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diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb
index 4529e50..0354bb7 100644
--- a/Lab3-policy-gradient.ipynb
+++ b/Lab3-policy-gradient.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -28,17 +28,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n"
-     ]
-    }
-   ],
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
    "source": [
     "import gym\n",
     "import tensorflow as tf\n",
@@ -103,14 +97,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
    "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",
+      "C:\\ProgramData\\Anaconda3\\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"
      ]
     }
@@ -214,6 +210,7 @@
     "            Sample solution should be only 1 line.\n",
     "            \"\"\"\n",
     "            # YOUR CODE HERE >>>>>>\n",
+    "            a = np.subtract(r, b)\n",
     "            # <<<<<<<<\n",
     "\n",
     "            p[\"returns\"] = r\n",
@@ -258,98 +255,70 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
    "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 = 26.15\n",
+      "Iteration 2: Average Return = 29.44\n",
+      "Iteration 3: Average Return = 35.15\n",
+      "Iteration 4: Average Return = 34.95\n",
+      "Iteration 5: Average Return = 40.74\n",
+      "Iteration 6: Average Return = 42.15\n",
+      "Iteration 7: Average Return = 49.97\n",
+      "Iteration 8: Average Return = 53.33\n",
+      "Iteration 9: Average Return = 49.59\n",
+      "Iteration 10: Average Return = 60.38\n",
+      "Iteration 11: Average Return = 60.81\n",
+      "Iteration 12: Average Return = 58.13\n",
+      "Iteration 13: Average Return = 64.9\n",
+      "Iteration 14: Average Return = 69.13\n",
+      "Iteration 15: Average Return = 69.71\n",
+      "Iteration 16: Average Return = 72.03\n",
+      "Iteration 17: Average Return = 85.71\n",
+      "Iteration 18: Average Return = 75.25\n",
+      "Iteration 19: Average Return = 84.27\n",
+      "Iteration 20: Average Return = 92.77\n",
+      "Iteration 21: Average Return = 101.06\n",
+      "Iteration 22: Average Return = 100.37\n",
+      "Iteration 23: Average Return = 103.63\n",
+      "Iteration 24: Average Return = 122.37\n",
+      "Iteration 25: Average Return = 127.16\n",
+      "Iteration 26: Average Return = 134.51\n",
+      "Iteration 27: Average Return = 141.05\n",
+      "Iteration 28: Average Return = 139.66\n",
+      "Iteration 29: Average Return = 147.9\n",
+      "Iteration 30: Average Return = 138.67\n",
+      "Iteration 31: Average Return = 153.0\n",
+      "Iteration 32: Average Return = 149.51\n",
+      "Iteration 33: Average Return = 151.01\n",
+      "Iteration 34: Average Return = 151.19\n",
+      "Iteration 35: Average Return = 156.05\n",
+      "Iteration 36: Average Return = 170.66\n",
+      "Iteration 37: Average Return = 169.34\n",
+      "Iteration 38: Average Return = 166.82\n",
+      "Iteration 39: Average Return = 175.26\n",
+      "Iteration 40: Average Return = 179.41\n",
+      "Iteration 41: Average Return = 184.0\n",
+      "Iteration 42: Average Return = 186.71\n",
+      "Iteration 43: Average Return = 180.32\n",
+      "Iteration 44: Average Return = 184.96\n",
+      "Iteration 45: Average Return = 188.73\n",
+      "Iteration 46: Average Return = 191.61\n",
+      "Iteration 47: Average Return = 191.81\n",
+      "Iteration 48: Average Return = 188.6\n",
+      "Iteration 49: Average Return = 192.66\n",
+      "Iteration 50: Average Return = 190.08\n",
+      "Iteration 51: Average Return = 193.29\n",
+      "Iteration 52: Average Return = 190.55\n",
+      "Iteration 53: Average Return = 193.46\n",
+      "Iteration 54: Average Return = 197.62\n",
+      "Solve at 54 iterations, which equals 5400 episodes.\n"
      ]
     }
    ],
@@ -362,6 +331,7 @@
     "discount_rate = 0.99\n",
     "baseline = LinearFeatureBaseline(env.spec)\n",
     "\n",
+    "\n",
     "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n",
     "                     discount_rate)\n",
     "\n",
@@ -371,14 +341,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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NnnIp+h+voj98xxgdFmnB9It7UAHNf2mdyjTlMpx5X6H/9TZE90N1YIkjFR6J6Z7Fxl/w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\nDTX+jM4LWgjRo/i85iLaToVFoC66Cv3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2xzup2fONth8e8N2PhKT+PTUBUM4NkfR0o7J9f7abc+1npMFFpbuCi7Misr+i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\nMTt37gRg586dLFmyxKNNYWE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ohwP94jNGBYGipT7bqaQU1CeL0W/+zShYmeV7MR9AxcSA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\nTjerErys6AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107bac18>"
