diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb
index 4529e50..09cc9fb 100644
--- a/Lab3-policy-gradient.ipynb
+++ b/Lab3-policy-gradient.ipynb
@@ -4,13 +4,14 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
     "# Automatically reload changes to external code\n",
     "%load_ext autoreload\n",
-    "%autoreload 2"
+    "%autoreload 2\n",
+    "%matplotlib inline"
    ]
   },
   {
@@ -28,17 +29,11 @@
   },
   {
    "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 +98,16 @@
   },
   {
    "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",
+      "/home/assistant/anaconda3/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
       "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
      ]
     }
@@ -152,7 +149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "collapsed": true
    },
@@ -214,6 +211,7 @@
     "            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 +256,105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
+   "execution_count": 8,
+   "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 = 19.77\n",
+      "Iteration 2: Average Return = 20.23\n",
+      "Iteration 3: Average Return = 22.17\n",
+      "Iteration 4: Average Return = 25.0\n",
+      "Iteration 5: Average Return = 23.43\n",
+      "Iteration 6: Average Return = 27.1\n",
+      "Iteration 7: Average Return = 27.61\n",
+      "Iteration 8: Average Return = 28.52\n",
+      "Iteration 9: Average Return = 32.36\n",
+      "Iteration 10: Average Return = 33.32\n",
+      "Iteration 11: Average Return = 33.35\n",
+      "Iteration 12: Average Return = 34.79\n",
+      "Iteration 13: Average Return = 35.09\n",
+      "Iteration 14: Average Return = 35.04\n",
+      "Iteration 15: Average Return = 37.94\n",
+      "Iteration 16: Average Return = 38.33\n",
+      "Iteration 17: Average Return = 45.0\n",
+      "Iteration 18: Average Return = 40.33\n",
+      "Iteration 19: Average Return = 41.47\n",
+      "Iteration 20: Average Return = 44.01\n",
+      "Iteration 21: Average Return = 43.84\n",
+      "Iteration 22: Average Return = 46.0\n",
+      "Iteration 23: Average Return = 44.93\n",
+      "Iteration 24: Average Return = 47.93\n",
+      "Iteration 25: Average Return = 49.81\n",
+      "Iteration 26: Average Return = 51.0\n",
+      "Iteration 27: Average Return = 54.29\n",
+      "Iteration 28: Average Return = 55.07\n",
+      "Iteration 29: Average Return = 49.35\n",
+      "Iteration 30: Average Return = 58.41\n",
+      "Iteration 31: Average Return = 54.68\n",
+      "Iteration 32: Average Return = 56.5\n",
+      "Iteration 33: Average Return = 55.3\n",
+      "Iteration 34: Average Return = 56.17\n",
+      "Iteration 35: Average Return = 56.57\n",
+      "Iteration 36: Average Return = 57.72\n",
+      "Iteration 37: Average Return = 59.39\n",
+      "Iteration 38: Average Return = 60.76\n",
+      "Iteration 39: Average Return = 61.4\n",
+      "Iteration 40: Average Return = 57.6\n",
+      "Iteration 41: Average Return = 56.77\n",
+      "Iteration 42: Average Return = 64.65\n",
+      "Iteration 43: Average Return = 61.76\n",
+      "Iteration 44: Average Return = 68.66\n",
+      "Iteration 45: Average Return = 65.79\n",
+      "Iteration 46: Average Return = 62.49\n",
+      "Iteration 47: Average Return = 67.81\n",
+      "Iteration 48: Average Return = 66.27\n",
+      "Iteration 49: Average Return = 72.55\n",
+      "Iteration 50: Average Return = 66.85\n",
+      "Iteration 51: Average Return = 73.11\n",
+      "Iteration 52: Average Return = 75.78\n",
+      "Iteration 53: Average Return = 74.53\n",
+      "Iteration 54: Average Return = 78.67\n",
+      "Iteration 55: Average Return = 85.61\n",
+      "Iteration 56: Average Return = 83.28\n",
+      "Iteration 57: Average Return = 89.36\n",
+      "Iteration 58: Average Return = 95.14\n",
+      "Iteration 59: Average Return = 94.41\n",
+      "Iteration 60: Average Return = 103.92\n",
+      "Iteration 61: Average Return = 108.29\n",
+      "Iteration 62: Average Return = 116.82\n",
+      "Iteration 63: Average Return = 112.77\n",
+      "Iteration 64: Average Return = 114.1\n",
+      "Iteration 65: Average Return = 123.68\n",
+      "Iteration 66: Average Return = 120.14\n",
+      "Iteration 67: Average Return = 131.54\n",
+      "Iteration 68: Average Return = 124.85\n",
+      "Iteration 69: Average Return = 126.46\n",
+      "Iteration 70: Average Return = 129.24\n",
+      "Iteration 71: Average Return = 134.63\n",
+      "Iteration 72: Average Return = 134.31\n",
+      "Iteration 73: Average Return = 140.47\n",
+      "Iteration 74: Average Return = 141.84\n",
+      "Iteration 75: Average Return = 148.68\n",
+      "Iteration 76: Average Return = 154.5\n",
+      "Iteration 77: Average Return = 156.97\n",
+      "Iteration 78: Average Return = 162.44\n",
+      "Iteration 79: Average Return = 154.25\n",
+      "Iteration 80: Average Return = 160.28\n",
+      "Iteration 81: Average Return = 169.13\n",
+      "Iteration 82: Average Return = 180.06\n",
+      "Iteration 83: Average Return = 176.97\n",
+      "Iteration 84: Average Return = 176.42\n",
+      "Iteration 85: Average Return = 186.76\n",
+      "Iteration 86: Average Return = 190.29\n",
+      "Iteration 87: Average Return = 190.12\n",
+      "Iteration 88: Average Return = 191.19\n",
+      "Iteration 89: Average Return = 197.08\n",
+      "Solve at 89 iterations, which equals 8900 episodes.\n"
      ]
     }
    ],
@@ -371,14 +376,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\niAsAwBxxAQCY+wBov72c9Z6d8AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107bac18>"
+       "<matplotlib.figure.Figure at 0x7fe4da646668>"
       ]
      },
      "metadata": {},
@@ -386,9 +393,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyo892QAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1107ba828>"
+       "<matplotlib.figure.Figure at 0x7fe4da6c9940>"
       ]
      },
      "metadata": {},
@@ -428,6 +435,146 @@
     "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": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Average Return = 25.98\n",
