO4aQd4TWp2aRCNp1NW1Ku6cLDi8m56ufnk+z422ngtOCy6bHl8gRdDSRxjcLT\nIe0SEpiBGxtHvfbOgy7kkH7yaxC+/0+Ai7AQkFbCVDI6qlWTz8dCaZWK7CoX+OCoFgoJ1OE1vZnx\n3UTV9NDa/aSn3gaWnwOyvralUl3c7jo5HdHTqVQsGx1LhaGc/gF9ybRe52yziMaFZtJygWwuIw8b\ntAEyHGFh3WRcNgAAMzqhoZaPISGhQeZxjl9Qf0OlkABgv3m76nTMIIXXesfoOJ6OTdBv/z1y3/sW\nyKZfg+v+r7G+U1o3CfE5Tck0UD1Th7++FUbHPwC4XLUS2Vz3ztKRaDQ9dGYKZGSJ+ff1eOoXh4rh\nNbN1Olpjqk3T5wcWFrQ7q5dtyuloDXLLZkyPNaiLeAzUCjY6n2x5aI3j+swDIL//sfob8YVfqVfC\na71jdBxPxybI79yC8E0fQSoiNg/0D1TXdnAWuKejkdMBWMioX7zhF3S2tWN/CWGhjJx2Tqe7hQT6\n4TVaqQBz08Blv2b+fd31jQ7LcxEmnzVDNi2OqW5iEF+/n3nCRY22SCV7ikMRCLC6D2WeL9eCnA6g\nHV5rk9Ehowa6ISs8HaYibVG9XLM4ns47F7JkObxrFOETvWK+YgOjoxQTtNDoAGAhthohQfd7OkQK\nr2nc/OMzLARmxdOpk9ORwmt9/ebDa+KqnjTj6fjr9F8r2xNeIy5x6iZvNlkus8+zUUVG+vrZZ6hD\nz/NJSS7dFXgVkulKmQlXutLTcYyOgwjxD8g1L0r0DIlXmadg0FYbnaBGg8d8DvD6zDdZbCdKr1DN\nzCQAWCsqdLs1PR1aLjNDIwkJTKrX7Bg5XqfpJ7VLMg2IXQnEc0LK79mc3B+OyMWyEOvR0qm2eTqG\nUKrXeDi1K3M6jnrNgdOvHV6jOuE1omF0Wj7DQ6v/Gl/RdzPc09HwOOj0FPvHiAWj4/FqezoLCkWf\nBfWa3I2gSfUaoO3p2KVeA4BACJR7v9z42JnTAdhxUHo6uQwz9t1kdMRjScul9s3SsQDxOJ6OA0ev\nQSNfJWsNcQO0jU4rJNPQafDY7X3XADnModU2aGaSXYBWbvBut9zgUYkyz9XHuiFQQTD+vinxBqvX\n58sIffXCaza1wQFYKI2fE+KCxFYhAZiCrSqn06YaHVMoJdPtmqVjBb7QcIpDHeD3s5uTeuWsKyRQ\ntd0AWr/CCtSON6C94OmI6jWthD6dngJGlrC5JGbRU6+JYSbCPR1AuyO4Hok4EBysarNvGi4u0fR0\neE6n+dUuUYbXcs01+9RlOAqkErLhVrXA6QqUkmlxodiS0oVm8foAj7flUnM7cYxOq5AUaKqbRCHP\nFELqVamep+Np4eCoQAjI56oNY7cPcANUdToqZiYBK/kcQBQSaBiTvCq8BpjK69BUc4WhAOSR1Vp5\nQm4A7QixKDwdqXC4FeG1SgXIsMmgtKs9nWJ353SWnAUsW9F4uy7CMTqtQi8GL0ova1Ymmuq1fG3n\nAjsJKjpNc3opvKbydCil1mt0AFEyrRVeUxTM8tWumbxOszU6QP3poeUSW8i4bVicBBULkWxzHab1\nkFR8PK/T1Uanu8NrrhveD/d9f9fp3TCFY3RahDQETS0m0JoaCkir96qQUaunFSo7CXPyWZBu7kYA\naHuFAJBKsBuEZaPj1hQS0JyWp2PO6BCbPB3d8JrXphCLNPIiLS9G7F6E8GORUBgdl8t249YUPLrQ\n5UKCXsQxOq1CLwavV2SmbrsBsI4GLfR0iFYrnF4Kr6kl0zNMuWZJLg3UyenIQgLCjY7Bpp+0UmE3\n1WaNDj9n9NRrdqmXpHMiw86Lfr/94V3elSAligl4CxwrebgWQQhhx7RUkheCXejp9CLd8ysvNqSV\nabWnQ7VGVQM6QoIWt95Q3mAg1qMUFro+vEZcbhZKUofXxBodS3JpQL8jgbJg1qynM59knQSGmjM6\nxOVihkfX07GnToMou4/b3Y2AMzjMwoGip0Pnk0Coi0JrHK+vSkjQlR0JehDH6LQK0dOhedVNYkHH\ne/FqtHYp5Fs7w4M3eOSezkIPdJjm+Ppqw2vTkyxMEx2x9p4evZyOomDWrNGRuhE06ekA+jL8sp2e\nDu+/Ns+EBHaLCADmOQ0Oy1LyNrbAMYXHw7zIbhYS9CCO0WkVOp6O1qhqAPqS6VaurtThtV7ou8YJ\nDYFOT1Y/NzMFREYsd1Mgem1wlDJyfuMx2vQz1XxhqIRea6VSyb6Gj6KnQ1vp6QDAUAQ0IYfXukou\nzfF6RU/HCa/ZiWN0WoVer6wFjVHVAAvtEFITXmtpbYB/ACCumrYnpAc8HbLuMuD1/aCK40tnpqyL\nCIA