+       "<matplotlib.figure.Figure at 0x22ce8de4e10>"
       ]
      },
      "metadata": {},
@@ -386,9 +358,9 @@
     },
     {
      "data": {
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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds\nNhv3338/3t6Vr+IlRE1ow0CvfBUOfYe67zFU526O51TrMNTd0RZWJ4T1ZFnVn8npsDkNrZ10brZ9\ncaD/fQ5n01E334lt3B01ft+G1k7OIu1kTqPvhhLCKlpr9Kq/2xcFKi+HLj2xTZwGfQdbXZoQLknC\nQjROPx5Fb/kMNWg4auwkVIhMxyHE5UhYiEZJ79oKNhvq9vtQ3jIdhxCVkTW4RaOjtUbv2gZdekpQ\nCGGShIVofH46AeknUTI+IYRpEhai0dG7toGyofoMtLoUIeoNCQvR6OhdW6FzN5SPv9WlCFFvSFiI\nRkWf/BFOpaL6SReUEFUhYSEaFb17GyiF6nPh5H9CiEuTsBCNit71FXTogvILrPzFQggHCQvRYOmS\nEvTB79AF+fbHZ05C2jHpghKiGuSmPNEg6cICjEXPwLFDoGzQLhyaNgNA9ZGwEKKqJCxEg6ML8zEW\n/Q1+PIqaPB3y89DffwNH9kPn7qjAYKtLFKLekbAQDYouzMeIewZSj2Gb+RSq98/3UoyfjC4uAjc3\nawsUop6SsBANhi4t/TUoHpiD6nVNhefVz91QQoiqkwFu0XB8vweOH0ZNnXVBUAghakbCQjQYeu9O\naNIU1W+o1aUI0eBIWIgGQWuN/nYnXN0L5eFhdTlCNDgSFqJhOPkjZGWgevS3uhIhGiQJC9Eg6L07\nAVDd+1lciRANU51cDRUfH8/u3bvx9fUlNjYWgDVr1rBhwwZ8fOyLz0yePJm+ffsCsHbtWjZu3IjN\nZmPatGn07t27LsoU9ZjeuxPatkcFBFldihANUp2ExfDhw7nhhhtYunRphe1jx45l/PjxFbalpaWx\nbds2Xn75ZbKzs5k3bx6vvPIKNpucBImL0wX5cOR71A1/sLoUIRqsOvkJ3LVrV7y9vU29Njk5mcGD\nB+Ph4UHLli0JCQnhyJEjTq5Q1Gd6/x4wDFQP6YISwlksvSlv/fr1bNmyhfDwcKZMmYK3tzdZWVl0\n6tTJ8ZqAgACysrIsrFK4vL3J0LwFhF9ldSVCNFiWhcXo0aO57bbbAFi9ejUrV64kOjq6Su+RmJhI\nYmIiADExMQQFVb+/2t3dvUb7Nxau1k7aMMjYl0LTfhH4tmxldTkOrtZOrkrayRxXaCfLwsLPz8/x\n9ciRI1mwYAFgP5M4e/as47msrCwCAgIu+h5RUVFERUU5HmdmZla7nqCgoBrt31i4WjvpowfR53Io\n6dTdpepytXZyVdJO5jiznUJDQ029zrJR4+zsbMfXO3bsICwsDID+/fuzbds2SktLSU9P59SpU3Ts\n2NGqMoWL03t3gbKhuve1uhQhGjRTZxb5+fl89NFHnDhxguLi4grPPfvss5Xuv2jRIvbv309eXh4z\nZ85k0qRJ7Nu3j+PHj6OUIjg4mOnTpwMQFhZGREQEjz76KDabjXvvvVeuhBIO+ttk9Feb0Hm5kJcL\nmachvDPK28fq0oRo0JTWWlf2ovnz51NWVkZERASenp4Vnhs+fLizaquykydPVntfOR02x6p20uXl\n6LUr0evXgl8ABIVACx+Utw9q8AhUx651XtPlyOfJHGknc1yhG8rUmcWhQ4d4/fXX8ZA5d4QFdE4W\nxvKFcGgfaviNqEn3yfxPQtQxU2HRrl07zp49S0hIiLPrEaICffonjIV/geIi1L2PYhs03OqShGiU\nTIVF9+7def755xk+fHiFq5gARowY4ZTChNBGOcaKV6CsDNvTL6HaXGF1SUI0WqbC4sCBAwQGBrJ3\n794LnpOwEM6iN30CPxxA/Wm2BIUQFqs0LLTWzJw5k6CgINxk/WJRR3TGafSHK6F7P5R0PQlhuUqv\nSVVK8fjjj6OUqot6hEBrjbHyVbDZsN0TLZ89IVyAqRsYrrzySk6dOuXsWoQAQP/vczjwLWriNFRA\nsNXlCCEwOWbRrVs3nn/+eSIjIy+Yn0TGLERt0kWF6PdXwFU9UMOut7ocIcTPTIXFwYMHadmyJd9/\n//0Fz0lYiNqkt22EogJsf5gq3U9CuBBTYfHMM884uw4h0IaB3vRfaN8Z1b5T5TsIIeqMqTELwzAu\n+UeIWrN/D5z5CTVinNWVCCF+x9SZxeTJky/53OrVq2utGNG4GRv/Az5+qP5DrC5FCPE7psLi1Vdf\nrfA4OzubhIQE+vfv75SiROOj00/Bd7tQY29Hucu8T0K4GlPdUMHBwRX+dO7cmYceeoh169Y5uz7R\nSOjNn4DNhoqUK6CEcEXVXiiisLCQc+fO1WYtopHSJcXorYmovoNRfoFWlyOEuAhT3VBLliypcBlj\nSUkJ33//PcOGDXNaYaLx0Ns3Q2EBasRYq0sRQlyCqbD4/dTkTZo0YdSoUfTs2dMpRYnGQxcWoP+7\nBq7oCB2utrocIcQlmAqL3r1706nThde9HzlyRNbHFjWi338LcrKwPfAXuQlPCBdmasziueeeu+j2\n+fPn12oxonHR+1LQ//scdf0tchOeEC7usmcWv9x0p7V2/PnFmTNnZMpyUW26qBBj5RIIaYsaf+n7\neIQQruGyYfHbm/HuuOOOCs/ZbDZuueUWUweJj49n9+7d+Pr6EhsbC8Dbb7/Nrl27cHd3p1WrVkRH\nR9O8eXPS09OZPXu2YxHxTp06MX369Cp9U8L16fdXQHYWtqdiUB6eVpcjhKjEZcPi1VdfRWvN3/72\nN5599lm01iilUErh4+ODp6e5/+TDhw/nhhtuYOnSpY5tPXv25M4778TNzY1Vq1axdu1a7r77bsA+\noL5w4cIafFvClelD+9BbPkONvgXVoYvV5QghTLhsWAQH29cSiI+PB+zdUrm5ufj7+1fpIF27diU9\nPb3Ctl69ejm+7ty5M9u3b6/Se4r6S2/fBM28UDffaXUpQgiTTF0NVVBQwOuvv8727dtxd3fn7bff\nZufOnRw5cuSC7qnq2LhxI4MHD3Y8Tk9P58knn6RZs2bccccdXH21XFLZUGit0d/uRHXtg/JsYnU5\nQgiTTIXF8uXLad68OfHx8Tz66KOA/Wxg5cqVNQ6LDz/8EDc3N8cNfv7+/sTHx9OiRQuOHj3KwoUL\niY2NxcvL64J9ExMTSUxMBCAmJuaChZmqwt3dvUb7NxY1bafSHw6SlZtFi8HX0awBt7d8nsyRdjLH\nFdrJVFjs3buX1157DXf3X1/u4+NDbm5ujQ6+efNmdu3axdy5cx3X2Ht4eODhYZ9ILjw8nFatWnHq\n1Ck6dOhwwf5RUVFERUU5HmdmZla7lqCgoBrt31jUtJ2M/30BSpF/ZWcKGnB7y+fJHGknc5zZTr9c\nTFQZU/dZeHl5kZeXV2FbZmZmlccufmvPnj2sW7eOp556iiZNfu2OOHfunOOS3TNnznDq1ClatWpV\n7eMI16K/3QlXdkL5+FldihCiCkydWYwcOZLY2FjuuOMOtNYcOnSId955h1GjRpk6yKJFi9i/fz95\neXnMnDmTSZMmsXbtWsrKypg3bx7w6yWy+/fvZ82aNbi5uWGz2bj//vvx9vau/ncoXIY+lwPHD8t9\nFULUQ6bC4uabb8bT05M33niD8vJy/v73vxMVFcWYMWNMHeSRRx65YNul1u4eNGgQgwYNMvW+on7R\n3+0CrVE9BlhdihCiiioNC8Mw2Lx5M6NGjTIdDkLo4iLw8ET99i7/b3eCXwC0C7euMCFEtVQ6ZmGz\n2Vi5cqVj0FmIyuiSEoz/92eM5x9DFxbYt5WVofenoHr0lwkDhaiHTA1w9+vXj507dzq7FtFA6PUf\nQsZp+OkExqvz0CUlcGQ/FBWieshSvELUR6bGLEpLS3n55Zfp3LkzgYGBFX4zfOihh5xWnKh/9NkM\n9PoPUP2HQt8I9PKXMF5bgGrZGtzd4epelb+JEMLlmAqLsLAwwsLCnF2LaAD0+28BoG6bhgoMxigs\nQK+KRwN07YNq2szS+oQQ1WMqLCZOnOjsOkQDoA9+h975JeqmyahA+7xitsgbMAry0GvfRvUeaHGF\nQojqMhUWQlRGG+UY7y6HgGDU9bdWeE7deBuqe19o296i6oQQNWVqgFuIyuikzyDtGLaJ01BNKk4Q\nqJRCteuAssnHTYj6Sv73ihrT6Sftixl17Q39hlhdjhDCCSQsRI3o8nKMN+LA3R3b1D/LPRRCNFCm\nxiy01mzYsIGtW7eSl5fHSy+9xP79+8nJyamwDoVofPRnH8DRg6j7H0f5B1pdjhDCSUydWaxevZpN\nmzYRFRXlmCY3MDCQdevWObU44dr0iSPoj99BDRiG7