diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb
index 4529e50..9e14d84 100644
--- a/Lab3-policy-gradient.ipynb
+++ b/Lab3-policy-gradient.ipynb
@@ -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": true
+   },
+   "outputs": [],
    "source": [
     "import gym\n",
     "import tensorflow as tf\n",
@@ -103,14 +97,14 @@
   },
   {
    "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",
+      "/home/anjie/anaconda3/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",
       "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
      ]
     }
@@ -214,6 +208,7 @@
     "            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 +253,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 6,
    "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 = 23.02\n",
+      "Iteration 2: Average Return = 23.96\n",
+      "Iteration 3: Average Return = 23.3\n",
+      "Iteration 4: Average Return = 25.42\n",
+      "Iteration 5: Average Return = 26.16\n",
+      "Iteration 6: Average Return = 30.4\n",
+      "Iteration 7: Average Return = 31.46\n",
+      "Iteration 8: Average Return = 31.78\n",
+      "Iteration 9: Average Return = 35.81\n",
+      "Iteration 10: Average Return = 34.07\n",
+      "Iteration 11: Average Return = 38.42\n",
+      "Iteration 12: Average Return = 39.99\n",
+      "Iteration 13: Average Return = 43.81\n",
+      "Iteration 14: Average Return = 41.9\n",
+      "Iteration 15: Average Return = 45.51\n",
+      "Iteration 16: Average Return = 48.5\n",
+      "Iteration 17: Average Return = 43.26\n",
+      "Iteration 18: Average Return = 47.94\n",
+      "Iteration 19: Average Return = 48.15\n",
+      "Iteration 20: Average Return = 52.89\n",
+      "Iteration 21: Average Return = 52.56\n",
+      "Iteration 22: Average Return = 50.18\n",
+      "Iteration 23: Average Return = 58.47\n",
+      "Iteration 24: Average Return = 62.32\n",
+      "Iteration 25: Average Return = 53.08\n",
+      "Iteration 26: Average Return = 56.62\n",
+      "Iteration 27: Average Return = 59.29\n",
+      "Iteration 28: Average Return = 65.93\n",
+      "Iteration 29: Average Return = 63.26\n",
+      "Iteration 30: Average Return = 67.14\n",
+      "Iteration 31: Average Return = 64.87\n",
+      "Iteration 32: Average Return = 73.27\n",
+      "Iteration 33: Average Return = 73.41\n",
+      "Iteration 34: Average Return = 73.19\n",
+      "Iteration 35: Average Return = 83.89\n",
+      "Iteration 36: Average Return = 85.32\n",
+      "Iteration 37: Average Return = 90.25\n",
+      "Iteration 38: Average Return = 92.49\n",
+      "Iteration 39: Average Return = 99.85\n",
+      "Iteration 40: Average Return = 116.93\n",
+      "Iteration 41: Average Return = 119.93\n",
+      "Iteration 42: Average Return = 118.09\n",
+      "Iteration 43: Average Return = 127.55\n",
+      "Iteration 44: Average Return = 120.04\n",
+      "Iteration 45: Average Return = 124.38\n",
+      "Iteration 46: Average Return = 123.53\n",
+      "Iteration 47: Average Return = 141.95\n",
+      "Iteration 48: Average Return = 145.59\n",
+      "Iteration 49: Average Return = 144.63\n",
+      "Iteration 50: Average Return = 144.98\n",
+      "Iteration 51: Average Return = 165.47\n",
+      "Iteration 52: Average Return = 163.97\n",
+      "Iteration 53: Average Return = 162.38\n",
+      "Iteration 54: Average Return = 166.69\n",
+      "Iteration 55: Average Return = 163.58\n",
+      "Iteration 56: Average Return = 165.28\n",
+      "Iteration 57: Average Return = 168.51\n",
+      "Iteration 58: Average Return = 174.09\n",
+      "Iteration 59: Average Return = 176.56\n",
+      "Iteration 60: Average Return = 182.93\n",
+      "Iteration 61: Average Return = 181.46\n",
+      "Iteration 62: Average Return = 177.08\n",
+      "Iteration 63: Average Return = 183.54\n",
+      "Iteration 64: Average Return = 181.86\n",