64TWFos+nXyOkhdRNuVn1GgD0+6u+r/QZpSKbx2QH0hjzNAu7tiq5H44CyXh3tsDheHyiZHoB\ncLu7uzVUD+EYnVbBPR0N9ZqWISG8dqdGMt06o0NcLtZZmKvXemGAmwjZcAVbhb72C/nJ6UnrIgKA\nhVO01GtKT8dnUjKdjDPDrhjDbJl+v3Y/v7KNnk6fnxnfLPN07O5GwCFDYleChTy7sXej0fF6WRuc\nbh1r0KM4RqdFEJdbnL2ioV7Ta2vj9dUKCVqdvBzQavDYA+G11WuBgSDoxH8DAGhOVFtZFREAug0/\nqxR9JtVrSMaBoWF2PjSL3vTQUsk+IQEhLMSWSbPj2arwWjgCZOaB+Cx73I1Gh/fiKxYcubSNOEan\nlWjN1NGr0wGqjA6b4dFaTwcAEAhKledV9ShdDnG7QdZfDvrLl5ksmculmwmvebyGw2tGPR2anGta\nucbRHVldKtrb8DEQAk3MshtuizwdfkzoiWMAuqwFDsfrlcNrjojANhyj00r6B6puErRcZqEQvROY\nn+QAu+DbMcND2eCxl4QEEENs2TRw5DVZVNBMeE1vXHU+K+W5iMejKdfWJWlDCxxOXU/HTqMTZEpA\n/u8WQMJijuv4Ufb/bjQ6HiYkoE54zVY63po0k8lg27ZtmJmZwcjICO6++24Eg7Un+p49e/DMM88A\nAG688UZcd911AIBjx47hscceQ7FYxMaNG3HrrbdKldk/+tGP8O///u9wu9249NJL8eEPf7ht3wtA\nbeJX0vvrezqU53R4WK6VkmmwDsJ06iR70AMD3KpYuxHweEF/8RIQEnMmzQoJNNVrqtZAZsYbpOIg\nqy60vk9K6kimiZ3zVAKDwNGDAADSqvCaaIjpcebpdKXR8frYb9+tU0N7lI4bnZ07d2LdunXYunUr\ndu7ciZ07d9YYh0wmg6effhoPPfQQAODP//zPsWnTJgSDQTzxxBO44447sHr1anzpS1/CxMQENm7c\niAMHDuDll1/Gl7/8ZXi9XqRSqfZ/uX5/dXhN0vsbyOlIrTdavMIKDlYLCXxdPsBNAen3AxdeAjrx\n3yAXrAOGws2p/UT1GqVUWrjQSkUsmFUYYoNGh5ZKLDdih3INYIuVYhG0UgFxK3JENofXSDAkd4Bu\nlXqNH5MTx5hqMzjYms9pBo9Hlkw7no5tdDy8tm/fPlx77bUAgGuvvRb79u2r2WZiYgLr169HMBhE\nMBjE+vXrMTExgUQigXw+jzVr1oAQgmuuuUZ6/Y9//GN84AMfgFcMOwwN2aAeMou/OrwmrVLNGJ0W\nezoYCAL5LLu55rM9E1rjkI1XAnPToL98uTkRASD321KG2LhabEBpdHzG6nRSNkwMVcLPBbW3Y6d6\nDag2NK3ydAIhZihzWTb2wW2D0MJmCFeTtrozyDuMjns6qVQK4TAbbhUOhzE/P1+zTTweRzQqrxYj\nkQji8XjN89FoFPE4u9AnJydx8OBB/PM//zO8Xi8+8pGPYNWqVS3+NtWQfn91K/pCg7kcPp9sbBpt\naxdSg8cs+69XQmsi5JLLQQkB0imQiy9r7s34ja9SkQ1QjoccFca4r99YnY44qMyWbgRAddNPpTGw\nsQ0OALn7ONC6nA4hzBjPnunO0Bog5nSKAGh3ztLpUdpidO6//34kk8ma52+55RbL70kIYQovHQRB\nQCaTwQMPPICjR49i27Zt2L59u2Yn3l27dmHXrl0AgIceegixWMzSPnk8nqrXzg9HsFBYkJ4rTp1A\nAsDQ6Bh8Gp+RCAQhLOQQjcVQnHy77rZ2kV+yFPMAwj4P0pUShNAQoi38PKD2ODVFLIb4+RejdPBV\nDJwzjmAT75sdGkYGQHR4EC7RGJfSccQBDI4uQb/43vGBAAitINzgsxbeKCMFYPiccXhN7pfWMVoY\nHUMKQNjfD4/ib2fKZfgHhxCy6ZjmxpaCt4CNrjgbrhYZnnhsDKXZM/BFRxoeSy1sPY80mA+GUBC9\nXt/gEIZafF20ilYfJ7O0xejcd999un8bGhpCIpFAOBxGIpHA4GBtbDcSieC1116THsfjcVx00UWI\nRqOYm5PH3s7NzSESiUivueKKK0AIwapVq+ByuZBOpzXff8uWLdiyZYv0eHZ21tL3jMViVa8VQEDz\nWek5OnMGAJAqFEE0PkMQKGg+j9nZWdBpcduFgua2dkHF0H3i5HEIqSTgH7D8/Y2iPk7NIqy9FDj4\nKnKBQSw08b6CONV1bnoaJMQ8GXr6NAAgXa4gI753xeUGspmG30E48RYAIEldpn9DrWNES0zkkJg8\nDeIX29VQCpSKyJdKKNh0TCkVF2YuF+ZyeZC88YF1ZhDEPE6pP2DpfLD7PFIjVCqiR0tQoNbvC52m\n1ceJs2zZMkPbdTyns2nTJjz//PMAgOeffx6XX355zTYbNmzA/v37kclkkMlksH//fmzYsAHhcBh+\nvx+HDh0CpRQvvPACNm3aBAC4/PLLceDAAQDA6dOnUS6XEQq1eV5Hvx8olyVFGtUbVc3x+aQENS20\nqbNtUNF/rRfGGmhArrgOuGgDyPkXN/dGfAha1SRVDUVfX78x9VoyzsJ0QZvOuz6N1kqVMkBpa8Jr\nA4HWjkHmYcduDq+VxC7TPhuP7zucjud0tm7dim3btmH37t2IxWK45557AABHjx7Fc889hzvvvBPB\nYBA33XQT7r33XgDAzTffLMmqb7vtNjz++OMoFovYsGEDNm7cCADYvHkzHn/8cXz605+Gx+PBJz/5\nyfbPEfcrZup4vcaEBHyeTruMjtRpOgPkcyA9JiQAWM2H++6/av6NuNFRFIhSjdol4usDNSokGIrY\nd95pDXLj54utQgIxGjDQ4kUaV7B1q9FRXo9ORwLb6LjRCf3/7d19cFT1uQfw79nd7OZl87IvQErE\nS1lQUJNgBwUcNRDSOi3tFBW4WqWEgo28WtM707RzZe4MRWkhjU2LN5Qi2iLtVdvJVacOlVLCtZkO\nEY1TkCBSYMykmJfNJizZZJPsuX+cPSebZDe7hvOyId/PP3GXk+wvJ8d99vec5/f8MjOxffv2Uc97\nPB54PB7lcXFxMYqLi6MeV1lZOep5i8WCbdu2qTvYzyuy03RmVvxAMqx6LU6AUktkp+nAxCskUJUl\nwZlOxIx0LKKaC0MB5XoSewNQwpi8rkvNMnd5pqNVNwKZMtMxoLI0EZGb17GQQDWGp9duZELqiC2r\n++IvDlX204m3pkctaenSOolun/RGOgFnOmoRIqvXZNG6NFgTTa91aBJ0hjX9lD+kqLo41D78q0YE\n9zTpq3Oc+x9pLfKcsmRaNQw6WkobsWV1X6+0yVis7X/D+3cofdfMFs0Xagoms/SG2tE6fMyTkbJO\nJ6LTd+AaYEmBEJm+stoSa/jp80JQa2EoED291q9+ek2wpAC2NO26Echm3QpT+Q5gboG2rzNekeeU\nvddUY3h67YY28pNpuNlnzBy/vHtofzDcYVqnCz3DLu1FA0zqmU70QoIoxRVWm/ThIDQYs3u02Nsj\nBQeVmn0CkD55m0xR7+mo2gYHgLCwCJijUvueWK8hCMC8Qk1f47pEfOATONNRDYOOlpQtq3ukHHxv\nnK7Ryu6h/eEO0xp3I5DZs6RFepgYG7hpxhylI8HIvmvA0IeBYDB2xwhfp/RVxfSaIAijm37K6TWV\nZ8SmNZtU/XkT0rDZLe/pqIXpNS2NbFsSa6tqmfxptT8I9OrYTj3DLu3eCEyMvXS0osx0htJrYtSZ\nTgIbuYVb4KjWjUA2KuhoUL1GADA8tc1CAtUw6GhJ3rI6IL1JiLG2qpZFBB0x2KfbTEeILI2dzOm1\naL3XolX0JbCRm9gZXrSs5j0dALClQeyLUjKt1nbVNISFBJpg0NGSNVWqDIssJEh0ptMX0HemI5vM\n6TXL6HU6Me/pAEOVhtGo3exTFiu9xpmO+iwsJNACg46GBJNJmpaPKCSIebxyT0cuJNDpnk4GZzoA\nIhp+Dg86IxfMCrYEdg/1eaXgpHaXcKbX9GMZUbFIqmDQ0Vpq+rB1OmN2q7UOn+noVjHDmY7ELL3J\niCMXh8ac6YxRNt3VKe3vo3YXjBFBR1QKCZheU10K7+logUFHa5F76sTba11+45D38BgrFacmeQW6\n1SptxzxZjVgcKoYGpb/dyNmfNf5MR+z2adLeRRg501Ha4DDoqM7C6jUtMOhoLXJPnXhl0PJMJxjU\ntWRakNNrkzm1BkS0wQm/kctv7iNnOrYEqtc0CjpItwPX/EM7eyrptUn8YUErciAXxljQTZ8bg47W\nIj+Z9vXFWacTvsgHgvFnRWqSV55P5tQaMHqdjtICJ3p6TRyrK0G3D0K2Q+UBArhppvSB5DNpywUw\nvaYdOdDYbPo3C76BMehoLZxeEwf6pRvUCSwOFQM90huf3oUEnOlIX+VCgnCzT2Hk2qU46TVxYEBq\noJqpQXptttQlQPwkvL+UFr3XSCLf0+H9HFUx6GhMsKVJn5gT2apAboPjD2/ZrVvJtBx0JvtMZ3j1\nmtjyqfTYPmLjv3jVa/JCWy3Sa9PypL/XhSbpsVz0wPSP+uRAzso1VTHoaE0uJIi3gRsw9MlKCTp6\nzXTCn+QnfdAJn/+BAYgDAxD/9zDwhRnA7NuGHyd/OIi1p0631AJHi/SaIAiAZy5EOej0B6WGpEz/\nqE8uJGDQURWDjtZS06R1OvIq8rHWbYyc6eh0sQsmM5CROVRQMFlFVK+J/