ZprrS5HCOFEpsIiKSmJp556iiFDhji6GVq2\nbHnBUqmi8dDFhRivvwwt/FB3zbS6HCGEk5kKC8MwaNq0aYVtxcXFF2wTjYM+lYbx/BNw5iS2aQ+j\nmrewuiQhhJOZCos+ffqwcuVKSktLAfsYxurVq+nXr59TixOup/irTRjzH4O8XGyzn0V17WN1SUKI\nOmAqLKZMmUJ2djZTp06lsLCQKVOmkJGRwV133eXs+oSL0FpjfPBPcl/8PwgNw/bXOJTM8yREo2Hq\naigvLy+eeOIJcnJyyMzMJCgoCD8/WRazsdBao/+9DL35U5qNvpmSCVNQMmW9EI2KqbD4ZU1sHx8f\nfHx8HNtsckdug/fboFDX30qLGY9x/uxZq8sSQtQxU2ExefLF10x2c3PD39+fgQMHMmnSJBnwbmB+\nHxTqD3+Um+6EaKRMhcW0adNITk5mwoQJBAYGkpmZyUcffUTfvn0JDQ3lvffeY8WKFcycKZdQNiR6\n7UoJCiEEYDIs/vvf/7JgwQK8vLwACA0NpUOHDsyZM4clS5bQrl07nnrqqcu+R3x8PLt378bX15fY\n2FgA8vPziYuLIyMjg+DgYGbPno23tzdaa9566y1SUlJo0qQJ0dHRhIfLus11SZ/4Af3ZWtTQURIU\nQghzV0MVFhZSUlJSYVtJSQmFhYUA+Pn5cf78+cu+x/Dhw3n66acrbEtISKBHjx4sXryYHj16kJCQ\nAEBKSgqnT59m8eLFTJ8+nddff930NyRqThvlGKvioYUPauI0CQohhLmwiIyM5LnnniMxMZE9e/aw\nYcMG5s+fT2RkJADffPMNoaGhl32Prl274u3tXWFbcnKy4z0iIyNJTk4GYOfOnVx77bUopejcuTMF\nBQVkZ2dX+ZsT1aO3rIfjh1ET/4Ty8q58ByFEg2eqG+ruu+8mJCSEbdu2kZ2djZ+fH9dffz1RUVEA\ndOvWjWeffbbKB8/NzcXf3x+wn53k5uYCkJWVRVBQkON1gYGBZGVlOV4rnEefy0Z/+DZ06YkaGGl1\nOUIIF2EqLGw2G6NHj2b06NEXfd7T07PGhSilqtzdkZiYSGJiIgAxMTEVAqaq3N3da7R/Q5G7ainF\npSUEPjgH9+DgC56XdjJH2skcaSdzXKGdTC+rmpOTw5EjR8jLy0Nr7dg+YsSIah/c19eX7Oxs/P39\nyc7OdtzDERAQ4JjdFuDs2bMEBARcsH9UVJTj7AaosE9VBQUF1Wj/hkAf3o+RtB41ZhI5Tb3hIu0h\n7WSOtJM50k7mOLOdKhtC+IWpsNixYwdLliyhdevWpKamEhYWRmpqKl26dKlRWPTv35+kpCQmTJhA\nUlISAwYMcGz/7LPPGDJkCIcPH8bLy0u6oOqA3vwpeHmjxky0uhQhhIsxFRarV68mOjqaiIgIpk2b\nxosvvsimTZtITU01faBFixaxf/9+8vLymDlzJpMmTWLChAnExcWxceNGx6WzYJ+4cPfu3Tz88MN4\nenoSHR1dve9OmKaLC9F7vkINGnHBGtpCCGEqLDIzM4mIiKiwLTIykunTpzNlyhRTB3rkkUcuun3u\n3LkXbFNKcd9995l6X1E12igHQNncKm7fvR3On0dFDLegKiGEqzN16ayPjw85OTkABAcHc+jQIc6c\nOeOYM0rUH8aiv2G8tvCC7Xr7JghqBR2utqAqIYSrM3VmMXLkSA4cOMCgQYMYO3Yszz77LEopxo0b\n5+z6RC3SZaVwaB+Ul6H3pzjWotA5Z+HAt6ixk+QGPCHERZkKi/HjxztmmI2MjKRbt24UFxfTtm1b\npxYnatmpNCgvA2XDWPMmtr8uQrm5ob/eAlqjBl1ndYVCCBdVaTeUYRjcc889jlXywH4ZlwRF/aN/\nPAqAmnAkt/EjAAAWbElEQVQX/HQC/eUX9u3bN0H7zqhW5i6hE0I0PpWGhc1mIzQ0lLy8vLqoRzhT\n6lFo0hR1w63QuRt63b/Qh/dD2nHUoOFWVyeEcGGmuqGGDh3KggULuPHGGwkMDKzQr929e3enFSdq\nl049Cm2vRNncsE26F2P+Yxjxz4ObG2rAtVaXJ4RwYabC4vPPPwfgvffeq7BdKcWrr75a+1WJWqcN\nA1KPoQYOB0Bd0REVMQK9bQP0ugbVwsfaAoUQLs1UWCxdutTZdQhnO5sORYUQ1t6xSd1yN/roQWwj\nxlpYmBCiPjA9N1RZWRmHDx8mOzubwYMHU1xcDCBLqdYXvwxut/t1ESnlF4jbvHirKhJC1COmwuLH\nH39kwYIFeHh4cPbsWQYPHsz+/ftJSkpyTNEhXJv+8SjYbNDmCqtLEULUQ6bu4F6+fDm33347ixYt\nwt3dni9du3blwIEDTi1O1B6dehRah6E8aj6dvBCi8TEVFmlpaQwbNqzCtqZNm1a6lKpwIalHUWGy\njrkQonpMhUVwcDBHjx6tsO3IkSOEhIQ4pShRu/S5HMjJqjC4LYQQVWFqzOL2228nJiaGUaNGUVZW\nxtq1a/niiy+YMWOGs+sTtSH1GFBxcFsIIarC1JlFv379ePrppzl37hxdu3YlIyODxx9/nF69ejm7\nPlELfpnmQ84shBDVZerM4ty5c7Rv317WmKivUo9CYEtU8xZWVyKEqKdMhUV0dDTdunVj6NChDBgw\nQO6tqGd06lGQwW0hRA2Y6oaKj4+nb9++fP7550yfPp1Fixaxc+dOysvLnV2fqCFdXARnTqKkC0oI\nUQOmzix8fHy4/vrruf7668nIyGDr1q28++67/P3vf+eNN95wdo2iJtKO29eqkMFtIUQNmJ7u4xe5\nubnk5OSQl5dH8+bNnVGTqCFdVAgZpyDjtH1tbZBuKCFEjZgKi7S0NL788ku2bt3K+fPniYiI4Ikn\nnqBjx441OvjJkyeJi4tzPE5PT2fSpEkUFBSwYcMGfHzsM6FOnjyZvn371uhYDZ3WGn44gPHFOkjZ\nDvo366Nf0RECgqwrTghR75kKi7/+9a8MHDiQ6dOn061bN8cSqzUVGhrKwoULAfuKfDNmzOCaa65h\n06ZNjB07lvHjx9fKcRo6vXcXxsfvwLFD4OWNGn0zqv1VEBwCQa1QXnIGKISoGVNhsXz5csecUM6y\nd+9eQkJCCA4OdupxGhqdcRrj1Xn2S2PvnIkaPALVRK5WE0LULlMJ4O7uTk5ODkeOHCEvL8/e5fGz\nESNG1EohW7duZciQIY7H69evZ8uWLYSHhzNlyhS8vb1r5TgNjf7sQ7DZsD35Asov0OpyhBANlNK/\n/cl/CTt27GDJkiW0bt2a1NRUwsLCSE1NpUuXLjzzzDM1LqKsrIwZM2YQGxuLn58fOTk5jvGK1atX\nk52dTXR09AX7JSYmkpiYCEBMTEyNJjZ0d3enrKys2vtbofxsBpkzb6PZyHH4zHyiTo5ZH9vJCtJO\n5kg7mePMdvL0NDcTtakzi9WrVxMdHU1ERATTpk3jxRdfZNOmTaSmptaoyF+kpKTQvn17/Pz8ABx/\nA4wcOZIFCxZcdL+oqCiioqIcjzMzM6tdQ1BQUI32t4Kx+g0wyikZPqbOaq+P7WQFaSdzpJ3McWY7\nhYaGmnqdqZHqzMxMIiIiKmyLjIxky5YtVa/sIn7fBZWdne34eseOHYSFhdXKcRoSfS4HveUz1KDr\nUEGtrC5HCNHAmb4pLycnBz8/P4KDgzl06BAtWrTAMIzKd65EcXEx3377LdOnT3dsW7VqFcePH0cp\nRXBwcIXnhJ1OXAelpagb/2B1KUKIRsBUWIwcOZIDBw4waNAgxo4dy7PPPotSinHjxtW4gKZNm/Lm\nm29W2DZr1qwav29Dpgvy0Zs+QfUfigppa3U5QohGwFRYTJgwwfF1ZGQk3bp1o7i4mLZt5QeVFfQX\nCVBchBpzm9WlCCEaiWrdPBEUJHcDW0V//w36k/dR10Si2srkgEKIulE7t2KLOqGzMjD+sRBC2qDu\necDqcoQQjYiERT2hS0sxli2AslJsD/wF1dTL6pKEEI2IhEU9od/9Bxw7hG3aI6jWMlYkhKhbEhb1\ngN79FXrLetSNf0D1jah8ByGEqGUSFi5Oa43xn3ft4xQT7ra6HCFEIyVh4er2pUDqMdT1t6JsblZX\nI4RopCQsXJzx2QfgF4gaNNzqUoQQjZiEhQvTPxyAg3tRoyeg3D2sLkcI0YhJWLgw47MPoHkL1LDR\nVpcihGjkJCxclD75I+z5GjViLKppM6vLEUI0cs5dK1WYprWG8+ehvAzKy9GfvAeeTVDX1XyyRiGE\nqCkJCxdhvPI3+5VPv6FG3oRq4WNNQUII8RsSFi5Apx2HfSmoAcPgyk7g5g6enqh+QyrdVwgh6oKE\nhQvQW9aDuzvqzhkobzmTEEK4HhngtpguKUFv34zqO0SCQgjhsiQsLKZ3/g+KClCR11tdihBCXJKE\nhcX0lvXQOgw6dbO6FCGEuCQJCwvptGNw9CDq2tEopawuRwghLsklBrgffPBBmjZtis1mw83NjZiY\nGPLz84mLiyMjI4Pg4GBmz56Nt7e31aXWKp20Htw9UBEjrC5FCCEuyyXCAuCZZ57Bx+fXAd6EhAR6\n9OjBhAkTSEhIICEhgbvvbjhTdOuSYvTXm1H9h6Cat7