+      "Iteration 65: Average Return = 183.21\n",
+      "Iteration 66: Average Return = 184.06\n",
+      "Iteration 67: Average Return = 182.35\n",
+      "Iteration 68: Average Return = 189.33\n",
+      "Iteration 69: Average Return = 191.96\n",
+      "Iteration 70: Average Return = 189.36\n",
+      "Iteration 71: Average Return = 187.51\n",
+      "Iteration 72: Average Return = 193.92\n",
+      "Iteration 73: Average Return = 192.94\n",
+      "Iteration 74: Average Return = 190.02\n",
+      "Iteration 75: Average Return = 189.59\n",
+      "Iteration 76: Average Return = 190.69\n",
+      "Iteration 77: Average Return = 194.18\n",
+      "Iteration 78: Average Return = 190.52\n",
+      "Iteration 79: Average Return = 193.96\n",
+      "Iteration 80: Average Return = 195.58\n",
+      "Solve at 80 iterations, which equals 8000 episodes.\n"
      ]
     }
    ],
@@ -371,14 +362,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUncKFxERqbtvCSJnLLKDx1IAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107bac18>"
+       "<matplotlib.figure.Figure at 0x7f5fef32d898>"
       ]
      },
      "metadata": {},
@@ -386,9 +377,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyo892QAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nWlIshBBCVOv/A8HgdJeiIxDZAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107ba828>"
+       "<matplotlib.figure.Figure at 0x7f5fef32d860>"
       ]
      },
      "metadata": {},
@@ -428,6 +419,139 @@
     "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": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Average Return = 26.1\n",
+      "Iteration 2: Average Return = 28.68\n",
+      "Iteration 3: Average Return = 31.64\n",
+      "Iteration 4: Average Return = 36.36\n",
+      "Iteration 5: Average Return = 38.44\n",
+      "Iteration 6: Average Return = 38.21\n",
+      "Iteration 7: Average Return = 43.47\n",
+      "Iteration 8: Average Return = 45.46\n",
+      "Iteration 9: Average Return = 50.92\n",
+      "Iteration 10: Average Return = 55.26\n",
+      "Iteration 11: Average Return = 56.64\n",
+      "Iteration 12: Average Return = 65.01\n",
+      "Iteration 13: Average Return = 67.85\n",
+      "Iteration 14: Average Return = 67.74\n",
+      "Iteration 15: Average Return = 71.5\n",
+      "Iteration 16: Average Return = 79.61\n",
+      "Iteration 17: Average Return = 76.91\n",
+      "Iteration 18: Average Return = 80.42\n",
+      "Iteration 19: Average Return = 106.04\n",
+      "Iteration 20: Average Return = 95.93\n",
+      "Iteration 21: Average Return = 93.24\n",
+      "Iteration 22: Average Return = 110.01\n",
+      "Iteration 23: Average Return = 105.6\n",
+      "Iteration 24: Average Return = 117.98\n",
+      "Iteration 25: Average Return = 118.29\n",
+      "Iteration 26: Average Return = 125.83\n",
+      "Iteration 27: Average Return = 123.25\n",
+      "Iteration 28: Average Return = 125.55\n",
+      "Iteration 29: Average Return = 139.84\n",
+      "Iteration 30: Average Return = 141.6\n",
+      "Iteration 31: Average Return = 141.26\n",
+      "Iteration 32: Average Return = 146.04\n",
+      "Iteration 33: Average Return = 152.26\n",
+      "Iteration 34: Average Return = 150.66\n",
+      "Iteration 35: Average Return = 150.46\n",
+      "Iteration 36: Average Return = 156.69\n",
+      "Iteration 37: Average Return = 164.12\n",
+      "Iteration 38: Average Return = 168.71\n",
+      "Iteration 39: Average Return = 154.21\n",
+      "Iteration 40: Average Return = 174.28\n",
+      "Iteration 41: Average Return = 165.04\n",
+      "Iteration 42: Average Return = 171.54\n",
+      "Iteration 43: Average Return = 169.25\n",
+      "Iteration 44: Average Return = 178.1\n",
+      "Iteration 45: Average Return = 173.15\n",
+      "Iteration 46: Average Return = 175.99\n",
+      "Iteration 47: Average Return = 180.91\n",
+      "Iteration 48: Average Return = 179.32\n",