+      "Iteration 2: Average Return = 31.66\n",
+      "Iteration 3: Average Return = 30.57\n",
+      "Iteration 4: Average Return = 31.98\n",
+      "Iteration 5: Average Return = 35.06\n",
+      "Iteration 6: Average Return = 37.63\n",
+      "Iteration 7: Average Return = 37.69\n",
+      "Iteration 8: Average Return = 45.96\n",
+      "Iteration 9: Average Return = 44.15\n",
+      "Iteration 10: Average Return = 45.05\n",
+      "Iteration 11: Average Return = 47.95\n",
+      "Iteration 12: Average Return = 50.74\n",
+      "Iteration 13: Average Return = 52.02\n",
+      "Iteration 14: Average Return = 55.01\n",
+      "Iteration 15: Average Return = 52.5\n",
+      "Iteration 16: Average Return = 55.65\n",
+      "Iteration 17: Average Return = 54.39\n",
+      "Iteration 18: Average Return = 64.79\n",
+      "Iteration 19: Average Return = 67.11\n",
+      "Iteration 20: Average Return = 63.36\n",
+      "Iteration 21: Average Return = 69.5\n",
+      "Iteration 22: Average Return = 66.57\n",
+      "Iteration 23: Average Return = 80.03\n",
+      "Iteration 24: Average Return = 76.17\n",
+      "Iteration 25: Average Return = 78.72\n",
+      "Iteration 26: Average Return = 85.57\n",
+      "Iteration 27: Average Return = 92.29\n",
+      "Iteration 28: Average Return = 102.69\n",
+      "Iteration 29: Average Return = 101.17\n",
+      "Iteration 30: Average Return = 105.99\n",
+      "Iteration 31: Average Return = 110.33\n",
+      "Iteration 32: Average Return = 103.82\n",
+      "Iteration 33: Average Return = 116.29\n",
+      "Iteration 34: Average Return = 113.42\n",
+      "Iteration 35: Average Return = 115.32\n",
+      "Iteration 36: Average Return = 120.76\n",
+      "Iteration 37: Average Return = 135.11\n",
+      "Iteration 38: Average Return = 129.55\n",
+      "Iteration 39: Average Return = 141.35\n",
+      "Iteration 40: Average Return = 149.3\n",
+      "Iteration 41: Average Return = 147.26\n",
+      "Iteration 42: Average Return = 151.25\n",
+      "Iteration 43: Average Return = 149.4\n",
+      "Iteration 44: Average Return = 151.49\n",
+      "Iteration 45: Average Return = 165.04\n",
+      "Iteration 46: Average Return = 157.06\n",
+      "Iteration 47: Average Return = 157.11\n",
+      "Iteration 48: Average Return = 166.29\n",
+      "Iteration 49: Average Return = 168.21\n",
+      "Iteration 50: Average Return = 164.75\n",
+      "Iteration 51: Average Return = 178.09\n",
+      "Iteration 52: Average Return = 182.78\n",
+      "Iteration 53: Average Return = 179.29\n",
+      "Iteration 54: Average Return = 180.1\n",
+      "Iteration 55: Average Return = 180.2\n",
+      "Iteration 56: Average Return = 184.85\n",
+      "Iteration 57: Average Return = 183.69\n",
+      "Iteration 58: Average Return = 176.73\n",
+      "Iteration 59: Average Return = 184.04\n",
+      "Iteration 60: Average Return = 182.17\n",
+      "Iteration 61: Average Return = 189.81\n",
+      "Iteration 62: Average Return = 189.37\n",
+      "Iteration 63: Average Return = 187.18\n",
+      "Iteration 64: Average Return = 190.59\n",
+      "Iteration 65: Average Return = 185.71\n",
+      "Iteration 66: Average Return = 192.83\n",
+      "Iteration 67: Average Return = 193.11\n",
+      "Iteration 68: Average Return = 192.16\n",
+      "Iteration 69: Average Return = 191.0\n",
+      "Iteration 70: Average Return = 192.3\n",
+      "Iteration 71: Average Return = 189.57\n",
+      "Iteration 72: Average Return = 191.14\n",
+      "Iteration 73: Average Return = 191.33\n",
+      "Iteration 74: Average Return = 191.76\n",
+      "Iteration 75: Average Return = 187.3\n",
+      "Iteration 76: Average Return = 195.01\n",
+      "Solve at 76 iterations, which equals 7600 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": 7,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZ0AAAENCAYAAADUubjhAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe4da7ad710>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fe4da7adda0>"
+      ]
+     },
+     "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": {},
@@ -477,9 +624,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": false
    },
    "outputs": [],
    "source": [
@@ -523,6 +670,7 @@
     "            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",
@@ -543,8 +691,274 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 6,
    "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Iteration 1: Average Return = 30.42\n",
+      "Iteration 2: Average Return = 31.75\n",
+      "Iteration 3: Average Return = 36.61\n",
+      "Iteration 4: Average Return = 33.66\n",
+      "Iteration 5: Average Return = 39.3\n",
+      "Iteration 6: Average Return = 46.84\n",
+      "Iteration 7: Average Return = 43.69\n",
+      "Iteration 8: Average Return = 44.6\n",
+      "Iteration 9: Average Return = 51.99\n",
+      "Iteration 10: Average Return = 53.31\n",
+      "Iteration 11: Average Return = 53.02\n",
+      "Iteration 12: Average Return = 56.74\n",
+      "Iteration 13: Average Return = 54.82\n",
+      "Iteration 14: Average Return = 62.28\n",
+      "Iteration 15: Average Return = 53.88\n",
+      "Iteration 16: Average Return = 66.47\n",
+      "Iteration 17: Average Return = 72.31\n",
+      "Iteration 18: Average Return = 71.48\n",
+      "Iteration 19: Average Return = 77.45\n",
+      "Iteration 20: Average Return = 79.39\n",
+      "Iteration 21: Average Return = 87.43\n",
+      "Iteration 22: Average Return = 81.72\n",
+      "Iteration 23: Average Return = 84.48\n",
+      "Iteration 24: Average Return = 84.25\n",
+      "Iteration 25: Average Return = 91.31\n",
+      "Iteration 26: Average Return = 95.76\n",
+      "Iteration 27: Average Return = 95.09\n",
+      "Iteration 28: Average Return = 97.76\n",
+      "Iteration 29: Average Return = 95.17\n",
+      "Iteration 30: Average Return = 92.92\n",
+      "Iteration 31: Average Return = 92.32\n",
+      "Iteration 32: Average Return = 108.99\n",
+      "Iteration 33: Average Return = 102.94\n",
+      "Iteration 34: Average Return = 118.28\n",