3cEaG2B6eHSoS0PwgSz\nWZpZxJrpdPukrxptTibMngdcaYbo75aKHpha0waDjiY4J9daajoQCgFXpUAy5jqdcAWS6L8qHav2\nwsIxmJ74D2DKNN1eLxkJJpPUxdnfDfHYW8Ct+UDBgugHW22x7+loHXQ8cyECwIVzUvUaF4ZqQjCb\npeuB93RUxaCjtXD/NVFuizLGBSyYzFIFlZJe0+9iF26/U7fXSmpmC8R33wGCfTCtWhc7bTXWRm5d\n4Q7TWm3D/G9zALMZ4oWzSnqNNGJJYdBRGdNrWpNnK774QQeAtFbnqv5Bh8LCaTNh0RII/zY79nFj\nbeTW7ZP2TdLo7yfYbMDNHinoML2mrRQr99JRGYOOxpQtq+VPv/GKAywphsx0KMxslm7Mr3h87OOs\nNqkTeDTdPiArW/2xRRA8c4GL56XO5UyvaSfDPtSxg1TB9JrW5IowJb0W51NTihW42hY+lkFHd7fk\nQ5h1CwTX1LGPs8W5p6PFwtAIwux5EI++AVz8GHBP7ntxWjJtfWZ4Q1y6bgw6WkuV7+kkONOxRqRK\nGHR0Z95YkdiBYxQSoNsHfOEm9QYVjWeu9NXfrf1rTWJCLs+t2phe09qo9FqcmY6FQWdCsNrGWKfj\ng6BVEUGYkOMC5NkY7+nQBMKgo7XIQgKLZfgWuNHIMx2TiVVJSUywRa9eEwf6pRY4Wdqm1wBA8Egt\ncRh0aCJh0NGaPNMJXEusPbr8BmJL5SrzZGa1Rd9Pp1vDFjgjzQ6n2NgChyYQBh2tWa3SrAVIbH8c\nNhmcGGLd07kqLQzVOr0GhCvYAAic6dAEwqCjMUEQhlJsifRSU2Y6+nUjoHGIFXQ07kYwTN5MqTpS\nx84VRNeL83I9pKYDPdcSmr0IKVapxYleHaZpfGw2qTHo4OCw3myi1t0IIghmM0xP/RfgcGn+WkRq\nMTzo+P1+VFVVoa2tDVOmTMHTTz8Nu90+6rjjx4/jj3/8IwDgoYcewpIlSwAA//znP7F3714Edqa7\n3QAADiZJREFUg0HceeedWLdOal1y6dIl7N+/H8FgEGazGRs2bMDs2WOsMNeSvFYnkZQZ02sTQ+Se\nOpHduZWZjvaFBMBQio1oojA8vVZbW4v8/HxUV1cjPz8ftbW1o47x+/14/fXX8eyzz+LZZ5/F66+/\nDr/fDwDYv38/ysrKUF1djStXrqCxsREAcOjQIaxcuRK7d+/G6tWrcejQIV1/r2GU9FoiQceW+LFk\nnFgbuXX7gNQ0qVUNEY1ieNBpaGhAUVERAKCoqAgNDQ2jjmlsbERBQQHsdjvsdjsKCgrQ2NiIzs5O\nBAIB3HLLLRAEAffff7/y/YIgIBCQthPo6emBw6HPJ8+owkEnoV5cnOlMDHIlYrSgo8f9HKIJyvD0\nWldXlxIQHA4Huru7Rx3j9Xrhcg3lrZ1OJ7xe76jnXS4XvF6p3czatWuxc+dO/Pa3v0UoFMKPf/xj\njX+T2ITUdOk+TSI3fMMzHSGR8moyjGCzSX/TEU0/RQYdojHpEnR27NgBn8836vlHHnlk3D9TEASI\nohjz3//85z9j7dq1WLRoEerr61FTU4Nnnnkm6rFHjx7F0aNHAQC7du2C2+0e15gsFkvU7+3KcaAX\nQFp2DjLj/Gx/TjauAUjNcSBrnONIdrHO00TS554CH4Cc9DSkRPwu7deuwjJjJnKu8/e7Ec6R1niO\nEpNs50mXoBPrzR4AsrOz0dnZCYfDgc7OTmRlZY06xul04qOPPlIee71e3HbbbXC5XOjo6FCe7+jo\ngNPpBADU1dVh3bp1AIDFixdj3759McdQUlKCkpIS5XF7e3viv1wEt9sd9XtDgpTFDISAvjg/OxSU\n9rzvFYHgOMeR7GKdp4lE7A0CAHytn0FwDjXcHPS2IzT7tuv+/W6Ec6Q1nqPE6HWepk+fntBxht/T\nWbBgAerq6gBIgeKuu+4adcz8+fPx4Ycfwu/3w+/348MPP8T8+fPhcDiQlpaGjz/+GKIo4sSJE1iw\nQNrpMTJQnT59Grm5ufr9UiPJabVEFofKbXB4Izq5yYUEEf3XxIF+oMfP9BrRGAy/p7NixQpUVVXh\n2LFjcLvdKC8vBwBcuHAB77zzDp588knY7XY8/PDD+OEPfwgAWLlypVJWvWHDBrzwwgsIBoOYP38+\n7rxT2gGzrKwMBw8eRCgUQkpKCsrKyoz5BYGhVjiJ3KeR+61xcWhyCwc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66n/GEOmyMsyEmZCbjeoXw5k9P3HmrV/Xwr51NNnKE34dsFpX6qg6ST05V1N1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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -523,6 +649,7 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " a = util.discount(a, self.discount_rate * LAMBDA)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -543,99 +670,58 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 25.12\n", - "Iteration 2: Average Return = 31.17\n", - "Iteration 3: Average Return = 30.07\n", - "Iteration 4: Average Return = 31.98\n", - "Iteration 5: Average Return = 36.77\n", - "Iteration 6: Average Return = 36.22\n", - "Iteration 7: Average Return = 43.52\n", - "Iteration 8: Average Return = 45.12\n", - "Iteration 9: Average Return = 50.86\n", - "Iteration 10: Average Return = 58.81\n", - "Iteration 11: Average Return = 58.87\n", - "Iteration 12: Average Return = 65.66\n", - "Iteration 13: Average Return = 69.72\n", - "Iteration 14: Average Return = 76.32\n", - "Iteration 15: Average Return = 77.74\n", - "Iteration 16: Average Return = 78.17\n", - "Iteration 17: Average Return = 94.97\n", - "Iteration 18: Average Return = 89.34\n", - "Iteration 19: Average Return = 98.15\n", - "Iteration 20: Average Return = 103.35\n", - "Iteration 21: Average Return = 106.54\n", - "Iteration 22: Average Return = 109.03\n", - "Iteration 23: Average Return = 113.63\n", - "Iteration 24: Average Return = 119.11\n", - "Iteration 25: Average Return = 115.67\n", - "Iteration 26: Average Return = 126.51\n", - "Iteration 27: Average Return = 131.33\n", - "Iteration 28: Average Return = 138.83\n", - "Iteration 29: Average Return = 143.7\n", - "Iteration 30: Average Return = 146.15\n", - "Iteration 31: Average Return = 146.41\n", - "Iteration 32: Average Return = 157.34\n", - "Iteration 33: Average Return = 160.51\n", - "Iteration 34: Average Return = 159.67\n", - "Iteration 35: Average Return = 169.42\n", - "Iteration 36: Average Return = 170.71\n", - "Iteration 37: Average Return = 174.41\n", - "Iteration 38: Average Return = 172.93\n", - "Iteration 39: Average Return = 173.29\n", - "Iteration 40: Average Return = 177.32\n", - "Iteration 41: Average Return = 177.14\n", - "Iteration 42: Average Return = 179.85\n", - "Iteration 43: Average Return = 181.82\n", - "Iteration 44: Average Return = 182.0\n", - "Iteration 45: Average Return = 181.89\n", - "Iteration 46: Average Return = 183.19\n", - "Iteration 47: Average Return = 183.87\n", - "Iteration 48: Average Return = 