+      "Iteration 49: Average Return = 189.3\n",
+      "Iteration 50: Average Return = 179.85\n",
+      "Iteration 51: Average Return = 180.3\n",
+      "Iteration 52: Average Return = 188.91\n",
+      "Iteration 53: Average Return = 188.98\n",
+      "Iteration 54: Average Return = 187.98\n",
+      "Iteration 55: Average Return = 186.22\n",
+      "Iteration 56: Average Return = 185.88\n",
+      "Iteration 57: Average Return = 189.3\n",
+      "Iteration 58: Average Return = 191.71\n",
+      "Iteration 59: Average Return = 192.4\n",
+      "Iteration 60: Average Return = 188.38\n",
+      "Iteration 61: Average Return = 191.95\n",
+      "Iteration 62: Average Return = 190.09\n",
+      "Iteration 63: Average Return = 189.46\n",
+      "Iteration 64: Average Return = 189.36\n",
+      "Iteration 65: Average Return = 191.88\n",
+      "Iteration 66: Average Return = 188.95\n",
+      "Iteration 67: Average Return = 192.17\n",
+      "Iteration 68: Average Return = 192.79\n",
+      "Iteration 69: Average Return = 193.23\n",
+      "Iteration 70: Average Return = 193.66\n",
+      "Iteration 71: Average Return = 191.78\n",
+      "Iteration 72: Average Return = 194.28\n",
+      "Iteration 73: Average Return = 195.77\n",
+      "Solve at 73 iterations, which equals 7300 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": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAENCAYAAADUubjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRNxI0hFCCBE3knSEEELEzf8Dqm4wHG/3Iz8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5fee68add8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5fee6e3dd8>"
+      ]
+     },
+     "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": {},
@@ -453,6 +577,225 @@
     "If you answer is right, your will solve CartPole with roughly ~ 80 iterations."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# set the hyperparameter for generalized advantage estimation (GAE)\n",
+    "LAMBDA = 0.98 # \\lambda\n",
+    "class PolicyOptimizer_actor_critic(PolicyOptimizer):\n",
+    "    def __init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n",
+    "        discount_rate=.99):\n",
+    "        PolicyOptimizer.__init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n",
+    "            discount_rate=.99)\n",
+    "    \n",
+    "    def process_paths(self, paths):\n",
+    "        for p in paths:\n",
+    "            if self.baseline != None:\n",
+    "                b = self.baseline.predict(p)\n",
+    "                b[-1] = 0 # terminal state\n",
+    "            else:\n",
+    "                b = 0\n",
+    "            \n",
+    "            \"\"\"\n",
+    "            1. Variable `b` is the reward predicted by our baseline\n",
+    "            2. Calculate the advantage function via one-step bootstrap\n",
+    "                    A(s, a) = [r(s,a,s') + \\gamma*v(s')] - v(s)\n",
+    "            3. `target_v` specifies the target of the baseline function\n",
+    "            \"\"\"\n",
+    "            r = util.discount_bootstrap(p[\"rewards\"], self.discount_rate, b)\n",
+    "            target_v = util.discount_cumsum(p[\"rewards\"], self.discount_rate)\n",
+    "            a = r - b\n",
+    "            \n",
+    "            p[\"returns\"] = target_v\n",
+    "            p[\"baselines\"] = b\n",
+    "            p[\"advantages\"] = (a - a.mean()) / (a.std() + 1e-8) # normalize\n",
+    "\n",
+    "        obs = np.concatenate([ p[\"observations\"] for p in paths ])\n",
+    "        actions = np.concatenate([ p[\"actions\"] for p in paths ])\n",
+    "        rewards = np.concatenate([ p[\"rewards\"] for p in paths ])\n",
+    "        advantages = np.concatenate([ p[\"advantages\"] for p in paths ])\n",
+    "\n",
+    "        return dict(\n",
+    "            observations=obs,\n",
+    "            actions=actions,\n",
+    "            rewards=rewards,\n",