+      "Iteration 35: Average Return = 107.06\n",
+      "Iteration 36: Average Return = 120.54\n",
+      "Iteration 37: Average Return = 126.66\n",
+      "Iteration 38: Average Return = 125.04\n",
+      "Iteration 39: Average Return = 125.53\n",
+      "Iteration 40: Average Return = 128.26\n",
+      "Iteration 41: Average Return = 130.53\n",
+      "Iteration 42: Average Return = 138.18\n",
+      "Iteration 43: Average Return = 135.19\n",
+      "Iteration 44: Average Return = 131.15\n",
+      "Iteration 45: Average Return = 142.54\n",
+      "Iteration 46: Average Return = 139.32\n",
+      "Iteration 47: Average Return = 136.11\n",
+      "Iteration 48: Average Return = 137.66\n",
+      "Iteration 49: Average Return = 127.27\n",
+      "Iteration 50: Average Return = 134.82\n",
+      "Iteration 51: Average Return = 133.24\n",
+      "Iteration 52: Average Return = 146.03\n",
+      "Iteration 53: Average Return = 142.4\n",
+      "Iteration 54: Average Return = 146.13\n",
+      "Iteration 55: Average Return = 149.51\n",
+      "Iteration 56: Average Return = 146.21\n",
+      "Iteration 57: Average Return = 152.22\n",
+      "Iteration 58: Average Return = 151.85\n",
+      "Iteration 59: Average Return = 147.66\n",
+      "Iteration 60: Average Return = 151.37\n",
+      "Iteration 61: Average Return = 161.92\n",
+      "Iteration 62: Average Return = 161.77\n",
+      "Iteration 63: Average Return = 165.17\n",
+      "Iteration 64: Average Return = 152.92\n",
+      "Iteration 65: Average Return = 154.4\n",
+      "Iteration 66: Average Return = 158.29\n",
+      "Iteration 67: Average Return = 143.3\n",
+      "Iteration 68: Average Return = 147.06\n",
+      "Iteration 69: Average Return = 157.79\n",
+      "Iteration 70: Average Return = 151.51\n",
+      "Iteration 71: Average Return = 150.82\n",
+      "Iteration 72: Average Return = 151.1\n",
+      "Iteration 73: Average Return = 161.63\n",
+      "Iteration 74: Average Return = 158.09\n",
+      "Iteration 75: Average Return = 162.35\n",
+      "Iteration 76: Average Return = 163.68\n",
+      "Iteration 77: Average Return = 168.79\n",
+      "Iteration 78: Average Return = 168.04\n",
+      "Iteration 79: Average Return = 172.63\n",
+      "Iteration 80: Average Return = 165.46\n",
+      "Iteration 81: Average Return = 170.22\n",
+      "Iteration 82: Average Return = 169.59\n",
+      "Iteration 83: Average Return = 168.76\n",
+      "Iteration 84: Average Return = 166.05\n",
+      "Iteration 85: Average Return = 166.01\n",
+      "Iteration 86: Average Return = 163.53\n",
+      "Iteration 87: Average Return = 157.96\n",
+      "Iteration 88: Average Return = 146.88\n",
+      "Iteration 89: Average Return = 160.35\n",
+      "Iteration 90: Average Return = 149.66\n",
+      "Iteration 91: Average Return = 156.96\n",
+      "Iteration 92: Average Return = 163.76\n",
+      "Iteration 93: Average Return = 167.98\n",
+      "Iteration 94: Average Return = 167.03\n",
+      "Iteration 95: Average Return = 176.91\n",
+      "Iteration 96: Average Return = 180.05\n",
+      "Iteration 97: Average Return = 175.29\n",
+      "Iteration 98: Average Return = 176.76\n",
+      "Iteration 99: Average Return = 171.04\n",
+      "Iteration 100: Average Return = 174.83\n",
+      "Iteration 101: Average Return = 170.27\n",
+      "Iteration 102: Average Return = 174.54\n",
+      "Iteration 103: Average Return = 174.44\n",
+      "Iteration 104: Average Return = 171.05\n",
+      "Iteration 105: Average Return = 162.14\n",
+      "Iteration 106: Average Return = 158.97\n",
+      "Iteration 107: Average Return = 160.22\n",
+      "Iteration 108: Average Return = 158.75\n",
+      "Iteration 109: Average Return = 163.81\n",
+      "Iteration 110: Average Return = 161.29\n",
+      "Iteration 111: Average Return = 160.98\n",
+      "Iteration 112: Average Return = 166.49\n",
+      "Iteration 113: Average Return = 160.24\n",
+      "Iteration 114: Average Return = 171.76\n",
+      "Iteration 115: Average Return = 174.59\n",
+      "Iteration 116: Average Return = 179.09\n",
+      "Iteration 117: Average Return = 175.14\n",
+      "Iteration 118: Average Return = 179.78\n",
+      "Iteration 119: Average Return = 182.82\n",
+      "Iteration 120: Average Return = 181.48\n",
+      "Iteration 121: Average Return = 175.98\n",
+      "Iteration 122: Average Return = 178.81\n",
+      "Iteration 123: Average Return = 174.22\n",
+      "Iteration 124: Average Return = 174.76\n",
+      "Iteration 125: Average Return = 170.89\n",
+      "Iteration 126: Average Return = 164.49\n",
+      "Iteration 127: Average Return = 166.15\n",
+      "Iteration 128: Average Return = 163.73\n",
+      "Iteration 129: Average Return = 159.59\n",
+      "Iteration 130: Average Return = 162.13\n",
+      "Iteration 131: Average Return = 161.68\n",
+      "Iteration 132: Average Return = 165.11\n",
+      "Iteration 133: Average Return = 162.93\n",
+      "Iteration 134: Average Return = 167.55\n",
+      "Iteration 135: Average Return = 169.21\n",
+      "Iteration 136: Average Return = 168.64\n",
+      "Iteration 137: Average Return = 175.12\n",
+      "Iteration 138: Average Return = 180.06\n",
+      "Iteration 139: Average Return = 179.14\n",
+      "Iteration 140: Average Return = 177.09\n",
+      "Iteration 141: Average Return = 178.77\n",
+      "Iteration 142: Average Return = 184.12\n",
+      "Iteration 143: Average Return = 179.37\n",
+      "Iteration 144: Average Return = 177.29\n",
+      "Iteration 145: Average Return = 181.96\n",
+      "Iteration 146: Average Return = 177.03\n",
+      "Iteration 147: Average Return = 171.98\n",
+      "Iteration 148: Average Return = 177.8\n",
+      "Iteration 149: Average Return = 168.45\n",
+      "Iteration 150: Average Return = 168.69\n",
+      "Iteration 151: Average Return = 164.69\n",
+      "Iteration 152: Average Return = 172.83\n",
+      "Iteration 153: Average Return = 157.88\n",
+      "Iteration 154: Average Return = 166.44\n",
+      "Iteration 155: Average Return = 165.06\n",