C6HCGEuCyX7YZKTk4mMjISgMjISJKTky2u\nqHbpr5OgqBB17Q1WlyKEEJVymTOL+fPnAzBq1CiioqLIzc3F398fAD8/P3Jzc60sr1bp8yXo/6yG\nKzpCx6utLkcIISrlEmExb948AgICyM3N5bnnniM0NLTC80qpiw4AJyYmkpiYCEBMTAxBQUHVrsHd\n3b1G+1dFwQcryc/OxP/Rv+EZHFwnx6wtddlO9Zm0kznSTua4Qju5RFgEBAQA4Ovry4ABAzhy5Ai+\nvr5kZ2fj7+9PdnZ2hfGMX0RFRREVFeV4nJmZWe0agoKCarS/WfpcDsb7/4Re13AupB3UwTFrU121\nU30n7WSOtJM5zmyn3/9yfimWj1kUFxdTVFTk+Prbb7+lXbt29O/fn6SkJACSkpIYMGCAlWXWGv3x\nO3C+BNttU60uRQghTLP8zCI3N5eXXnoJgPLycoYOHUrv3r3p0KEDcXFxbNy40XHpbH2nT6Wit6xH\nRd6ACmlrdTlCCGGa5WHRqlUrFi5ceMH2Fi1aMHfuXAsqch7j/RXQpCnqpslWlyKEEFVieVg0Bjr1\nGMbH78C3yahb/4hq4Wt1SUIIUSUSFk6kf/oR46N/we6voJkX6qY7UKNutrosIYSoMgkLJ9GFBRgL\nngK0PSRGjkc1b1jTlQghGg8JCyfRWxOhqADb/8WiruxkdTlCCFEjll862xBpoxy94WPo1BUJCiFE\nQyBh4Qx7dsDZdGwjx1tdiRBC1AoJCycwNnwEgS2h90CrSxFCiFohYVHL9I8/wKF9qBFjUW5uVpcj\nhBC1QsKilunEj+033g0dZXUpQghRa+RqqBrQJSXob3eggkIgNAyKi9DJW1DDrkd5yWWyQoiGQ8Ki\nBvTq5ej/fY7+ZUPzFlBWhhoxzsqyhBCi1klYVJPel4L+3+eo68aguvRCnzwBaSegdRgqpI3V5Qkh\nRK2SsKgGXViAsXKJPRgm/gnl4YnqG2F1WUII4TQywF0N+v23IDsL29SHUR6eVpcjhBBOJ2FRRY7u\np9ETUOFXWV2OEELUCQmLKtCHvsNYsdje/XTznVaXI4QQdUbGLH5H79qK8dmHqF4DUAOuRbUKRedk\noT9Ygd6+GQKCsd33qHQ/CSEaFQmL39DnSzDeXQ4lJeh1/0av+ze0C4f0U1BWiho7CXXjRFSTJlaX\nKoQQdUrC4jf0pk8gJwvb489DcAh655fo3dugSy9st01FtQq1ukQhhLCEhMXPjIJ89KfvQ7c+qKu6\nA6BGT4DREyyuTAghrGdpWGRmZrJ06VJycnJQShEVFcWYMWNYs2YNGzZswMfHB4DJkyfTt29fp9ZS\nuO4dKMjDdssUpx5HCCHqI0vDws3NjXvuuYfw8HCKioqYM2cOPXv2BGDs2LGMH18360HoczkUfvwu\nqt8Q1BUd6uSYQghRn1gaFv7+/vj7+wPQrFkz2rRpQ1ZWVp3XoT95D33+PLYJd9X5sYUQoj5wmfss\n0tPTOXbsGB07dgRg/fr1PP7448THx5Ofn++04+qzGeikT2k6YgwqpK3TjiOEEPWZ0lrryl/mXMXF\nxTzzzDPceuutDBw4kJycHMd4xerVq8nOziY6OvqC/RITE0lMTAQgJiaG8+fPV/nYZT+dIO+NRfjP\n+v/AP7Bm30gj4O7uTllZmdVluDxpJ3OkncxxZjt5epq7Z8zysCgrK2PBggX06tWLceMunNo7PT2d\nBQsWEBsbW+l7nTx5stp1BAUFkZmZWe39GwtpJ3OkncyRdjLHme0UGmrulgBLu6G01ixbtow2bdpU\nCIrs7GzH1zt27CAsLMyK8oQQQvzM0gHugwcPsmXLFtq1a8cTTzwB2C+T3bp1K8ePH0cpRXBwMNOn\nT7eyTCGEaPQsDYsuXbqwZs2aC7Y7+54KIYQQVeMyV0MJIYRwXRIWQgghKiVhIYQQolISFkIIISol\nYSGEEKJSlt+UJ4QQwvXJmcXP5syZY3UJ9YK0kznSTuZIO5njCu0kYSGEEKJSEhZCCCEqJWHxs6io\nKKtLqBekncyRdjJH2skcV2gnGeAWQghRKTmzEEIIUSlLJxJ0BXv27OGtt97CMAxGjhzJhAkTrC7J\nJWRmZrJ06VJycnJQShEVFcWYMWPIz88nLi6OjIwMgoODmT17Nt7e3laXaznDMJgzZw4BAQHMmTOH\n9PR0Fi1aRF5eHuHh4cyaNQt390b/342CggKWLVtGamoqSikeeOABQkND5TP1O//5z3/YuHEjSinC\nwsKIjo4mJyfH0s9Uoz6zMAyDN954g6effpq4uDi2bt1KWlqa1WW5BDc3N+655x7i4uKYP38+69ev\nJy0tjYSEBHr06MHixYvp0aMHCQkJVpfqEj755BPatGnjeLxq1SrGjh3LkiVLaN68ORs3brSwOtfx\n1ltv0bt3bxYtWsTChQtp06aNfKZ+Jysri08//ZSYmBhiY2MxDINt27ZZ/plq1GFx5MgRQkJCaNWq\nFe7u7gwePJjk5GSry3IJ/v7+hIeHA9CsWTPatGlDVlYWycnJREZGAhAZGSntBZw9e5bdu3czcuRI\nwL6o1759+xg0aBAAw4cPl3YCCgsL+f777xkxYgRgXyq0efPm8pm6CMMwOH/+POXl5Zw/fx4/Pz/L\nP1ON+rw4KyuLwMBf190ODAzk8OHDFlbkmtLT0zl27BgdO3YkNzcXf39/APz8/MjNzbW4OuutWLGC\nu+++m6KiIgDy8vLw8vLCzc0NgICAALKysqws0SWkp6fj4+NDfHw8J06cIDw8nKlTp8pn6ncCAgK4\n6aabeOCBB/D09KRXr16Eh4db/plq1GcWonLFxcXExsYydepUvLy8KjynlEIpZVFlrmHXrl34+vo6\nzsLEpZWXl3Ps2DFGjx7Niy++SJMmTS7ocpLPFOTn55OcnMzSpUt57bXXKC4uZs+ePVaX1bjPLAIC\nAjh79qzj8dmzZwkICLCwItdSVlZGbGwsw4YNY+DAgQD4+vqSnZ2Nv78/2dnZ+Pj4WFyltQ4ePMjO\nnTtJSUnh/PnzFBUVsWLFCgoLCykvL8fNzY2srCz5XGE/cw8MDKRTp04ADBo0iISEBPlM/c7evXtp\n2bKlox0GDhzIwYMHLf9MNeoziw4dOnDq1CnS09MpKytj27Zt9O/f3+qyXILWmmXLltGmTRvGjRvn\n2N6/f3+SkpIASEpKYsCAAVaV6BLuvPNOli1bxtKlS3nkkUfo3r07Dz/8MN26dWP79u0AbN68WT5X\n2LuYAgMDOXnyJGD/odi2bVv5TP1OUFAQhw8fpqSkBK21o52s/kw1+pvydu/ezT//+U8Mw+C6667j\n1ltvtbokl3DgwAHmzp1Lu3btHN0CkydPplOnTsTFxZGZmSmXOf7Ovn37+Pjjj5kzZw5nzpxh0aJF\n5Ofn0759e2bNmoWHh4fVJVru+PHjLFu2jLKyMlq2bEl0dDRaa/lM/c6aNWvYtm0bbm5uXHnllcyc\nOZOsrCxLP1ONPiyEEEJUrlF3QwkhhDBHwkIIIUSlJCyEEEJUSsJCCCFEpSQshBBCVErCQjRKjz76\nKPv27bPk2JmZmdxzzz0YhmHJ8YWoDrl0VjRqa9as4fTp0zz88MNOO8aDDz7IjBkz6Nmzp9OOIYSz\nyZmFEDVQXl5udQlC1Ak5sxCN0oMPPsif/vQnXnrpJcA+XXZISAgLFy6ksLCQf/7zn6SkpKCU4rrr\nrmPSpEnYbDY2b97Mhg0b6NChA1u2bGH06NEMHz6c1157jRMnTqCUolevXtx77700b96cJUuW8OWX\nX+Lu7o7NZuO2224jIiKChx56iHfeeccxz8/y5cs5cOAA3t7e3HzzzY41l9esWUNaWhqenp7s2LGD\noKAgHnzwQTp06ABAQkICn376KUVFRfj7+3PffffRo0cPy9pVNFyNeiJB0bh5eHhwyy23XNANtXTp\nUnx9fVm8eDElJSXExMQQGBjIqFGjADh8+DCDBw9m+fLllJeXk5WVxS233MLVV19NUVERsbGxvPfe\ne0ydOpVZs2Zx4MCBCt1Q6enpFep45ZVXCAsL47XXXuPkyZPMmzePkJAQunfvDthntn3ssceIjo7m\n3Xff5c0332T+/PmcPHmS9evX88ILLxAQEEB6erqMgwinkW4oIX4jJyeHlJQUpk6dStOmTfH19WXs\n2LFs27bN8Rp/f39uvPFG3Nzc8PT0JCQkhJ49e+Lh4YGPjw9jx45l//79po6XmZnJgQMHuOuuu/D0\n9OTKK69k5MiRjon1ALp06ULfvn2x2Wxce+21HD9+HACbzUZpaSlpaWmOuZZCQkJqtT2E+IWcWQjx\nG5mZmZSXlzN9+nTHNq11hUWygoKCKuyTk5PDihUr+P777ykuLsYwDNMT4WVnZ+Pt7U2zZs0qvP8P\nP/zgeOzr6+v42tPTk9LSUsrLywkJCWHq1Km89957pKWl0atXL6ZMmSLToQunkLAQjdrvF9oJDAzE\n3d2dN954w7EqWWXeeecdAGJjY/H29mbHjh28+eabpvb19/cnPz+foqIiR2BkZmaa/oE/dOhQhg4d\nSmFhIf/4xz/417/+xaxZs0ztK0RVSDeUaNR8fX3JyMhw9PX7+/vTq1cvVq5cSWFhIYZhcPr06ct2\nKxUVFdG0aVO8vLzIysri448/rvC8n5/fBeMUvwgKCuKqq67i3//+N+fPn+fEiRNs2rSJYcOGVVr7\nyZMn+e677ygtLcXT0xNPT89Gv8qccB4JC9GoRUREAHDvvffy1FNPAfDQQw9RVlbGo48+yrRp03j5\n5ZfJzs6+5HtMnDiRY8eO8cc//pEXXniBa665psLzEyZM4IMPPmDq1Kl89NFHF+z/5z//mYyMDGbM\nmMFLL73ExIkTTd2TUVpayr/+9S/uvfde7r//fs6dO8edd95ZlW9fCNPk0lkhhBCVkjMLIYQQlZKw\nEEIIUSkJCyGEEJWSsBBCCFEpCQshhBCVkrAQQghRKQkLIYQQlZKwEEIIUSkJCyGEEJX6/wG3Vkil\nyo892QAAAABJRU5ErkJggg==\n",