+    "            advantages=advantages,\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Average Return = 19.26\n",
+      "Iteration 2: Average Return = 19.0\n",
+      "Iteration 3: Average Return = 20.61\n",
+      "Iteration 4: Average Return = 21.16\n",
+      "Iteration 5: Average Return = 23.18\n",
+      "Iteration 6: Average Return = 26.47\n",
+      "Iteration 7: Average Return = 25.54\n",
+      "Iteration 8: Average Return = 27.3\n",
+      "Iteration 9: Average Return = 28.79\n",
+      "Iteration 10: Average Return = 31.26\n",
+      "Iteration 11: Average Return = 35.2\n",
+      "Iteration 12: Average Return = 34.03\n",
+      "Iteration 13: Average Return = 34.78\n",
+      "Iteration 14: Average Return = 36.76\n",
+      "Iteration 15: Average Return = 36.06\n",
+      "Iteration 16: Average Return = 39.78\n",
+      "Iteration 17: Average Return = 39.89\n",
+      "Iteration 18: Average Return = 46.0\n",
+      "Iteration 19: Average Return = 40.1\n",
+      "Iteration 20: Average Return = 42.5\n",
+      "Iteration 21: Average Return = 44.58\n",
+      "Iteration 22: Average Return = 46.97\n",
+      "Iteration 23: Average Return = 43.54\n",
+      "Iteration 24: Average Return = 46.47\n",
+      "Iteration 25: Average Return = 47.68\n",
+      "Iteration 26: Average Return = 50.25\n",
+      "Iteration 27: Average Return = 49.23\n",
+      "Iteration 28: Average Return = 48.78\n",
+      "Iteration 29: Average Return = 47.92\n",
+      "Iteration 30: Average Return = 49.72\n",
+      "Iteration 31: Average Return = 49.3\n",
+      "Iteration 32: Average Return = 51.64\n",
+      "Iteration 33: Average Return = 49.14\n",
+      "Iteration 34: Average Return = 52.92\n",
+      "Iteration 35: Average Return = 51.14\n",
+      "Iteration 36: Average Return = 54.7\n",
+      "Iteration 37: Average Return = 57.42\n",
+      "Iteration 38: Average Return = 50.94\n",
+      "Iteration 39: Average Return = 52.51\n",
+      "Iteration 40: Average Return = 54.94\n",
+      "Iteration 41: Average Return = 58.1\n",
+      "Iteration 42: Average Return = 51.95\n",
+      "Iteration 43: Average Return = 58.8\n",
+      "Iteration 44: Average Return = 57.96\n",
+      "Iteration 45: Average Return = 57.5\n",
+      "Iteration 46: Average Return = 58.54\n",
+      "Iteration 47: Average Return = 54.82\n",
+      "Iteration 48: Average Return = 54.24\n",
+      "Iteration 49: Average Return = 59.35\n",
+      "Iteration 50: Average Return = 58.65\n",
+      "Iteration 51: Average Return = 59.99\n",
+      "Iteration 52: Average Return = 61.51\n",
+      "Iteration 53: Average Return = 58.73\n",
+      "Iteration 54: Average Return = 62.7\n",
+      "Iteration 55: Average Return = 57.76\n",
+      "Iteration 56: Average Return = 64.79\n",
+      "Iteration 57: Average Return = 60.5\n",
+      "Iteration 58: Average Return = 64.41\n",
+      "Iteration 59: Average Return = 68.62\n",
+      "Iteration 60: Average Return = 63.43\n",
+      "Iteration 61: Average Return = 63.57\n",
+      "Iteration 62: Average Return = 69.89\n",
+      "Iteration 63: Average Return = 67.95\n",
+      "Iteration 64: Average Return = 68.19\n",
+      "Iteration 65: Average Return = 69.92\n",
+      "Iteration 66: Average Return = 67.62\n",
+      "Iteration 67: Average Return = 72.27\n",
+      "Iteration 68: Average Return = 74.44\n",
+      "Iteration 69: Average Return = 76.3\n",
+      "Iteration 70: Average Return = 77.04\n",
+      "Iteration 71: Average Return = 70.98\n",
+      "Iteration 72: Average Return = 74.9\n",
+      "Iteration 73: Average Return = 74.99\n",
+      "Iteration 74: Average Return = 79.64\n",
+      "Iteration 75: Average Return = 84.0\n",
+      "Iteration 76: Average Return = 84.78\n",
+      "Iteration 77: Average Return = 80.9\n",
+      "Iteration 78: Average Return = 87.47\n",
+      "Iteration 79: Average Return = 92.22\n",