+      "Iteration 156: Average Return = 165.72\n",
+      "Iteration 157: Average Return = 168.93\n",
+      "Iteration 158: Average Return = 174.47\n",
+      "Iteration 159: Average Return = 172.61\n",
+      "Iteration 160: Average Return = 179.54\n",
+      "Iteration 161: Average Return = 180.98\n",
+      "Iteration 162: Average Return = 181.34\n",
+      "Iteration 163: Average Return = 182.22\n",
+      "Iteration 164: Average Return = 180.93\n",
+      "Iteration 165: Average Return = 185.27\n",
+      "Iteration 166: Average Return = 186.11\n",
+      "Iteration 167: Average Return = 186.11\n",
+      "Iteration 168: Average Return = 181.49\n",
+      "Iteration 169: Average Return = 180.88\n",
+      "Iteration 170: Average Return = 180.11\n",
+      "Iteration 171: Average Return = 178.08\n",
+      "Iteration 172: Average Return = 173.74\n",
+      "Iteration 173: Average Return = 170.05\n",
+      "Iteration 174: Average Return = 167.27\n",
+      "Iteration 175: Average Return = 170.92\n",
+      "Iteration 176: Average Return = 163.86\n",
+      "Iteration 177: Average Return = 163.59\n",
+      "Iteration 178: Average Return = 163.59\n",
+      "Iteration 179: Average Return = 166.06\n",
+      "Iteration 180: Average Return = 161.0\n",
+      "Iteration 181: Average Return = 165.54\n",
+      "Iteration 182: Average Return = 164.5\n",
+      "Iteration 183: Average Return = 164.24\n",
+      "Iteration 184: Average Return = 171.55\n",
+      "Iteration 185: Average Return = 171.15\n",
+      "Iteration 186: Average Return = 173.47\n",
+      "Iteration 187: Average Return = 174.87\n",
+      "Iteration 188: Average Return = 176.26\n",
+      "Iteration 189: Average Return = 179.25\n",
+      "Iteration 190: Average Return = 181.22\n",
+      "Iteration 191: Average Return = 184.07\n",
+      "Iteration 192: Average Return = 184.76\n",
+      "Iteration 193: Average Return = 183.26\n",
+      "Iteration 194: Average Return = 184.08\n",
+      "Iteration 195: Average Return = 181.24\n",
+      "Iteration 196: Average Return = 177.06\n",
+      "Iteration 197: Average Return = 178.87\n",
+      "Iteration 198: Average Return = 181.03\n",
+      "Iteration 199: Average Return = 177.33\n",
+      "Iteration 200: Average Return = 173.78\n"
+     ]
+    }
+   ],
+   "source": [
+    "#problem 5\n",
+    "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",
+    "# reinitialize the baseline function\n",
+    "baseline = LinearFeatureBaseline(env.spec) \n",
+    "sess.run(tf.global_variables_initializer())\n",
+    "po = PolicyOptimizer_actor_critic(env, policy, baseline, n_iter, n_episode, path_length,\n",
+    "                     discount_rate)\n",
+    "\n",
+    "# Train the policy optimizer\n",
+    "loss_list, avg_return_list = po.train()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n15HFbgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fab181e9128>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7fab1824a9e8>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "util.plot_curve(loss_list, \"loss\")\n",
+    "util.plot_curve(avg_return_list, \"average return\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": false,
     "scrolled": true
    },
    "outputs": [
@@ -552,94 +966,91 @@
      "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 = 27.74\n",
+      "Iteration 2: Average Return = 30.54\n",
+      "Iteration 3: Average Return = 30.57\n",
+      "Iteration 4: Average Return = 31.6\n",
+      "Iteration 5: Average Return = 32.14\n",
+      "Iteration 6: Average Return = 38.38\n",
+      "Iteration 7: Average Return = 33.61\n",
+      "Iteration 8: Average Return = 35.8\n",
+      "Iteration 9: Average Return = 40.73\n",
+      "Iteration 10: Average Return = 45.81\n",
+      "Iteration 11: Average Return = 40.85\n",
+      "Iteration 12: Average Return = 42.67\n",
+      "Iteration 13: Average Return = 48.81\n",
+      "Iteration 14: Average Return = 46.8\n",
+      "Iteration 15: Average Return = 46.21\n",
+      "Iteration 16: Average Return = 52.94\n",
+      "Iteration 17: Average Return = 48.96\n",
+      "Iteration 18: Average Return = 50.92\n",
+      "Iteration 19: Average Return = 54.77\n",
+      "Iteration 20: Average Return = 54.22\n",
+      "Iteration 21: Average Return = 51.52\n",
+      "Iteration 22: Average Return = 58.2\n",
+      "Iteration 23: Average Return = 57.95\n",
+      "Iteration 24: Average Return = 61.3\n",
+      "Iteration 25: Average Return = 61.43\n",
+      "Iteration 26: Average Return = 65.0\n",
+      "Iteration 27: Average Return = 68.26\n",
+      "Iteration 28: Average Return = 69.28\n",
+      "Iteration 29: Average Return = 80.05\n",
+      "Iteration 30: Average Return = 85.03\n",
+      "Iteration 31: Average Return = 90.33\n",
+      "Iteration 32: Average Return = 98.74\n",
+      "Iteration 33: Average Return = 114.04\n",
+      "Iteration 34: Average Return = 118.01\n",
+      "Iteration 35: Average Return = 114.93\n",
+      "Iteration 36: Average Return = 128.8\n",
+      "Iteration 37: Average Return = 116.02\n",
+      "Iteration 38: Average Return = 124.77\n",
+      "Iteration 39: Average Return = 128.39\n",
+      "Iteration 40: Average Return = 140.18\n",
+      "Iteration 41: Average Return = 141.24\n",
+      "Iteration 42: Average Return = 144.39\n",
+      "Iteration 43: Average Return = 144.51\n",
+      "Iteration 44: Average Return = 158.55\n",
+      "Iteration 45: Average Return = 154.68\n",
+      "Iteration 46: Average Return = 169.73\n",
+      "Iteration 47: Average Return = 169.71\n",
+      "Iteration 48: Average Return = 178.76\n",
+      "Iteration 49: Average Return = 174.85\n",
+      "Iteration 50: Average Return = 179.57\n",
+      "Iteration 51: Average Return = 177.01\n",
+      "Iteration 52: Average Return = 174.17\n",
+      "Iteration 53: Average Return = 170.14\n",
+      "Iteration 54: Average Return = 172.01\n",
+      "Iteration 55: Average Return = 171.13\n",
+      "Iteration 56: Average Return = 177.0\n",
+      "Iteration 57: Average Return = 167.12\n",