183.26\n", - "Iteration 49: Average Return = 183.27\n", - "Iteration 50: Average Return = 189.11\n", - "Iteration 51: Average Return = 181.45\n", - "Iteration 52: Average Return = 186.91\n", - "Iteration 53: Average Return = 188.84\n", - "Iteration 54: Average Return = 189.76\n", - "Iteration 55: Average Return = 189.51\n", - "Iteration 56: Average Return = 186.36\n", - "Iteration 57: Average Return = 190.55\n", - "Iteration 58: Average Return = 189.35\n", - "Iteration 59: Average Return = 189.84\n", - "Iteration 60: Average Return = 187.14\n", - "Iteration 61: Average Return = 191.82\n", - "Iteration 62: Average Return = 189.32\n", - "Iteration 63: Average Return = 190.74\n", - "Iteration 64: Average Return = 188.13\n", - "Iteration 65: Average Return = 190.99\n", - "Iteration 66: Average Return = 189.23\n", - "Iteration 67: Average Return = 186.98\n", - "Iteration 68: Average Return = 188.0\n", - "Iteration 69: Average Return = 191.68\n", - "Iteration 70: Average Return = 188.03\n", - "Iteration 71: Average Return = 193.07\n", - "Iteration 72: Average Return = 191.96\n", - "Iteration 73: Average Return = 189.53\n", - "Iteration 74: Average Return = 186.71\n", - "Iteration 75: Average Return = 190.05\n", - "Iteration 76: Average Return = 191.1\n", - "Iteration 77: Average Return = 193.49\n", - "Iteration 78: Average Return = 188.66\n", - "Iteration 79: Average Return = 191.49\n", - "Iteration 80: Average Return = 191.68\n", - "Iteration 81: Average Return = 193.19\n", - "Iteration 82: Average Return = 193.87\n", - "Iteration 83: Average Return = 195.04\n", - "Solve at 83 iterations, which equals 8300 episodes.\n" + "Iteration 1: Average Return = 48.52\n", + "Iteration 2: Average Return = 62.44\n", + "Iteration 3: Average Return = 52.64\n", + "Iteration 4: Average Return = 60.4\n", + "Iteration 5: Average Return = 63.23\n", + "Iteration 6: Average Return = 64.07\n", + "Iteration 7: Average Return = 75.46\n", + "Iteration 8: Average Return = 80.36\n", + "Iteration 9: Average Return = 80.73\n", + "Iteration 10: Average Return = 78.7\n", + "Iteration 11: Average Return = 94.32\n", + "Iteration 12: Average Return = 94.99\n", + "Iteration 13: Average Return = 104.76\n", + "Iteration 14: Average Return = 103.1\n", + "Iteration 15: Average Return = 119.88\n", + "Iteration 16: Average Return = 130.84\n", + "Iteration 17: Average Return = 132.14\n", + "Iteration 18: Average