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E9aCLCtE7tqLa3YRnoyiM6f9Gf/YBes3b6OSDGPdOsO1ut2c7+sAuyM0BL2/bfhT+QShv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\nCCFEuaRYCCGEKJcUCyGEEOX6fyISDYiaV0LdAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107ba828>"
+       "<matplotlib.figure.Figure at 0x22ce66350b8>"
       ]
      },
      "metadata": {},
@@ -428,6 +400,159 @@
     "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": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Average Return = 19.01\n",
+      "Iteration 2: Average Return = 17.26\n",
+      "Iteration 3: Average Return = 19.88\n",
+      "Iteration 4: Average Return = 21.99\n",
+      "Iteration 5: Average Return = 22.17\n",
+      "Iteration 6: Average Return = 25.72\n",
+      "Iteration 7: Average Return = 23.49\n",
+      "Iteration 8: Average Return = 26.35\n",
+      "Iteration 9: Average Return = 29.59\n",
+      "Iteration 10: Average Return = 30.43\n",
+      "Iteration 11: Average Return = 33.12\n",
+      "Iteration 12: Average Return = 39.41\n",
+      "Iteration 13: Average Return = 33.87\n",
+      "Iteration 14: Average Return = 41.64\n",
+      "Iteration 15: Average Return = 35.97\n",
+      "Iteration 16: Average Return = 39.16\n",
+      "Iteration 17: Average Return = 43.57\n",
+      "Iteration 18: Average Return = 42.11\n",
+      "Iteration 19: Average Return = 40.98\n",
+      "Iteration 20: Average Return = 44.7\n",
+      "Iteration 21: Average Return = 50.5\n",
+      "Iteration 22: Average Return = 49.48\n",
+      "Iteration 23: Average Return = 51.22\n",
+      "Iteration 24: Average Return = 51.44\n",
+      "Iteration 25: Average Return = 50.27\n",
+      "Iteration 26: Average Return = 56.14\n",
+      "Iteration 27: Average Return = 58.16\n",
+      "Iteration 28: Average Return = 52.85\n",
+      "Iteration 29: Average Return = 60.39\n",
+      "Iteration 30: Average Return = 57.33\n",
+      "Iteration 31: Average Return = 59.64\n",
+      "Iteration 32: Average Return = 61.09\n",
+      "Iteration 33: Average Return = 56.73\n",
+      "Iteration 34: Average Return = 60.95\n",
+      "Iteration 35: Average Return = 64.53\n",
+      "Iteration 36: Average Return = 62.29\n",
+      "Iteration 37: Average Return = 62.38\n",
+      "Iteration 38: Average Return = 68.92\n",
+      "Iteration 39: Average Return = 65.49\n",
+      "Iteration 40: Average Return = 72.31\n",
+      "Iteration 41: Average Return = 70.46\n",
+      "Iteration 42: Average Return = 75.13\n",
+      "Iteration 43: Average Return = 76.05\n",
+      "Iteration 44: Average Return = 74.29\n",
+      "Iteration 45: Average Return = 76.9\n",
+      "Iteration 46: Average Return = 79.63\n",
+      "Iteration 47: Average Return = 84.12\n",
+      "Iteration 48: Average Return = 81.36\n",
+      "Iteration 49: Average Return = 83.62\n",
+      "Iteration 50: Average Return = 88.09\n",
+      "Iteration 51: Average Return = 99.47\n",
+      "Iteration 52: Average Return = 94.7\n",
+      "Iteration 53: Average Return = 105.75\n",
+      "Iteration 54: Average Return = 115.27\n",
+      "Iteration 55: Average Return = 125.27\n",
+      "Iteration 56: Average Return = 131.91\n",
+      "Iteration 57: Average Return = 142.35\n",
+      "Iteration 58: Average Return = 152.48\n",
+      "Iteration 59: Average Return = 155.68\n",
+      "Iteration 60: Average Return = 153.77\n",
+      "Iteration 61: Average Return = 166.94\n",
+      "Iteration 62: Average Return = 165.18\n",
+      "Iteration 63: Average Return = 154.73\n",
+      "Iteration 64: Average Return = 161.18\n",
+      "Iteration 65: Average Return = 166.77\n",
+      "Iteration 66: Average Return = 160.53\n",
+      "Iteration 67: Average Return = 169.21\n",
+      "Iteration 68: Average Return = 173.57\n",
+      "Iteration 69: Average Return = 178.88\n",
+      "Iteration 70: Average Return = 179.22\n",
+      "Iteration 71: Average Return = 175.59\n",
+      "Iteration 72: Average Return = 176.31\n",
+      "Iteration 73: Average Return = 178.63\n",
+      "Iteration 74: Average Return = 187.14\n",
+      "Iteration 75: Average Return = 181.99\n",
+      "Iteration 76: Average Return = 189.63\n",