+      "Iteration 58: Average Return = 172.87\n",
+      "Iteration 59: Average Return = 170.21\n",
+      "Iteration 60: Average Return = 174.77\n",
+      "Iteration 61: Average Return = 179.47\n",
+      "Iteration 62: Average Return = 177.49\n",
+      "Iteration 63: Average Return = 180.49\n",
+      "Iteration 64: Average Return = 178.13\n",
+      "Iteration 65: Average Return = 185.82\n",
+      "Iteration 66: Average Return = 184.96\n",
+      "Iteration 67: Average Return = 184.53\n",
+      "Iteration 68: Average Return = 185.38\n",
+      "Iteration 69: Average Return = 190.47\n",
+      "Iteration 70: Average Return = 186.08\n",
+      "Iteration 71: Average Return = 189.78\n",
+      "Iteration 72: Average Return = 187.48\n",
+      "Iteration 73: Average Return = 188.53\n",
+      "Iteration 74: Average Return = 192.1\n",
+      "Iteration 75: Average Return = 188.91\n",
+      "Iteration 76: Average Return = 189.07\n",
+      "Iteration 77: Average Return = 190.61\n",
+      "Iteration 78: Average Return = 190.94\n",
+      "Iteration 79: Average Return = 195.53\n",
+      "Solve at 79 iterations, which equals 7900 episodes.\n"
      ]
     }
    ],
    "source": [
+    "#problem 6\n",
     "sess.run(tf.global_variables_initializer())\n",
     "\n",
     "n_iter = 200\n",
@@ -658,14 +1069,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
    "outputs": [
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAENCAYAAADDmygoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8lOW5+P/PPRmyb7OEhJCwhE3RgMaAgkuKRGvtltrW\nr3q0p4djbYvVc2jtsfh16etHqXisJ9VWW/uV0lY99diq6WnrmqKg4BL2TfYtQEKWSUJWsjz3749n\nZpLJZJlMJpkhXO/XyxeZmft55kqQuXJv16201hohhBAihCzhDkAIIcTYI8lFCCFEyElyEUIIEXKS\nXIQQQoScJBchhBAhJ8lFCCFEyFnDHYDHtm3bWLNmDYZhsHjxYoqKinxe11qzZs0atm7dSkxMDEuX\nLiUnJweAZ555hi1btpCSksITTzwRjvCFEEL0EBE9F8MwWL16NQ888ADFxcVs2LCBEydO+LTZunUr\nlZWVPPXUU9x1110899xz3tc+85nP8MADD4x22EIIIfoREcnl4MGDZGRkkJ6ejtVqZeHChZSVlfm0\n2bRpE9dccw1KKWbOnElzczN1dXUAzJ49m8TExHCELoQQog8RkVxcLhcOh8P72OFw4HK5/No4nc4B\n2wghhIgMETPnMhpKS0spLS0FYNWqVbS3twd1H6vVSmdnZyhDCxmJLTgSW3AktuCcy7FFR0cHdp9Q\nBTQcdrud2tpa7+Pa2lrsdrtfm5qamgHbDKawsJDCwkLv4573Gwqn0xn0tSNNYguOxBYciS0453Js\nmZmZAd0nIobFpk2bRkVFBVVVVXR2drJx40by8/N92uTn57N+/Xq01uzfv5/4+HhsNluYIhZCCDGQ\niOi5REVFsWTJElauXIlhGCxatIjs7GzefvttAK6//nouvfRStmzZwr333kt0dDRLly71Xv/zn/+c\nPXv20NjYyHe+8x1uvvlmrr322nB9O0IIcd6LiOQCkJeXR15ens9z119/vfdrpRR33nlnn9f++7//\n+4jGJoQQYmgiYlhMCCHE2CLJRQghRMhJchFCCBFyklyEEGIMMcreR9fXDt5whElyEUKIMUKfqUf/\n5nH0X/473KFIchFCiDHj+GEA9OaN6I6OsIYiyUUIIcYIXW4mF1qbYffmsMYiyUUIIcaK44fB7oSk\nFPTH68MaSsRsohRCCDE8uvwITJ6OSrWjPyhFt7WgYuPDEov0XIQQYgzQba1QdQo1KQc1vwA62tFb\nPw5bPJJchBBiLDhxBLRGZU+DaReAYzz6k/ANjUlyEUKIIdIHP0W3tYQ7DB/avVKM7KkopVDzr4Y9\nW9GNDWGJR5KLEEIMgd63E+Ox+9Gv/yncofgqPwKJyWAzT/VV8wvAMNCbNoQlHEkuQggRIN3ZgfHi\nr82vN29Eaz38e5YfwfjgneHf5/hhmJSDUgoAlTUFJk5Gf7Ju2PcOhiQXIURQdM1pWt7+S7jDGFX6\nnb9ARTnkLYCqCjhxdHj3qyjHeOJB9B9+aU7IB3ufzk44dQyVPdXneTX/Gjj4Kbq2alhxBkOSixAi\nKPrd12n81WPouvDXsRoNurYK/beX4JIrsNy+FJQFvTn4ISftqsH4+SPQ1gJaDy9RVZRDZydk5/g8\nreZfY77XR+8Ff+8gSXIRQgRFV5SbXxw9EN5ARonxx98ACsst30IlpcCsi9GbNwZ3r8YzZmJpacby\n3eWAe49KkDw789WkaT7PK2c6zMpFbygNyRDeUEhyEUIEx51c9JH9YQ5k5OltH8P2T1BfvAXlSANA\n5S2EyhPoU8eHdq+WZup/+h9QXYHl7v8Lc+ZBQhJ4Srf0bl9Rjh6sV3P8METHQPoEv5fUVYVQXQn7\ndw8pzuGS5CKEGDJ99iy4x/F1gD0XrTXaMEYyrBGh21oxXvp/MCEbVfhl7/Pq0itAqYB6L7qjA731\nI4xfP4Zx3z/TsW8nljt/gLpgjjkBnz21356LseZJjF+sGPBnp8sPQ9YUlCXK7zV16UKIi0dvGP6i\ngaGQ5CKEGLrTJ0BrLCk2OHpw0KShz9Rh/OT76N8Wj1KAA9OVJ9AH9wTWtuQFqK3CcsfdKGt3xSyV\naodpFw4676JPHsP4j29iPPNT9P5dqKuuw/74atRlV3bfa1IOnDyG7uryvbatFY4dBFc17N/V9/0N\nA8qPmPfog4qJQc27Br15A7p19PbmRExy2bZtG//2b//GPffcQ0lJid/rWmt++9vfcs8993Dfffdx\n+PDhgK8VQoSWPmUOicVec71Zgbeqov+2DXUYP3sQjh9Cb/oA3XRmtMLsl/H8M2ZvoHPgsvT60F70\n2r+hFt2ImjHb73V12UIzKVSe7P8e778NbW1Y7n0Ey3+uwXLbtxk37QLfRtlToaMdet/nyH5wJ279\n4bt9v0FtFbS2+E3m+8R51XXQ3o4uG70d+xGRXAzDYPXq1TzwwAMUFxezYcMGTpw44dNm69atVFZW\n8tRTT3HXXXfx3HPPBXytECLEKk6AxUJswWcB0Ef7nnfR9S6Mn/1fqK1CfX0JdHWht340mpH6x9Tc\nCAf3QEszfLq9/3YdHRi//wXYHKibvtFnG5W3wGy7pe+hMa01ekcZXDgXlXuZT8/H5z7uxKB7zbvo\nA7tBWVCXXWnuqznb5n+xe2e+GiC5MGW6ueflg9L+24RYRCSXgwcPkpGRQXp6OlarlYULF1JWVubT\nZtOmTVxzzTUopZg5cybNzc3U1dUFdK0QIrR0xXEYPwHrlBkQEwtHD/q3qa81E0tdDZZ/+zHqui9D\nWkbYdox749q52ewNWCwDxqLf+BNUlGO5fWm/lYWVPQ2mzux/3qWiHKorUXPmDRxU+kSwjjN32feM\n4eCnkD0Fde3n4WwreuuH/nEePwwWC2RN7vf2SinUlYVwZD/65NAWIAQrIpKLy+XC4XB4HzscDlwu\nl18bp9Pp1yaQa4UQIVZxAjKyUVFRMCmnzxVjxh+ehnqXmVhmXmR+wOVfCXu3oxvDODS2owySU1Hz\nr0Fv+6jPoTF98hj69T+jLi9A5eYPeDt12ZXmkF91pf99tpu/6A6WXJTVavYsevRcdGcnHN6Hmj4b\nps8GZzp641r/9yg/bC42GBc98Htc8RmIsqI3jk7v5bw6z6W0tJTSUvMHu2rVKp9kNRRWqzXoa0ea\nxBYciS1wuqODqqoKEq68FqvVSvyFc2h541UcqaneYZ+u2mpqdm8h4aY7SFxwjffajsIv4HrjFRIO\n7CT++i/39xYh0dfPTXd2Ur17K7ELPkPs5QXUf/QeySePEHPZwu42WlP3swfQCYk4l96PJTl1wPfp\nuu4L1LzyO+K2bCDxn77t85prz1Z0zkwcM33nWPqK7cyMC2n7eD0OhwOlFB0H9uA620Zy3uXEjh9P\n07Wfp/lPa7BhEOUcb753TRW1R/YRc9lCUgb7f8TppH7+VbR/9B6OO5ehxo0L+OcWjIhILna7ndra\n7l2+tbW12O12vzY1NTV+bbq6uga91qOwsJDCwkLv4573Gwqn0xn0tSNNYguOxBY4feo4GF20pDpI\n7OykNSMbOtqp2b4ZNdncxGe8/mcwDFovXUBbj9h1kh3GZ9L43pu05F3Z31sMP8baahIO76E5/xpv\nrS0A/el2dEsT7bPm0J6VA3HxNKx9A8vkmd1tNm/E2LcL9Y3v4WrvhMF+9pZxMPdyml9/hdaCG1Gx\nceZ9Gs+Y9/n8zX5/f339nRrjM9GNDdQc2IeyOzE2mUNgjelZNNXUoOdeDi//lto3XsXyua+hGxsw\n/nM5dHXRXnBjQP+P6HnXoD98j5p33/TOF/U22P9vmZmZg74PRMiw2LRp06ioqKCqqorOzk42btxI\nfr5vVzQ/P5/169ejtWb//v3Ex8djs9kCulYIEULuzZNqwiTzzykzgO79Llprc/hmxmzUeN8PInNo\n7CrYuxN9pn5Ib6tPHkPv3+3+bxf6mP88j7ftW6/S+JsnoNfiAb2jzJzbmH0Jatw41NzLfYbGdGcn\nxqt/gMxJqCsXBxyb5bNfgZYm9IbuISe9cxNoAzV3kPkWN++EvHveRR/YDWkZqFR3lePxE2D6hegP\n30W3NGP8/MfmEunvPeRXU6xfsy9Ffes+uCgv4O8tWBHRc4mKimLJkiWsXLkSwzBYtGgR2dnZvP32\n2wBcf/31XHrppWzZsoV7772X6Oholi5dOuC1QoiRoSvKQSnIyDKfcKZDYpJZBqbgBji0F06fRN1w\nU5/Xq3lXol9/Gb3lQ9RnPhfYe5Yfwfj//s3vecv3V6AunOvbVmv0rs0AGK/8DsucfJR1nPn89k/g\ngjmomFgzlvyr0B+9C5/ugNzL0B+8DVWnsHzvwT43JPZHTb/Q/OB/5y/oz9yIiopC7/gEUmzQqyRL\nv7Imm5syyw/BnHw4+CnqYt8koBZci37+aYxV/2HGefeDqJkXBR5nVJS33thIi4jkApCXl0denu8P\n8vrrr/d+rZTizjvvDPhaIcQIOVUO9jRUTAxg/ttkygzvpL7e+A+IjjEn7/sycQpkTERv+gACTS5b\nNoKyYLn7AXBPXBvP/BS9eYNfcuH0KaiuJHreVbSXfYBe9xZq8Reg8oS5cuv6ou62sy8xd69v/gBm\nzEb/9SWYMdssyTJEls9+BeNpMybyFsDurah5V6MsgQ0