+      "Iteration 80: Average Return = 100.7\n",
+      "Iteration 81: Average Return = 104.11\n",
+      "Iteration 82: Average Return = 107.5\n",
+      "Iteration 83: Average Return = 112.73\n",
+      "Iteration 84: Average Return = 118.82\n",
+      "Iteration 85: Average Return = 132.46\n",
+      "Iteration 86: Average Return = 133.6\n",
+      "Iteration 87: Average Return = 148.75\n",
+      "Iteration 88: Average Return = 149.98\n",
+      "Iteration 89: Average Return = 154.15\n",
+      "Iteration 90: Average Return = 157.76\n",
+      "Iteration 91: Average Return = 154.33\n",
+      "Iteration 92: Average Return = 154.39\n",
+      "Iteration 93: Average Return = 157.81\n",
+      "Iteration 94: Average Return = 156.77\n",
+      "Iteration 95: Average Return = 159.61\n",
+      "Iteration 96: Average Return = 157.34\n",
+      "Iteration 97: Average Return = 168.61\n",
+      "Iteration 98: Average Return = 161.92\n",
+      "Iteration 99: Average Return = 172.29\n",
+      "Iteration 100: Average Return = 174.62\n",
+      "Iteration 101: Average Return = 173.58\n",
+      "Iteration 102: Average Return = 178.55\n",
+      "Iteration 103: Average Return = 180.57\n",
+      "Iteration 104: Average Return = 189.68\n",
+      "Iteration 105: Average Return = 188.96\n",
+      "Iteration 106: Average Return = 191.17\n",
+      "Iteration 107: Average Return = 193.55\n",
+      "Iteration 108: Average Return = 195.91\n",
+      "Solve at 108 iterations, which equals 10800 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 = LinearFeatureBaseline(env.spec) \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": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n4SKEECLufgAcW1WD4N92ngAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5fec9a2cf8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nFAshhBAV+n9u4m85J1dyrAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f5fec9a2240>"
+      ]
+     },
+     "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": {},
@@ -477,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -523,6 +866,7 @@
     "            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",
@@ -543,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {
     "scrolled": true
    },
@@ -552,90 +896,184 @@
      "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 = 18.82\n",
+      "Iteration 2: Average Return = 20.46\n",
+      "Iteration 3: Average Return = 20.94\n",
+      "Iteration 4: Average Return = 20.68\n",
+      "Iteration 5: Average Return = 24.44\n",
+      "Iteration 6: Average Return = 24.7\n",
+      "Iteration 7: Average Return = 26.52\n",
+      "Iteration 8: Average Return = 26.62\n",
+      "Iteration 9: Average Return = 30.28\n",
+      "Iteration 10: Average Return = 32.26\n",
+      "Iteration 11: Average Return = 34.24\n",
+      "Iteration 12: Average Return = 33.56\n",
+      "Iteration 13: Average Return = 36.47\n",
+      "Iteration 14: Average Return = 39.82\n",
+      "Iteration 15: Average Return = 38.08\n",
+      "Iteration 16: Average Return = 40.75\n",
+      "Iteration 17: Average Return = 45.29\n",
+      "Iteration 18: Average Return = 46.37\n",
+      "Iteration 19: Average Return = 46.97\n",
+      "Iteration 20: Average Return = 48.76\n",
+      "Iteration 21: Average Return = 48.9\n",
+      "Iteration 22: Average Return = 48.46\n",
+      "Iteration 23: Average Return = 47.52\n",
+      "Iteration 24: Average Return = 48.14\n",
+      "Iteration 25: Average Return = 52.07\n",
+      "Iteration 26: Average Return = 50.37\n",
+      "Iteration 27: Average Return = 51.86\n",
+      "Iteration 28: Average Return = 52.77\n",
+      "Iteration 29: Average Return = 54.83\n",
+      "Iteration 30: Average Return = 55.18\n",
+      "Iteration 31: Average Return = 56.71\n",
+      "Iteration 32: Average Return = 60.94\n",
+      "Iteration 33: Average Return = 60.89\n",