+      "Iteration 77: Average Return = 185.32\n",
+      "Iteration 78: Average Return = 186.06\n",
+      "Iteration 79: Average Return = 188.13\n",
+      "Iteration 80: Average Return = 183.85\n",
+      "Iteration 81: Average Return = 185.33\n",
+      "Iteration 82: Average Return = 190.44\n",
+      "Iteration 83: Average Return = 188.63\n",
+      "Iteration 84: Average Return = 188.7\n",
+      "Iteration 85: Average Return = 189.96\n",
+      "Iteration 86: Average Return = 191.3\n",
+      "Iteration 87: Average Return = 186.0\n",
+      "Iteration 88: Average Return = 192.34\n",
+      "Iteration 89: Average Return = 190.13\n",
+      "Iteration 90: Average Return = 189.6\n",
+      "Iteration 91: Average Return = 187.1\n",
+      "Iteration 92: Average Return = 190.71\n",
+      "Iteration 93: Average Return = 191.69\n",
+      "Iteration 94: Average Return = 192.09\n",
+      "Iteration 95: Average Return = 190.5\n",
+      "Iteration 96: Average Return = 194.41\n",
+      "Iteration 97: Average Return = 193.88\n",
+      "Iteration 98: Average Return = 191.17\n",
+      "Iteration 99: Average Return = 195.34\n",
+      "Solve at 99 iterations, which equals 9900 episodes.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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GpvB7ixbB/s5nGrRu/+Tm5gZ8XSYm3GuFhYVob2+HyWSCzWZDfX09SkpK3MaU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\naGIS2H3/4Zzvu2+C76kCjjcCxdeLeE7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QBEEQEYNEhyAIgogYJDoEQRBExCDRIQiCICIG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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22ce9144518>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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F6Y0fQb36qIERZe6hvH0xxk2SV1BOTJKFEHWYNk04dgh1UX/Fb5SrG7TthE78EX0uA53w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\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x22ceb2a50f0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "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()\n",
+    "\n",
+    "util.plot_curve(loss_list, \"loss\")\n",
+    "util.plot_curve(avg_return_list, \"average return\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -477,14 +602,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 30,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "# set the hyperparameter for generalized advantage estimation (GAE)\n",
-    "LAMBDA = 0.98 # \\lambda\n",
+    "#LAMBDA = 0.98 # \\lambda\n",
+    "LAMBDA = 0.95\n",
     "class PolicyOptimizer_actor_critic(PolicyOptimizer):\n",
     "    def __init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n",
     "        discount_rate=.99):\n",
@@ -523,6 +649,8 @@
     "            Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n",
     "            \"\"\"\n",
     "            # YOUR CODE HERE >>>>>>>>\n",
+    "            #print(util.discount(np.log(np.multiply(np.ones(10000), self.discount_rate * LAMBDA)), 1))\n",
+    "            a = util.discount(a, self.discount_rate * LAMBDA)\n",
     "            # <<<<<<<\n",
     "            p[\"returns\"] = target_v\n",
     "            p[\"baselines\"] = b\n",
@@ -543,99 +671,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 33,
    "metadata": {
-    "scrolled": true
+    "collapsed": false,
+    "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 = 24.98\n",
+      "Iteration 2: Average Return = 29.41\n",
+      "Iteration 3: Average Return = 31.71\n",
+      "Iteration 4: Average Return = 30.51\n",
+      "Iteration 5: Average Return = 34.77\n",
+      "Iteration 6: Average Return = 38.54\n",
+      "Iteration 7: Average Return = 37.77\n",
+      "Iteration 8: Average Return = 37.87\n",
+      "Iteration 9: Average Return = 40.96\n",
+      "Iteration 10: Average Return = 44.97\n",
+      "Iteration 11: Average Return = 45.46\n",
+      "Iteration 12: Average Return = 44.93\n",
+      "Iteration 13: Average Return = 45.97\n",
+      "Iteration 14: Average Return = 44.72\n",
+      "Iteration 15: Average Return = 49.52\n",
+      "Iteration 16: Average Return = 53.87\n",
+      "Iteration 17: Average Return = 52.84\n",
+      "Iteration 18: Average Return = 52.65\n",
+      "Iteration 19: Average Return = 56.58\n",
+      "Iteration 20: Average Return = 54.04\n",
+      "Iteration 21: Average Return = 56.78\n",
+      "Iteration 22: Average Return = 61.62\n",
+      "Iteration 23: Average Return = 62.86\n",
+      "Iteration 24: Average Return = 63.36\n",
+      "Iteration 25: Average Return = 64.5\n",
+      "Iteration 26: Average Return = 68.53\n",
+      "Iteration 27: Average Return = 68.72\n",