Qqdh4SJuALj+COn0KGhvMifyebfKvRP/x\nN1B5AvWtH6JyLxtynKMlYpKLEOLcoCtOQOYkn+fUlJno3S+jGxvQZe+jLruy/+W7SqHyr0b//WV0\nQx0qxTb4e277GKZfgJo7v/vJi/PQ2z5B3/Ydnw9wvWsTAMn/+u/UNJ5B/+2P6AWLzF4Lviu3vENj\nWz+GVAecqcey9AGfeZqAzZlvJs23XkMlJkFb6+BLkHtR2VPRxw+ZQ2KAmuHbK1HxiahvfA8VG2eW\nn4lgETHnIoQ4N2ijy/yteUKWz/NqygzQBrrkRfND9crCvm/gaZ9/ldm+n82HPu9ZXQknjqIu8f0w\nVZcugAaXuYu9Z/udWyAji6j0TCxf/xdobkK/8WdzWXD2VHNvik8sV5rzJa//CfIWoHrvng+QslhQ\n1xXB8UMYr/zB7GFdeMnQbjIpB6orzfmaxCTImOjXxLJgUcQnFpDkIoToQR/eN3CdsJoq6OyACb3m\nNadON69//y1zDqaPUik9qYmTIH0ieteWwWPa/rF5zSWX+94jN9/ct7Gle2OhPtsG+3ehLjaHi9Sk\naajLP4Mu/V84tNe35+Mx+1KIiwelsHzljkHjGYhasAiSU+H4IffcTszQrvdM6m//BKbPDq4HFSEk\nuQghADOxGI/+EL3+zf4beVeK+SYXlWwDexpojVq4OKB5BjXtAnPH+CDnjOitH8PEyeZqqZ7XxyfA\nBbnorR9232PvTujs8JmLUEW3mwsQtIGa459c1LhxqC//E+qr30RlZPm9PhRqXDTq2i+YX/eVyAbj\nWfVlGH3WMjuXSHIRQgCgd281/1z7934/8L0HhPXuuQBMmQFKoRZeG9gbTp1pTlrXnO4/psYzcGCP\nX6/FQ+UtMM8qOXnMbL9rs1mOpsdchXKkob7wf2DydJjc98oty+IvYuk50T8MavEXUF+8FXV5EKuy\nUmyQlGLeZ7okl/OKLj/C2bIPwh2GECGn9+6AqCizd7J3R9+NTpVDqt3sNfRi+dxXUbd/F+UYH9D7\nqZxZ5vse3td/TDvLzB5HP3MM6pLLzeW7Wz8ylxrv3GQOR/XafW658etEPfhfAa/cGg4VG4/lS7f2\nu6BhwGuVMuddoqPNP89hklyGSK9/i4ZfrAx3GEKElD57Fg7vRRV8DhKTMdb+re92lSf67rVgTupb\nrrkh8DedONk8PXGAkyz11o/B5ux3r4hKtsG0C8x5l8qTZvXliyN3eW4gLJ/7Ouq276CsfZdnOVdI\nchmquHh0a/Oon0ctxIg6tAc6O82y8FdfD9vL0L2Gq7TWUFHuN98SLBUVBVOm99tz0WfPwp4tqEvm\nDzixrS69Ak4cQb/3uvk4gvd+BELNuhjLIKvtzgWSXIYqLh46O80VM0KMEd4hsemzzV3zCvR7b/g2\nqquFtlaYMLxJ757U1JlQfhjd0ce/p0+3QXu73xJkv3tc6j5T5d3XzerAAQ7LiZElyWWoPOOoo3hc\nqBAjTX+6A6bONDfn2dPgkivQH7yDbj/b3chdy8tTUywU1NRZ5i9rvQ7JAtDbPoK4BJh58cD3SMuA\nrCnm3Mw53msZSyS5DFWcWfGUNkkuYmzQLU1w7BDqgu4yKpZrPw/NjeiP16Hbz2L85UWM3/ynuZIp\n0CKJgfBM6vfeCNnVhd5ehsrN7/f0xp48vZdzfb5lLJHyL0OkYuPRAK2t4Q5FiNDYv8v8rf+COd3P\nzbzYPLzqjT+j3/izWZPris+gvvYvfa4UC5ayOcyyK4f3weIvdr+w/RNoOoOaF1hZfrX4C+aQ9ayB\nezli9EjPZaji3MNi0nMRY4Teu9Nc+uruRYC7/te1XzD3kERZsfzgJ1j+9fsB1QEbspyZfj0X492/\nm5syA6zNpRKSsFz35SFVMhYjS3ouQyVzLmKM0Xt3mBP5vfaGqKuuQznHw8yLR3RZrMqZhd7yIbqx\nAZWUYh5GtncH6qZ/lmRxDpOey1C551y09FzEOUC3tvR5Rrz39TN1cPKYz3yLh7JYULMvHfH9Fmqq\n+xTIw+6S/e/+HazjUFddN6LvK0aWJJehkp6LOIcYP/0B+ndP9fu63rsTwHe+ZbRNng4WC/rIPnRL\nM/rDd1Hzr0ElJYcvJjFsklyGKk6Sizg36MYGqDyJ/mR9d02w3vbuMJf7Tg5fqREVE2suHji8D/3h\nWjjbhrr282GLR4SGJJehso4Dq1Um9EXkO+7eO6K1eVZJH/TeHTDr4rDPbaips+DoAXMjZM4s1OTp\nYY1HDJ8klyFSSqHiE2Upsoh42p1c1JWL0R+vR1ed8n19y4fmEuPZl4YjPF85s8zRgNMnUYuk1zIW\nSHIJgiUuXnouIvKVHwbHeFTRHRAVhX79z96XdNUpjN89CVNmRMTEucpxT+onp5onQ4pzXtiXIjc1\nNVFcXEx1dTVpaWksW7aMxMREv3bbtm1jzZo1GIbB4sWLKSoyz1748MMP+dOf/sTJkyf56U9/yrRp\nfVdPDSUVn4CWORcR4XT5YcjOQaXaUVdfj17/JvqLt0BSCsavHgNlwfKd+/2WIIdF+kRIn4i6+rpz\nvhqwMIW951JSUkJubi5PPfUUubm5lJSU+LUxDIPVq1fzwAMPUFxczIYNGzhx4gQA2dnZ3HfffVx4\n4YWjFrOKSzAL+AkRoXRbK5w+hXKfCaJu+Kp57smbr6Bf+n9w4giWf10WMUUelcWCZcUzqOu/Eu5Q\nRIiEPbmUlZVRUFAAQEFBAWVlZX5tDh48SEZGBunp6VitVhYuXOhtl5WVRWZm5qjGrOITZLWYiGwn\njppHDrvrgCm7E7WwEL3uLfT7b6Nu/DoqwN3vo0UpdU6fGS98hT25NDQ0YLOZJSVSU1NpaGjwa+Ny\nuXA4HN7HDocDl8s1ajH2ZolPkDkXEdG0p8pwj9MM1ee+ChYLzMpFfem2MEUmzhejMueyYsUK6uvr\n/Z6/5ZZbfB6P9G8upaWllJaWArBq1SqcTmdQ92lKSMRyti3o60eS1WqNyLhAYgtWMLE1VJ3ibFIK\nzhkXdP+bcjrpfPIFopzjzb0lYYpttEhswQlVbKOSXB566KF+X0tJSaGurg6bzUZdXR3Jyf67cu12\nO7W1td7HtbW12O32IcdRWFhIYWH3CW81NTVDvgdATGwcRktz0NePJKfTGZFxgcQWrGBi69q/B7Km\n+Py7ASAmHhqbzP/CFNtokdiCM1hsgU5DhH1YLD8/n3Xr1gGwbt065s3zHweeNm0aFRUVVFVV0dnZ\nycaNG8nPzx/tUL1UXAJ0tA9Ys0mIcNGdnXDqmHcyX4hwCHtyKSoqYseOHdx7773s3LnTu8TY5XLx\n6KOPAhAVFcWSJUtYuXIly5YtY8GCBWRnm+d4f/LJJ3znO99h//79rFq1ipUrV454zBbPeRaykVJE\noopy83THbEkuInzCvs8lKSmJhx9+2O95u93O8uXLvY/z8vLIy8vzazd//nzmz58/ojH2puLd+3Da\nWkCK64kI492ZP2nk93wJ0Z+w91zORSpeileKCFZ+GKJjIH1CuCMR5zFJLkGwxLmHxWQ5sohA5s78\nqWEvRinOb5JcgqBkzkVEKG0YUH7Eu3lSiHCR5BIE5e65yGmUYqRpVzVtH5QGfkHNaXO4VibzRZhJ\ncglCd89FkosYWfqNP9PwxMMYH68L7IJyz2S+JBcRXpJcguBdiiw9FzHCPMcQ6xd/ha6uHLz98cNm\niZeJk0c6NCEGJMklGNEx5j9gmXMRI0g31EHlCeJuuAlQGM89YW6QHOia44dhQjZqXPToBClEPyS5\nBEEpBbHx0Noc7lDEGKb3mb2WuGtvRN2xFA7vQ//tpYEvOnlMJvNFRAj7JspzlpxGKUbavl0QG4c1\nZyYW23iM3VvQr/8JfeElqFkX+zXXLc1QVwOZk8IQrBC+pOcSrLh4tAyLiRGk9+2EGRehoszfAdUt\nd8H4TIwXnu77glPHzXaSXEQEkOQSrFjpuYiRo+tr4fRJ1Kxc73MqNg519fVQeRLdeMb/GndykZ6L\niASSXIIVFy9LkcWI0ft2AaAuyPV53rvE2HMYWE+njpuLTSLk6GJxfpPkEiQVGwdtMiwmRsi+nRCX\nAL0n592PdR/JRZ86bq4Us8g/axF+8n9hsGRCX4wgvW8nzLzIrz6YSkwGuxOO991zkfkWESkkuQQr\nVobFxMjQrhqoqkDN9F8RBkB2jresvvea5kZoqJPNkyJiSHIJVlwctJ9Fd3WFOxIxxuj95v6W3vMt\nHmpSDpw+iT7b1v3kSVkpJiKLJJdgxbrPdJF5FxFq+3ZBfAJkTenzZTUpB7SGE0e9z8lKMRFpJLkE\nK86TXGRoTISWOd9ycf/nsWSbJ0z6TOqfOg6xceZ8jBARQJJLkJSn5yIlYEQI6dpqqK7scwe+l90J\nCUk+k/relWJKjUKUQgxOkkuwPD0X2aUvQkjv2QqAmtn3fAu4a9tlT/Wd1D91HCWT+SKChL22WFNT\nE8XFxVRXV5OWlsayZctITEz0a7dt2zbWrFmDYRgsXryYoqIiAJ5//nk2b96M1WolPT2dpUuXkpCQ\nMPKBy7CYCDGtNfq912FCtv/+ll7UpBz02r+bVZJbm6GxQeZbREQJe8+lpKSE3NxcnnrqKXJzcykp\nKfFrYxgGq1ev5oEHHqC4uJgNGzZw4sQJAObMmcMTTzzBz372MyZMmMBrr702OoG7h8W0LEcWoXJg\nNxw/jCr84uDDW9k50NkBlSekppiISGFPLmVlZRQUFABQUFBAWVmZX5uDBw+SkZFBeno6VquVhQsX\netvNnTuXqChz4nPmzJm4XK7RCTwuzvxTei4iRIzS/4WEJNTliwZt6ykDo48fRp88Zj4pyUVEkLAP\nizU0NGCz2QBITU2loaHBr43L5cLhcHgfOxwODhw44Ndu7dq1LFy4sN/3Ki0tpbTUPI981apVOJ3B\nrayxWq04JmZTDSRYLCQEeZ+RYLVag/6+RprE1r/OypPUbvuYhJvuIHHiRJ/X+opN22xURccQV1OB\nbm+nLSEJ5/SZoz6hH+6f20AktuCEKrZRSS4rVqygvr7e7/lbbrnF57FSKuh/HK+++ipRUVFcffXV\n/bYpLCyksLDQ+7impiao93I6ndQ2NYNSNNdW0xrkfUaC0+kM+vsaaRJb/4xXXgCLhdbLF9HWK45+\nY5s4mZb9e8DogglZ1NbWjlK0AcQWASS24AwWW2ZmZkD3GZXk8tBDD/X7WkpKCnV1ddhsNurq6khO\nTvZrY7fbff7h1NbWYrfbvY/fe+89Nm/ezMMPPzxqv7l1n0Ypw2JieHRrC/qDt1GXXYWyOQa/wE1l\n56DL3geLBXVZ/z12IcIh7HMu+fn5rFu3DoB169Yxb948vzbTpk2joqKCqqoqOjs72bhxI/n5+YC5\niuwvf/kL999/PzExMaMaO3FxMucihk1v/Ae0taIKvzi0CyflmCvFmhtlvkVEnLDPuRQVFVFcXMza\ntWu9S5HBnGd59tlnWb58OVFRUSxZsoSVK1diGAaLFi0iOzsbgNWrV9PZ2cmKFSsAmDFjBnfdddfo\nBB8rp1EKf3rLh5AzC5VqH7yt0YX+x19h2gWoqTOH9D5qUg7a87UkFxFhwp5ckpKSePjhh/2et9vt\nLF++3Ps4Ly+PvLw8v3a/+MUvRjS+AcXFyw594UN3tGP8ehXqc19DfeWOwS/4dIe5I/8r3xj6m02c\nDBYLGAZMlOQiIkvYh8XOaXHxUrhS+GpuMotK1pwOqLnesxWsVtRc/+HgwajoGMjIgsQkSEod8vVC\njKSw91zOZSo2Hl1TFe4wRCRpaQJA1wb2/4XeuxNyLjATRRDUVddBY73UFBMRR5LLcMhplKI3d3Kh\ntnrQprq5CcoPo754a9BvZ7nuy0FfK8RIkmGx4YiNk8KVwlezew6uwYXu7Bi47f5doDVqVv9FKoU4\nV0lyGY7YeDjbijbkNEph0p6ei9bgGniTnN63E6KjYYirxIQ4F0hyGQ5vZeS2gduJ84cnuQAMMu+