+      "Iteration 34: Average Return = 59.71\n",
+      "Iteration 35: Average Return = 68.57\n",
+      "Iteration 36: Average Return = 65.16\n",
+      "Iteration 37: Average Return = 70.96\n",
+      "Iteration 38: Average Return = 69.16\n",
+      "Iteration 39: Average Return = 70.81\n",
+      "Iteration 40: Average Return = 79.71\n",
+      "Iteration 41: Average Return = 84.33\n",
+      "Iteration 42: Average Return = 90.04\n",
+      "Iteration 43: Average Return = 84.57\n",
+      "Iteration 44: Average Return = 83.18\n",
+      "Iteration 45: Average Return = 102.23\n",
+      "Iteration 46: Average Return = 98.87\n",
+      "Iteration 47: Average Return = 102.66\n",
+      "Iteration 48: Average Return = 107.39\n",
+      "Iteration 49: Average Return = 105.16\n",
+      "Iteration 50: Average Return = 111.83\n",
+      "Iteration 51: Average Return = 108.32\n",
+      "Iteration 52: Average Return = 114.33\n",
+      "Iteration 53: Average Return = 115.77\n",
+      "Iteration 54: Average Return = 119.42\n",
+      "Iteration 55: Average Return = 116.63\n",
+      "Iteration 56: Average Return = 122.64\n",
+      "Iteration 57: Average Return = 123.8\n",
+      "Iteration 58: Average Return = 132.13\n",
+      "Iteration 59: Average Return = 132.66\n",
+      "Iteration 60: Average Return = 136.09\n",
+      "Iteration 61: Average Return = 138.67\n",
+      "Iteration 62: Average Return = 139.61\n",
+      "Iteration 63: Average Return = 142.17\n",
+      "Iteration 64: Average Return = 142.72\n",
+      "Iteration 65: Average Return = 147.83\n",
+      "Iteration 66: Average Return = 154.65\n",
+      "Iteration 67: Average Return = 160.01\n",
+      "Iteration 68: Average Return = 161.56\n",
+      "Iteration 69: Average Return = 171.01\n",
+      "Iteration 70: Average Return = 167.06\n",
+      "Iteration 71: Average Return = 168.64\n",
+      "Iteration 72: Average Return = 175.58\n",
+      "Iteration 73: Average Return = 175.31\n",
+      "Iteration 74: Average Return = 178.66\n",
+      "Iteration 75: Average Return = 173.94\n",
+      "Iteration 76: Average Return = 172.89\n",
+      "Iteration 77: Average Return = 179.22\n",
+      "Iteration 78: Average Return = 176.39\n",
+      "Iteration 79: Average Return = 174.61\n",
+      "Iteration 80: Average Return = 180.04\n",
+      "Iteration 81: Average Return = 171.26\n",
+      "Iteration 82: Average Return = 183.48\n",
+      "Iteration 83: Average Return = 174.01\n",
+      "Iteration 84: Average Return = 178.26\n",
+      "Iteration 85: Average Return = 182.07\n",
+      "Iteration 86: Average Return = 180.75\n",
+      "Iteration 87: Average Return = 187.27\n",
+      "Iteration 88: Average Return = 177.33\n",
+      "Iteration 89: Average Return = 189.05\n",
+      "Iteration 90: Average Return = 184.37\n",
+      "Iteration 91: Average Return = 178.13\n",
+      "Iteration 92: Average Return = 186.29\n",
+      "Iteration 93: Average Return = 180.3\n",
+      "Iteration 94: Average Return = 187.12\n",
+      "Iteration 95: Average Return = 190.54\n",
+      "Iteration 96: Average Return = 186.13\n",
+      "Iteration 97: Average Return = 184.18\n",
+      "Iteration 98: Average Return = 185.83\n",
+      "Iteration 99: Average Return = 188.27\n",
+      "Iteration 100: Average Return = 188.96\n",
+      "Iteration 101: Average Return = 186.21\n",
+      "Iteration 102: Average Return = 191.24\n",
+      "Iteration 103: Average Return = 191.43\n",
+      "Iteration 104: Average Return = 191.1\n",
+      "Iteration 105: Average Return = 189.13\n",
+      "Iteration 106: Average Return = 190.17\n",
+      "Iteration 107: Average Return = 189.91\n",
+      "Iteration 108: Average Return = 190.11\n",
+      "Iteration 109: Average Return = 192.92\n",
+      "Iteration 110: Average Return = 187.13\n",
+      "Iteration 111: Average Return = 192.68\n",