+      "Iteration 28: Average Return = 71.13\n",
+      "Iteration 29: Average Return = 73.12\n",
+      "Iteration 30: Average Return = 77.93\n",
+      "Iteration 31: Average Return = 82.36\n",
+      "Iteration 32: Average Return = 85.47\n",
+      "Iteration 33: Average Return = 88.01\n",
+      "Iteration 34: Average Return = 109.07\n",
+      "Iteration 35: Average Return = 113.3\n",
+      "Iteration 36: Average Return = 126.71\n",
+      "Iteration 37: Average Return = 133.74\n",
+      "Iteration 38: Average Return = 138.0\n",
+      "Iteration 39: Average Return = 156.24\n",
+      "Iteration 40: Average Return = 158.01\n",
+      "Iteration 41: Average Return = 152.1\n",
+      "Iteration 42: Average Return = 159.29\n",
+      "Iteration 43: Average Return = 164.76\n",
+      "Iteration 44: Average Return = 158.42\n",
+      "Iteration 45: Average Return = 167.66\n",
+      "Iteration 46: Average Return = 169.72\n",
+      "Iteration 47: Average Return = 172.65\n",
+      "Iteration 48: Average Return = 173.46\n",
+      "Iteration 49: Average Return = 181.0\n",
+      "Iteration 50: Average Return = 182.64\n",
+      "Iteration 51: Average Return = 184.82\n",
+      "Iteration 52: Average Return = 189.58\n",
+      "Iteration 53: Average Return = 190.29\n",
+      "Iteration 54: Average Return = 194.92\n",
+      "Iteration 55: Average Return = 192.96\n",
+      "Iteration 56: Average Return = 191.9\n",
+      "Iteration 57: Average Return = 195.42\n",
+      "Solve at 57 iterations, which equals 5700 episodes.\n"
      ]
     }
    ],
@@ -658,14 +761,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 34,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": 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NnnIp+h+voj98xxgdFmnB9It7UAHNf2mdyjTlMpx5X6H/9TZE90N1YIkjFR6J6Z7Fxl/w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\nDTX+jM4LWgjRo/i85iLaToVFoC66Cv3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+fTo/+9nPcDqd7uSS1dXVxMbGBq1y4ULFxJnZkZsbwY/cS+IS02hmsdA1YRhc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\nS5eyZMkS945rq9VKWloa69evp6WlhV/84hfs3r0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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1278b4d30>"
+       "<matplotlib.figure.Figure at 0x22cec690748>"
       ]
      },
      "metadata": {},
@@ -673,9 +778,9 @@
     },
     {
      "data": {
-      "image/png": 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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n",
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0dn68QngHp4rFwoULKSsrIz4+Hn9//8u+yZNPPlnpWL9+/S56/tChQxk6dOhl30cI4Vo6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\niCpJsRBCCFElKRZCCCGqJMVCCCFElf4fdLYnnWbZuqsAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x127a02390>"
+       "<matplotlib.figure.Figure at 0x22ced7a6550>"
       ]
      },
      "metadata": {},
@@ -713,7 +818,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.6.0"
   }
  },
  "nbformat": 4,
diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py
index 99fecf3..98400d1 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 >>>>>>
+        hidden_layer = tf.contrib.layers.fully_connected(inputs = self._observations, num_outputs = hidden_dim, activation_fn = tf.tanh)
+        probs = tf.contrib.layers.fully_connected(inputs = hidden_layer, num_outputs = out_dim, activation_fn = 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(tf.multiply(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..a4a015a 100644
--- a/policy_gradient/util.py
+++ b/policy_gradient/util.py
@@ -32,6 +32,9 @@ def discount_bootstrap(x, discount_rate, b):
     Sample code should be about 3 lines
     """
     # YOUR CODE >>>>>>>>>>>>>>>>>>>
+    #print(b)
+    #print(np.append(b[1:], 0))
+    return np.add(x, np.multiply(np.append(b[1:], 0), discount_rate))
     # <<<<<<<<<<<<<<<<<<<<<<<<<<<<
  
 def plot_curve(data, key, filename=None):
diff --git a/report.md b/report.md
index 1e5017e..9f5e559 100644
--- a/report.md
+++ b/report.md
@@ -1,3 +1,30 @@
 # Homework3-Policy-Gradient report
+105062575 何元通  
 
-TA: try to elaborate the algorithms that you implemented and any details worth mentioned.
+## Introduction
+
+&nbsp; &nbsp; &nbsp; &nbsp; In this assignment, we are asked to perform policy gradient to solve the cartpole problem. We will build a neural network to learn the parameters. And, the agent is able to select better actions without consulting a value function.
+
+## Implementation
+
+&nbsp; &nbsp; &nbsp; &nbsp; When implementing the functionality, what we should do is to finish the functions. All functions have been given without several critical lines of codes. Consequently, we simply need to fill in the correct codes in specific position.   