i\n9+6A6bNR48aNcFBCjD5JLsMRK8UrRS8t3UvTtav/eRfd2AAnj8mQmBizJLkMg5IDw0RvLU3mUJey\nwEArCfftBJDkIsasgFeL7dq1i/HjxzN+/Hjq6up48cUXsVgs3HbbbaSmnqdr7GPlwDDRS3MTJCaD\nZsBhMb13h9nznTJj9GITYhQF3HNZvXo1FovZ/A9/+ANdXV0opXj22WdHLLiI5+25SHIRJt3SBPGJ\n4EgbeFhs306YcRHKfRaREGNNwD0Xl8uF0+mkq6uL7du388wzz2C1Wvn2t789kvFFtrju0yhlC5sA\nzGGx+ERUqgN9eG+fTXR9LVSeRF19/SgHJ8ToCbjnEhcXR319PXv27CErK4vY2FgAOjs7Ryy4iCfD\nYqK3lmaz5+IcD3U1fS5T13s98y1zRjs6IUZNwD2XG264geXLl9PZ2ck3v/lNAPbu3cvEXqfmnVdk\nWEz01tx2XEW+AAAgAElEQVSEmpIAjjTo6oJ6F9jTfNvs2wnxCZA9JRwRCjEqAk4uRUVFzJ8/H4vF\nQkZGBmBWLv7Od74zYsFFvBiz9yY9F+HlGRazjzfL4ddW+yUXvXcHzMxFWWS+RYxdQ1qKnJmZ6U0s\nu3btor6+nkmTzt9S38pikRIwwkt3dkD7WfeE/njzuVrf6si6tgpqTqMukCXIYmwLOLk88sgj7N1r\nTlCWlJTw5JNP8uSTT/Lqq6+OWHDnhFgpXincPLvzE8zVYoBfAUu9bxcAaubFoxmZEKMu4ORSXl7O\nzJlmDaR//OMfPPLII6xcuZJ33nlnxII7J8TFy5yLMHmKVsYlmCX0k1L897oc3GPOt0ycPPrxCTGK\nAp5z0do8ULWyshKArKwsAJqbz/OTGGPj0NJzEeDtuaiERPOxYzy6d8/lwG6znphFimOIsS3g5DJr\n1ix++9vfUldXx7x55ql5lZWVJCUljVhw5wQ5jVJ4eOqKxXuSSxqcOOZ9WZ+pM/e3XFkYhuCEGF0B\n//p09913Ex8fz+TJk7n55psBOHXqFDfeeOOIBXcuUAlJ0Hgm3GGICOAtt+9OLsqRDq5qb6+fA5+a\nz8+4KBzhCTGqAu65JCUlcdttt/k8l5eXN+wAmpqaKC4uprq6mrS0NJYtW0ZiYqJfu23btrFmzRoM\nw2Dx4sUUFRUB8NJLL7Fp0yaUUqSkpLB06VLsdvuw4wqYzQnbPkZrHXFHzepdW9C1VVgKbgh3KOcH\n74R+gvmnIw062qGxHpJt5pBYdDRMnha+GIUYJQEnl87OTl599VXWr19PXV0dNpuNa665hptuugmr\nNfjTkktKSsjNzaWoqIiSkhJKSkq4/fbbfdoYhsHq1at58MEHcTgcLF++nPz8fLKysvjSl77ELbfc\nAsDrr7/On//8Z+66666g4xky7wdIAyRHVgFPY+3fYM9W9Nz5qNRRTLjnq+bePZcee108yWXqLJRV\nzm8RY1/Aw2IvvPACO3fu5Fvf+haPP/443/rWt9i1axcvvPDCsAIoKyujoKAAgIKCAsrKyvzaHDx4\nkIyMDNLT07FarSxcuNDbLj4+3tvu7Nmzo957UPa+l5xGhPpa6OpCbygNdyTnh5YmiI7pTh7u5ci6\npgrd0gzlR1EzZUhMnB8C7nJ89NFHPP74494J/MzMTKZOncoPf/hDbzmYYDQ0NGCz2QBITU2loaHB\nr43L5cLhcHgfOxwODhw44H38xz/+kfXr1xMfH88jjzzS73uVlpZSWmp+0K5atQqn0xlUzFar1Xtt\nx7SZuICkjlZig7xfKPWMrfpMPQagNryD4/Zvh70Cb8/YIk0oYmvo6qQ9Kdl7HyMulmogoa0Za/Up\n6rVBSv5CYob4PmP95zZSJLbghCq2IS9FDsaKFSuor6/3e94znOWhlAqq53Hrrbdy66238tprr/Hm\nm296Fxz0VlhYSGFh90qdmpqBzzjvj9Pp9F6rLeZvqWeOHqZpZvgLEXpi052dGA11kDUF48RRat57\nCzV3fkTEFolCEVuXqxZi433vE59Ac/kRqD4NUVGccWSghvg+Y/3nNlIktuAMFltmZmZA9wk4uSxY\nsIDHHnuMr33ta943f+WVV7jiiisGvfahhx7q97WUlBTvHE5dXR3Jycl+bex2O7W1td7HtbW1fU7a\nX3311Tz66KP9JpcREZ9gloAZ4OyOsDhTB4C65gb031/GWPcmUWFOLmNeS5P5/0NPdvdel5ZmmDQN\n5alHJ8QYF/Ccy+23305ubi6rV6/mRz/6Eb/97W+56KKLuOOOO4YVQH5+PuvWrQNg3bp13j00PU2b\nNo2Kigqqqqro7Oxk48aN5OfnA1BRUeFtV1ZWFnBWDRWlFNjT/DbLhV29CwDlSDPPDdm1GV1dGeag\nxjjPQWE9OdKg8gQc3S/zLeK8MmDPZdeuXT6PL7roIi666CKfZbd79+7l4ouDr5NUVFREcXExa9eu\n9S5FBnOe5dlnn2X58uVERUWxZMkSVq5ciWEYLFq0iOzsbABefPFFKioqUErhdDpHd6WYh2M8uAY4\nLz0c3MmFVDvq6uvQf38Z/f5bqJv+ObxxjWUtTajsHJ+nlDMdvf0T82vZ3yLOIwMml1/96ld9Pu9J\nLJ4k88tf/jLoAJKSknj44Yf9nrfb7Sxfvtz7OC8vr899Nffdd1/Q7x0qypGGPrIv3GH40A09kkuy\nDebOQ39Qiv7SbbIUdqS0NJtFK3vqWW5/+oWjG48QYTRgcnn66adHK45zmz0NmhrRZ9siZ0y93gUW\nCySmAGApuAFj28foLR+i5l8T5uDGHt3VZZYB6jUs5t3rMnGyWc1BiPOEVM8LBe9elwgaGqt3QYq9\nu0Di7EshLQO9/q2ALtdGF12/XoU++OkIBjmGeOuK9ZrQd+91kSExcb6R5BICynN2RwStGNP1Luix\nK19ZLKiFi2HfzsAm9utdsHmjd75ADKJXXTGvjCzImoKad9XoxyREGElyCQW759TByEkuNJg9l57U\ngmtBKfRH7wVwvXtfUs3pgdsJk6fcfu9hsdg4oh55Sg4HE+cdSS6hkGqDqKg+ey769Cl0OKom17v8\n6okpRxrMykV/uHbwTbHufTJakktgmnsVrRTiPCfJJQSUJQpSHX5zLlprjJ89gPHiM6Maj+5oh+ZG\nn2ExD7VwMVRXwoE9A9/Ds9pMkktAepfbF+J8J8klVBx9bKSsrjTnLnZtMT/we9FGF0bJC+iqU6GN\npccel95U3gKIiUNv/MfA9/AMizWdQcthaIOT5CKED0kuIaLs4/2GxfThveYXZ9tg307/i/btcm9u\nfCe0wbh7HSqlj+QSE4vKX4jevAF9tq3/e7iHxQDpvQTCOywmyUUIkOQSOo40qK819zt4HNoHMXEQ\nHYPe4X+UgC573/zzyP7QxjJAzwVALVgMba3orR/2ewvdUAeeIqKRtMQ6UrU0w7ho1LjocEciRESQ\n5BIq9jQwjO4Pdtw9l5yZcOFc9PYyn0l03dmB3uL+cD92EG0YIQtFD5JcmDEbnOnojWv7v0lDHUyc\nbN5Pei6D66uumBDnMUkuIaIc5nJkz2/5+mwbnDiKyplllrp3VcPJY90XfLrdnHTPW2Du7D59MnTB\n1LvAaoV+doQri8Vclrx3B7q/vTkNdaiJkyE6RobFAqBbmv03UApxHpPkEiruXfraU8Dy6AEwDNS0\nC1C5ZgXnnhsS9SfvQ3wCls+bxwOEdGjMvcdloLNx1IJFoDX6w3f9XtNaw5l6c5+MM116LoFoaZL5\nFiF6kOQSKr2OO9aH3JP5ObPM/SaTp3vnXXT7WfS2j1CXLoCsKea8zNEDfdw0OL135/dFpWXAxMnd\ncfbU2gwd7ZCSCs506bkEQobFhPAhySVEVEwMJCZ7V4zpw/sgY6K3WKGaOx+O7EefqYddm6GtFTX/\nanOPzJTp6COhSy4EkFwASJvQ92S9Zxlyih3lTi7DOYn0vNDc5Lc7X4jzmSSXUHKMR7uqzQ/iQ3tR\nORd4X1Jz5pnDUDs3m0NiSSkwyzwWWU2ZAeVH0B0doYmjwYVKdQzaTDnH9504PKdYJrt7Lm2t3fs4\nRojetQW97eMRfY8R1Ve5fSHOY5JcQsnuNIfFqiug6QxMm9X92qQcSHWgP1mP3lmGuuxKVFQUAGrq\nDOjqhBNHhx2C0doCrS1+dcX65EyH9rNmrD3oBvcelxRb90KFER4aM15/GeN3T4UuwY4ibXSZQ4lx\nMqEvhIcklxBSDnMjpT5kHhzm03NRyuy97NkK7e2oeVd3XzhlJgD66PAn9Y26WvOLAIbFuhNHr6Ex\nb3IxJ/TNNiM879Laaq6e62M/UMRrbTH/lLpiQnhJcgklR5q5G3/nJoiNg8xsn5fV3HnmF6kO31MJ\n7U5IToUQrBgzXDXmewUy5+L0LJ/ulTga6sylzPEJ3uQSyIox3X52SLH6aDM/oI0PB9h7E6mapfSL\nEL0NeBKlGBplT0PjXnI87QJzsr6nC+ZAfCJqwWe6D/HCfWz0lBnooweHHUNXnXvfSiDJxeFOHLVV\n+CxaPlMHyTYzrvgE80NzkOSiN2/A+PVj5vc9/xpU/pXm8cqBam0BZYGdm9Bn6oZ2bbj1U25fiPOZ\n9FxCyTPM1H4WNe0Cv5dVdAyWn/wK9aXb/F+bOgMqT5ib8YbB03MJZM5FxcWbGy17JQ7dUAcpPT7c\nneno3kNnPdsbBsZf/ttcjt3Wiv7jbzDu+xe6nv5pQJUHtNZmz2XuPDAM9MfrB70mokjRSiH8hL3n\n0tTURHFxMdXV1aSlpbFs2TISE/3/kW7bto01a9ZgGAaLFy+mqKjI5/W//vWvPP/88zz33HMkJyeP\nVvi+PHtd8J1v6UklpfT9/JSZ5ofssYNw4dygQzDqas1d9XHxgV3gGO+fOBrqIC2j+7EzHU4d7/8e\n2z6CinLUnT/AcnkB+uRx9N//x6ydVu8yh/0G0tEOXV2onFnoepdZsfm6LwcWfwTQze5fCGS1mBBe\nYe+5lJSUkJuby1NPPUVubi4lJSV+bQzDYPXq1TzwwAMUFxezYcMGTpw44X29pqaGHTt24HQO8iE2\n0hKTIdpduDBn5tCunTIdYNhDY4arBlIH3p3vwznef6/LmXqfYSnlTIfaqj73umitMf7+Jxg/wXuU\nr5o4CXV5gftedX7X+HHPtxAbb543c+Io+vjhwOKPBK3ScxGit7Anl7KyMgoKzA+igoICysr8Vwsd\nPHiQjIwM0tPTsVqtLFy40Kfd73//e/7pn/4p8A/UEaKUMnsvPTZPBnxtYjKkZQx7xViXO7kE/L6O\n8VDbvddFd3aaS5NTUrsbOcebvYuGPhLF7i1w/BDqhq/6zjF5klNf1/TW6j4vJi4ONf9qsFoHP28m\nknh6LpJchPAK+7BYQ0MDNpv5QZSamkpDQ4NfG5fLhcPRvSnQ4XBw4IC5o72srAy73c6UKVMGfa/S\n0lJKS0sBWLVqVdA9HavV2u+1rV//JowbR1wQ9264IJf2PduH1QOrqashJmcWqQHeo2XKNBrfacc+\nLoqoVDtdtdXUaE3ixEnEu+9xNmcG9UBK51mie9xXa03dW6+BMx3nF76OGjfO+1oXBjVAQle79z79\n/dw6GmpwAcnjM4idPJX6eVfTXvY+jm/f53PPkTTQ3+lgGnUXLdZxODMzR+QXnOHENtIktuCcD7GN\nSnJZsWIF9fX1fs/fcsstPo+VUkP6x3n27Flee+01HnzwwYDaFxYWUlhY6H1cU1MT8Hv15HQ6+792\nzuUANAdxb2PCJPT771B9cH9gS4l70VqjXTW0X5QX8PemY8y9Ga79n5pzHscOAdAcZaXFfQ8dHQdA\n/cF9WJwTuq/dtwtj7w7UrXdR2+uXAt1pnmvTdPKE9z79/dx0hXkSZ2NHJ001NejLrkJ/+C41695C\nXXJFwN//cAz4dzoIo7Ya4hOora0NcVSm4cQ20iS24JzLsWVmZgZ0n1FJLg899FC/r6WkpFBXV4fN\nZqOurq7PyXi73e7zD7e2tha73c7p