+      "Iteration 112: Average Return = 194.33\n",
+      "Iteration 113: Average Return = 189.54\n",
+      "Iteration 114: Average Return = 192.39\n",
+      "Iteration 115: Average Return = 193.98\n",
+      "Iteration 116: Average Return = 187.77\n",
+      "Iteration 117: Average Return = 192.66\n",
+      "Iteration 118: Average Return = 192.91\n",
+      "Iteration 119: Average Return = 192.93\n",
+      "Iteration 120: Average Return = 193.57\n",
+      "Iteration 121: Average Return = 189.61\n",
+      "Iteration 122: Average Return = 192.21\n",
+      "Iteration 123: Average Return = 189.11\n",
+      "Iteration 124: Average Return = 191.71\n",
+      "Iteration 125: Average Return = 190.65\n",
+      "Iteration 126: Average Return = 190.37\n",
+      "Iteration 127: Average Return = 187.43\n",
+      "Iteration 128: Average Return = 185.5\n",
+      "Iteration 129: Average Return = 189.3\n",
+      "Iteration 130: Average Return = 190.72\n",
+      "Iteration 131: Average Return = 190.52\n",
+      "Iteration 132: Average Return = 191.24\n",
+      "Iteration 133: Average Return = 187.7\n",
+      "Iteration 134: Average Return = 188.06\n",
+      "Iteration 135: Average Return = 187.14\n",
+      "Iteration 136: Average Return = 191.17\n",
+      "Iteration 137: Average Return = 190.14\n",
+      "Iteration 138: Average Return = 190.59\n",
+      "Iteration 139: Average Return = 185.24\n",
+      "Iteration 140: Average Return = 190.52\n",
+      "Iteration 141: Average Return = 190.59\n",
+      "Iteration 142: Average Return = 188.41\n",
+      "Iteration 143: Average Return = 189.54\n",
+      "Iteration 144: Average Return = 190.87\n",
+      "Iteration 145: Average Return = 191.51\n",
+      "Iteration 146: Average Return = 188.65\n",
+      "Iteration 147: Average Return = 187.7\n",
+      "Iteration 148: Average Return = 192.18\n",
+      "Iteration 149: Average Return = 188.55\n",
+      "Iteration 150: Average Return = 191.15\n",
+      "Iteration 151: Average Return = 188.9\n",
+      "Iteration 152: Average Return = 188.65\n",
+      "Iteration 153: Average Return = 189.25\n",
+      "Iteration 154: Average Return = 188.08\n",
+      "Iteration 155: Average Return = 181.44\n",
+      "Iteration 156: Average Return = 190.69\n",
+      "Iteration 157: Average Return = 188.88\n",
+      "Iteration 158: Average Return = 189.24\n",
+      "Iteration 159: Average Return = 189.28\n",
+      "Iteration 160: Average Return = 190.74\n",
+      "Iteration 161: Average Return = 188.79\n",
+      "Iteration 162: Average Return = 187.41\n",
+      "Iteration 163: Average Return = 191.7\n",
+      "Iteration 164: Average Return = 188.06\n",
+      "Iteration 165: Average Return = 187.52\n",
+      "Iteration 166: Average Return = 184.26\n",
+      "Iteration 167: Average Return = 185.29\n",
+      "Iteration 168: Average Return = 187.25\n",
+      "Iteration 169: Average Return = 189.05\n",
+      "Iteration 170: Average Return = 186.43\n",
+      "Iteration 171: Average Return = 189.73\n",
+      "Iteration 172: Average Return = 187.74\n",
+      "Iteration 173: Average Return = 187.6\n",
+      "Iteration 174: Average Return = 192.48\n",
+      "Iteration 175: Average Return = 192.57\n",
+      "Iteration 176: Average Return = 189.65\n",
+      "Iteration 177: Average Return = 195.5\n",
+      "Solve at 177 iterations, which equals 17700 episodes.\n"
      ]
     }
    ],
@@ -658,14 +1096,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOCkuQgghIk6KixBCiIj7H4rG3zqDrIK9AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1278b4d30>"
+       "<matplotlib.figure.Figure at 0x7f5fec9586a0>"
       ]
      },
      "metadata": {},
@@ -673,9 +1111,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyo892QAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQlTp/wM82hONLa4rUQAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x127a02390>"