+  
+&nbsp; &nbsp; &nbsp; &nbsp; For the problem 1, we have to build a two-layered fully-connected neural network. I directly use the function from tensorflow package. The function is capable of building a fully-connected layer. Thus, I just call the function twice and decides its input, output dimension, and activation function. Eventually, the network will give us outputs of probability for action selection.  
+  
+&nbsp; &nbsp; &nbsp; &nbsp; Then, finish the loss function of the network for problem 2. I follow the loss function provided in .ipynb file. In addition, the accumulated discounted rewards have been provided and the action probabilities have been computed before as well. As a consequence, following with the formula, I add the reward vector and probability vector elementwisly and compute the mean of the addition one. It should be notice that since we would like to maximize the function, we have to multiply it with negative one.  
+  
+&nbsp; &nbsp; &nbsp; &nbsp; I merely follow the formulas in problem 3, that changes the loss function. Different from the original one, we substract the baseline from the rewards. And, it is similar to problem 3, I just copy all of the codes in problem 3 to problem 4 and not substract the baseline. With running results, it is much convenient for comparison.  
+  
+&nbsp; &nbsp; &nbsp; &nbsp; We are asked to complete a function in policy.py in problem 5. Following the hint and formulas from both .py and .ipynb files, I orinially simply multiply the baseline with discount vactor and add it with the immediate rewards elementwisly. Nonetheless, it is noticed that we should first remove the first element of the baseline and append a zero to it. As we have to give it a space for the next state and keep the size of the array. And, in problem 6, I use the discount function to compute the generalized advantage estimation with given parameters.  
+
+## Discussion
+
+&nbsp; &nbsp; &nbsp; &nbsp; For problem 3 and for problem 4, the main difference is the former's reward substract a baseline, but the later's is not. According to the results from the two parts, the one with baseline can converge quicklier than the one without.(As shown at the following two plots. The former is of problem 3 and the later is of problem4.) From the codes, the baseline is a value function. When we add the value function into the function, it will not influence the expectation of the function. Thus, it can be added for a faster convergence. As to why can it converge faster, I consider that because we would like to maximize the function, it performs the same as minimizing the function with the negative rewards. And, the value function stand for the predicted reward for next few states. So, if we substract the value from the reward, it is similar to maximize the target function and the target function can inffer from some future states. Contrary to the problem 4, which does not use baseline, the value function makes it precisely on selection of the policy and makes it be able to converge more quicklier.
+
+<img src="CEDL_3_pic/p3_1.JPG" width="400" height="200" />&nbsp;<img src="CEDL_3_pic/p4_1.JPG" width="400" height="200" />
+  
+&nbsp; &nbsp; &nbsp; &nbsp; As to problem 5 and problem 6, it introduces a hyperparameter lamda to improve the original policy gradient. From the discussion of my classmate and I and the data searched from Internet, we consider this change will improve the original policy gradient. It seems to update the values for each step other than updating for each round. Thus, we assume that the higher frequency it updates, the faster it converges. And, the lamda also influence the steps it considers. This may effect its speed of convergence. Moreover, as showed in the following two images. The former is the plot of problem 3 and the later is the plot of problem 6. The change of loss in problem 6 is stabler than the one in problem 3. There is much less sharp promotion or sharp drop in problem 6 contrary to problem 3. I consider this may also caused by the improvement since updating for each step can help it find a better value in local small area.  
+  
+<img src="CEDL_3_pic/p3_1.JPG" width="400" height="200" />&nbsp;<img src="CEDL_3_pic/p6_1.JPG" width="400" height="200" />  
+  
+&nbsp; &nbsp; &nbsp; &nbsp;By the way, we have also found the disadvange of it. From the data from the Internet, it tells us this algorithm is hard to converge. Of course, this happens on us that we should re-run it for several times to satisfy the request of around 80 iteration. Thus, I think this may be a crucial challenge to improve it.