Return = 144.28\n", + "Iteration 19: Average Return = 148.67\n", + "Iteration 20: Average Return = 145.85\n", + "Iteration 21: Average Return = 146.24\n", + "Iteration 22: Average Return = 159.14\n", + "Iteration 23: Average Return = 161.4\n", + "Iteration 24: Average Return = 166.98\n", + "Iteration 25: Average Return = 161.49\n", + "Iteration 26: Average Return = 162.79\n", + "Iteration 27: Average Return = 167.3\n", + "Iteration 28: Average Return = 176.73\n", + "Iteration 29: Average Return = 178.42\n", + "Iteration 30: Average Return = 179.28\n", + "Iteration 31: Average Return = 175.76\n", + "Iteration 32: Average Return = 182.85\n", + "Iteration 33: Average Return = 182.11\n", + "Iteration 34: Average Return = 184.32\n", + "Iteration 35: Average Return = 189.72\n", + "Iteration 36: Average Return = 188.47\n", + "Iteration 37: Average Return = 189.21\n", + "Iteration 38: Average Return = 189.04\n", + "Iteration 39: Average Return = 188.64\n", + "Iteration 40: Average Return = 192.69\n", + "Iteration 41: Average Return = 194.17\n", + "Iteration 42: Average Return = 195.64\n", + "Solve at 42 iterations, which equals 4200 episodes.\n" ] } ], @@ -658,14 +744,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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yk5+QkJBAWVkZb7/9NrW1te4V+z2SnOnSpeiyw1B+BHXl9YEORYhuzePk8tFH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\nR0DuXV5ezm233YZpmgG5vxDekEeRhWi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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -686,15 +772,6 @@ "util.plot_curve(loss_list, \"loss\")\n", "util.plot_curve(avg_return_list, \"average return\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { @@ -713,7 +790,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/photo/Q3.png b/photo/Q3.png new file mode 100644 index 0000000..07a1ab7 Binary files /dev/null and b/photo/Q3.png differ diff --git a/photo/Q4.png b/photo/Q4.png new file mode 100644 index 0000000..fdbc412 Binary files /dev/null and b/photo/Q4.png differ diff --git a/photo/Q6.png b/photo/Q6.png new file mode 100644 index 0000000..2353021 Binary files /dev/null and b/photo/Q6.png differ diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 99fecf3..ee18cf1 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -30,6 +30,8 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> + h1 = tf.layers.dense(self._observations, units=hidden_dim, activation=tf.tanh) + probs = tf.layers.dense(h1, units=out_dim, activation=tf.nn.softmax) # <<<<<<<< # -------------------------------------------------- @@ -72,6 +74,7 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 1~3 lines. """ # YOUR CODE HERE >>>>>> + surr_loss = -tf.reduce_mean(log_prob * self._advantages) # <<<<<<<< grads_and_vars = self._opt.compute_gradients(surr_loss) diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..e448e25 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,8 +32,10 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> + bb = np.append(b[1:],0.0) + return x + discount_rate*bb # <<<<<<<<<<<<<<<<<<<<<<<<<<<< - + def plot_curve(data, key, filename=None): # plot the surrogate loss curve x = np.arange(len(data)) diff --git a/report.md b/report.md index 1e5017e..49e2ccf 100644 --- a/report.md +++ b/report.md @@ -1,3 +1,50 @@ + + # Homework3-Policy-Gradient report -TA: try to elaborate the algorithms that you implemented and any details worth mentioned. +## Problem 1: Construct a neural network to represent policy +```Python + h1 = tf.layers.dense(self._observations, units=hidden_dim, activation=tf.tanh) + probs = tf.layers.dense(h1, units=out_dim, activation=tf.nn.softmax) +``` + +## Problem 2: Compute the surrogate loss +```Python + surr_loss = -tf.reduce_mean(log_prob * self._advantages) +``` +前兩題分別把 Policy Gradient的網路架構和loss function造出 + +## Problem 3: Use baseline to reduce the variance of our gradient estimate +```Python + a = r - b +``` +前3題主要在架設Policy-Gradient的結構 +運用神經網路計算出各方向的機率做加分 +越往上(陡峭)分數越高 +然而要設定baseline使得較差的路徑分數下降 +![Q3](https://github.com/w95wayne10/homework3-policy-gradient/blob/master/photo/Q3.png) + +## Problem 4 +測試拿掉baseline的狀況 +收斂速度下降 +![Q4](https://github.com/w95wayne10/homework3-policy-gradient/blob/master/photo/Q4.png) + +## Problem 5 Actor-Critic algorithm (with bootstrapping) +```Python + bb = np.append(b[1:],0.0) + return x + discount_rate*bb +``` + +## Problem 6: Generalized Advantage Estimation +```Python + r = util.discount_bootstrap(p["rewards"], self.discount_rate, b) + target_v = util.discount_cumsum(p["rewards"], self.discount_rate) + a = r - b + a = util.discount(a, self.discount_rate * LAMBDA) +``` +從採樣方式(5題) +和baseline取法(各方向期望值)做優化(6題) +使之更快速完成訓練 +從圖中可看出收斂速度明顯上升 +(因為是結合第三題和第五題的結果 如第五題寫錯會反映在此題) +![Q6](https://github.com/w95wayne10/homework3-policy-gradient/blob/master/photo/Q6.png)