+       "<matplotlib.figure.Figure at 0x7f5fec958320>"
       ]
      },
      "metadata": {},
@@ -699,7 +1137,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
diff --git a/p3_1.png b/p3_1.png
new file mode 100644
index 0000000..d2b55fa
Binary files /dev/null and b/p3_1.png differ
diff --git a/p3_2.png b/p3_2.png
new file mode 100644
index 0000000..699e50a
Binary files /dev/null and b/p3_2.png differ
diff --git a/p4_1.png b/p4_1.png
new file mode 100644
index 0000000..e118979
Binary files /dev/null and b/p4_1.png differ
diff --git a/p4_2.png b/p4_2.png
new file mode 100644
index 0000000..0825877
Binary files /dev/null and b/p4_2.png differ
diff --git a/p5_1.png b/p5_1.png
new file mode 100644
index 0000000..f391f66
Binary files /dev/null and b/p5_1.png differ
diff --git a/p5_2.png b/p5_2.png
new file mode 100644
index 0000000..5f9778c
Binary files /dev/null and b/p5_2.png differ
diff --git a/p6_1.png b/p6_1.png
new file mode 100644
index 0000000..7f9cb73
Binary files /dev/null and b/p6_1.png differ
diff --git a/p6_2.png b/p6_2.png
new file mode 100644
index 0000000..446511e
Binary files /dev/null and b/p6_2.png differ
diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py
index 99fecf3..90543ab 100644
--- a/policy_gradient/policy.py
+++ b/policy_gradient/policy.py
@@ -31,6 +31,9 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session):
         """
         # YOUR CODE HERE >>>>>>
         # <<<<<<<<
+        layer1 = tf.contrib.layers.fully_connected(self._observations, hidden_dim, activation_fn=tf.tanh)
+        layer2 = tf.contrib.layers.fully_connected(layer1, out_dim, activation_fn=tf.nn.softmax)
+        probs = layer2
 
         # --------------------------------------------------
         # This operation (variable) is used when choosing action during data sampling phase
@@ -72,6 +75,7 @@ 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..3560a48 100644
--- a/policy_gradient/util.py
+++ b/policy_gradient/util.py
@@ -32,6 +32,8 @@ def discount_bootstrap(x, discount_rate, b):
     Sample code should be about 3 lines
     """
     # YOUR CODE >>>>>>>>>>>>>>>>>>>
+    b = np.append(b[1:], 0)
+    return x + discount_rate * b
     # <<<<<<<<<<<<<<<<<<<<<<<<<<<<
  
 def plot_curve(data, key, filename=None):
diff --git a/report.md b/report.md
index 1e5017e..db3c8b6 100644
--- a/report.md
+++ b/report.md
@@ -1,3 +1,50 @@
 # Homework3-Policy-Gradient report
+## Problem 1
+Following instructions: Use `tanh` as the activation function of the first hidden layer, and append softmax layer after the output of the neural network to get the probability of each possible action.
+```python
+layer1 = tf.contrib.layers.fully_connected(self._observations, hidden_dim, activation_fn=tf.tanh)
+layer2 = tf.contrib.layers.fully_connected(layer1, out_dim, activation_fn=tf.nn.softmax)
+probs = layer2
+```
+## Problem 2
+Compute surrogate loss and assign it to variable `surr_loss`. Since we need to maximize it not minimize it, so we turn it into negative.
+```python
+surr_loss = tf.reduce_mean(-log_prob * self._advantages)
+```
+
+## Problem 3
+Simply subtract the variance and then assign the result to the variable `a`.
+```python
+ a = r - b
+```
+![](https://i.imgur.com/78ARcKp.png)
+![](https://i.imgur.com/TfxMgpZ.png)
+
+## Problem 4
+Curve without baseline substraction.
+
+![](https://i.imgur.com/hAIowgF.png)
+![](https://i.imgur.com/jxAnLjv.png)
+
+## Problem 5
+Following `y = r(s_t,a,s_{t+1}) + \gamma*V_t`
+```python
+b = np.append(b[1:], 0)
+y = x + discount_rate * b
+```
+
+![](https://i.imgur.com/fonXELu.png)
+![](https://i.imgur.com/20uIuUT.png)
+
+
+## Problem 6
+Generalized Advantage Estimation
+```python
+a = util.discount(a, self.discount_rate * LAMBDA)
+```
+
+![](https://i.imgur.com/HnAgfXD.png)
+![](https://i.imgur.com/8vN4nNC.png)
+
+
 
-TA: try to elaborate the algorithms that you implemented and any details worth mentioned.