06epqqrihz/8off5+++/n0cffZTU1FS/+0Q6NXUGGsxJ/dT5\ng7bXB/Zg/ON/sXzjHlR8grlSq611aInJvddF11ahcmZ1D2P1XArc6zgBD+P1lyE5FXXVdf7fi3Wc\nOQfV4PJ7zY9nzsWzCOGiSyEpBeOj94gapeQyLM1StFKI3sI+55Kfn8+6desAWLduHfPmzfNrM23a\nNCoqKqiqqqKzs5ONGzeSn5/PpEmTeO6553j66ad5+umncTgcPPbYY+dkYgFggrnpUp8+FVBzvfVD\n2LwR49nHzLkSzyFhgZR+8ei1S79n6RcPFR1jPu6xZFnv2wl7tqGu+7L5el9SbN33G+j7aOme0AdQ\nVivqksth91Z0Z2DlYPTJY+bPIAy0lNsXwk/Yk0tRURE7duzg3nvvZefOnd4lxi6Xi0cffRSAqKgo\nlixZwsqVK1m2bBkLFiwgOzt7oNuem+ITzfpUNZUBNddVFebqtD3b0H/8DdSbvbtAilZ6qNg4s4fh\nSRx99VzA51wX3dKM8dufmyvEFn2+/5un2AKb0O/dcwHzDJy2VjiwZ9DLjbL3MX58D3rzhn7b6I72\nweMIVkuz9FyE6CXsE/pJSUk8/PDDfs/b7XaWL1/ufZyXl0deXt6A93r66adDHt9oUkpBWjq6OrDk\nQnUlzL4UNSEL/cYr6FPuky6HOl/jGI/2lIA5U2dWEeg1ka4c6eaxzYD+47NQX4vl/sdQMbH9fz8p\nNnRlACdstvr2XABzr4/Vit65CTXAvh9dXYn+wy/NB9UVfbfZvwuj+BEsjzyJysgaPJ6hamlCpU8M\n/X2FOIeFvecieknLgOoA6ngZBlRXotIyUEV3wGUL4eCn5ospQyyd0mOvi26o7/t6p1mU0/joPfRH\n76G+cIs5RzOQZBucqRv8LJi2VrBafRKaio2DmbnonZv6vUx3dmD85nGwWMxabnV9T6jr8iPQ2YHe\nvHHgOIKgqyrMM3w8ddqEEIAkl4ijnBnmvhOja+CG9S5z78n4CSiLBcuSZTB1JpYUm/nBPJT3dKRD\nrfscmgZXP8kl3SzN8odfmqdr3vj1wW+caoPOzsHPgmlr8e21eOKakw+VJ9FVfc9B6ddegKMHsHzj\nHhg/Ad1PcsFdEqfnMdOhoktegCjrwMODQpyHJLlEmvEZ5gdy3SCrrNxDQGq8uTRYRcdg+cFPsD36\n7NDf05luJqoz9X678z2Up/S+xYLlX5d5z6IZkGdhQf0g8y6tLX2Wq1G5+QDoHf69F71zM/rt11Cf\n+RzqsoVgc/bbc6HOvazyyH50feiWC+tjB9Fl75uLGoJYOi7EWCbJJcIop7um1yCT+rrKPb+Q1r3v\nRMXEYp0w9DkF5exxIFhDne/ufI+JkyEuHnXbt1HjA1vn7k1Sg5SA0W2t5rBW7+vHT4CMiX5DY7qx\nAWPNzyFrCurrS8y2NgfU9702X9fVeBOd3h6682KMV34PiUmoz94UsnsKMVZIcok07oKRg07qV1dA\nVJRPscyguZcj6xNHzZMp+xgWU8mpWH7+IpaFiwO/r/s+erC9Lq0t/Z7iqObMg/27zATkpl9eDS3N\nWO78Qfcy6FQHNDX2faaMqwZ14RyzvE6Ihsb0nq3w6XbU5/+PWV1aCOFDkkuksaeZE9SDJBddVQGO\n9MCGpwbj2ety2DxBs78FAX7n0wzGc58G/+oMPlqb+63irHLzzWHCT7cDoHdtMRcUfO6rqImTuxva\n3Muvew17aaPLfM7mRM29HD7d7pOogqENw+y1OMajCj43rHsJMVZJcokwKirK/LAftOdSCeMnDNwm\n0PeMiYWkFLQ7uYTsoK7YOLP8/2A9l7bW/hchTJ8NcfHonZvQZ9swXngGMrJQN97s00zZ3LWQes+7\nNNSDYYDdibpkPnR2wO6tQX5DJl32Phw/jCq6fdRqnwlxrpHkEonSMgYcFtNaQ1WFdzI/JJzpUOk+\nxmCoS5n7oZQKbCNlPxP6YO7WZ/Yl6B2b0CUvQm0Vlm98z/9D3d1z8Vsx5p7MV7Y0M1ElJKG3fxzU\n9+Oh3/izOd8z/5ph3UeIsUySSwRSzoyBJ/QbG8y9IT0P9Bruezp67NMIUXLx3GvQEjD9LEX2ULnz\noMGFLv0LquAG1IzZ/o08VQn6SS7YnaioKFRuvpmougZZ6t0P7aqGk8dQCxb5HFUthPAl/zoiUVq6\nOTnd35HHVb7LkEPCk1yirKEtZTJIz0V3dJhzKgPszVG57soMqXbUTf/cd5vYOIhP6E4mnvt7jn12\n92zUJfOhubF7w+kQafeQmrrosqCuF+J8IcklAinP8uJ+ei/eIbNQD4uBWeU4hL+Rq+RBhsX6qCvW\n1z3ULd/C8u3/MKs/9yfV0fewWHQ0eA5vu+hSs6xMkENjetcWc09N5hisbSdECElyiURp7g/6/srA\nVFWAsoAjPWRv6d3rkhziitIpNmht7nuJMPRdV6wPlsVfRE3vYzisJ5vDr+eCqwZsad5zglRsPFww\nF73t48HL0vSiu7rM5ccX54X91FMhIp0kl0jk9Ox16bsQI1UV5hxCKFcqeRJVqHeae+7XX+/F3XNR\n8cPfK6JsTv+lyHU1YPc9VU/NnW+utvMsYAjUkX3Q2oy6aOACqkIISS4RScUnQGJSvz0XXV0R2iEx\nAIe5GVOFuOfSvUu/n70ure49J4P0XAJic8CZet9zXVw1fkcQqFxzvkTv2jKk2+tdW8w9SBfOGXao\nQox1klwilTMD3d+KseqK7nmZEFHRMahrv4DKWxjS+3ZvpOxnr0ure9FCKHa525ygtbeXpLu6zK97\n91wc42FCNnrX5iHdXu/aYhbtlLNbhBiUJJcIpdIy+txIqZuboKnRLHAZYpZb70JdHOIhn1RPCZi+\nh8V0W2BzLoHw9lA88y4NLtCGX3IBzO9z/y702baA7q3P1MOxgzIkJkSAJLlEqrQMqK3y34/hqYYc\n4p7LiElMNhcf9Dfn4hkWixvaMQF96r2R0tVjA2Uv6uLLzCXQe3cGdGu9Z5v7OkkuQgRCkkukcp+f\ngqva5+kRWYY8gpQlylyBNsiEfmjmXDwlYNznt3iSjK2PY59nXAQxsYEPje3eYibKSdOGH6cQ5wFJ\nLhHKu0Gy99CYt9R+6IfFRkxKav+79FtbzOrO46KH/z7xCWYtM09SqXMn5r6GxcaNgwvmoHdtHnRJ\nsjYM9O6tqIsulV35QgTIGu4AmpqaKC4uprq6mrS0NJYtW0Ziov+E6bZt21izZg2GYbB48WKKiooA\nePnll/nHP/5BcnIyALfeeit5eWNg6MKzHLmmEp8dFVUVkGIf8Oz6iJNiH7jnEhsfkn0jSimz91Lf\nY1gsJq7/cv4X55kl+E+fhLQBji4oP2KW3JEhMSECFvbkUlJSQm5uLkVFRZSUlFBSUsLtt9/u08Yw\nDFavXs2DDz6Iw+Fg+fLl5Ofnk5VlHoz1+c9/ni996UvhCH/k2OxgtUKVb8/FXIZ8DvVaAJViQx8/\n3PeLra2hWSnmkWo397bgHhazO/tNXOriy9C4V4FdfEm/t/QMnanZl4YuTiHGuLD38cvKyigoKACg\noKCAsjL/kwIPHjxIRkYG6enpWK1WFi5c2Ge7sURZosCR7r8cuaoytDXFRkOyDRrr+ywWqQcpWjlU\nqudxx3U1fc+3eNo60yEja9B5F739E5g8PeR7gIQYy8KeXBoaGrDZzOWqqampNDQ0+LVxuVw4HN0f\nEg6HA5ere9/Em2++yX333cczzzxDU1PTyAc9WtLSfTZS6rNt5vLac2WlmEeqDQwDo9H/79Ystx+C\nlWIeNodZQdnoMjdQ2vznW3pSF18G+/pfkqyrK+HIflT+laGLUYjzwKgMi61YsYL6ev8d2rfccovP\nY6XUkMfer7/+er72ta8B8D//8z/84Q9/YOnSpX22LS0tpbS0FIBVq1bhdA78wdMfq9Ua9LVDcSZ7\nKm3r3sLhcKCUouPoQVxA8rSZxPbz/qMV21C0ZU2iAbA01uPMzvF5rbajHYvNgS1EMbdkT6axqws7\nBjVn6ojPmkTiAPc+e+VnqC/9C12fbsd5yeV+rze/93eaAMdnv0xUmH6ukfh36iGxBed8iG1UkstD\nDz3U72spKSnU1dVhs9moq6vzTsz3ZLfbqa3trhlVW1uL3W7WrEpN7R6qWLx4MY899li/71VYWEhh\nYaH3cU1NTb9tB+J0OoO+diiMxBR0SxM1a99AV5R7y703xiXS1M/7j1ZsQ6GV+b9Ze3UVjXG+f79d\nTWdQzvSQxazHxQDg2voJaE1LTDxtA9xbp2dDdAwtZRtoz/JdZqy1xlj7OkyfTZ2yQph+rpH4d+oh\nsQXnXI4tMzMzoPuEfVgsPz+fdevWAbBu3TrmzZvn12batGlUVFRQVVVFZ2cnGzduJD8/H4C6uu5V\nSJ988gnZ2WOnFLpnbsX45U/Qr/weaqtQVxbCxCnhDWyo3CVgjN4Vi8EcFgvhnItnr4v3yObBhsXG\nRcOsXNo3b0Qbhu+LJ49CRTnqcjlxUoihCvtqsaKiIoqLi1m7dq13KTKY8yzPPvssy5cvJyoqiiVL\nlrBy5UoMw2DRokXeJPLCCy9w9OhRlFKkpaVx1113hfPbCa2L81C3fduceJ4yA5WUEu6IguNJLvV9\n1Bdraw39nAugD+01H/exx6U3dXkBXc89gfpwrZm83fQn6yEqCnXZVaGLT4jzRNiTS1JSEg8//LDf\n83a7neXLl3sf5+Xl9bl/5Z577hnR+MJJWcehFn0+3GEMm4qOgbgEunqfEtnZAR3toe25JKaYp2ke\nO2Q+DiS5zL8G6wfv0PHK79GXXIFKSDQ3Tn7yPlx4CSrJf6hWCDGwsA+LifNESiqGq9cpkW2eumID\nnC45RMpiMc+Q6eyAuATzcLDBrlGKpLu+bx4t/b//bT55eK85DClDYkIERZKLGB0pdv9hMc8plKEc\nFoPuvS0D7HHpbdzUmaiCG9Dvvo4+ccQcEouORvWxgkwIMThJLmJUqBSb/4S+O7kE0rsY0nt5JvED\nGBLzua7onyAhAePFZ9GbNqDmzA95bEKcLyS5iNGRYqPLVeNbJNJTETmU5V/A22MZbKVYbyohCXXT\nP8PBPdDYIENiQgyDJBcxOsZPgPaz3aVZILRHHPfkGQ4bYs8FMFeLTZkB8Ylw0WWhjUuI80jYV4uJ\n84PKyEIDVJ7wfuh7T6EM8ZyLsjnN9xpizwXMBQGWex6EpkazLL8QIijScxGjI2MiAPr0ye7nWkN4\nUFhPEyeb+1OypwZ1uUq2oTInhTYmIc4z0nMRoyPFjoqLh8oeyWWE5lxURhaWp14y99cIIcJCei5i\nVCiliJo4CV15ovvJ1lawWMzTI0P9fpJYhAgrSS5i1FgzJ/n3XEJ0CqUQIrJIchGjJiprMriqu89O\naW0J/TJkIUREkOQiRo01c7L5xelTAOjWFogN8e58IUREkOQiRk1UlplcvCvG2qTnIsRYJclFjBpr\nRnfjEKkAAA4iSURBVBYoBRXuSf1Qn+UihIgYklzEqFExMWBPA2/PpdVcniyEGHMkuYjRNSGrezly\nm8y5CDFWSXIRo0qlT4TTp8wClq0tIT3LRQgROSS5iNGVkQVn26C2yixkGeqzXIQQEUGSixhVyl1j\njKMHzD9lQl+IMUmSixhdGVkA6CPu5CIT+kKMSWEvXNnU1ERxcTHV1dWkpaWxbNkyEhMT/dpt27aN\nNWvWYBgGixcvpqioyPvaG2+8wVtvvYXFYiEvL4/bb799NL8FMRQpNoiNQx/dD4T+FEohRGQIe3Ip\nKSkhNzeXoqIiSkpKKCkp8UsOhmGwevVqHnzwQRwOB8uXLyc/P5+srCx27drFpk2bePzxxxk3bhwN\nDQ1h+k5EIJRSkD4Rjh0yn5A5FyHGpLAPi5WVlVFQUABAQUEBZWVlfm0OHjxIRkYG6enpWK1WFi5c\n6G339ttv8+Uvf5lx7oOdUlJSRi94ERQ1wT2pDzLnIsQYFfaeS0NDAzabDYDU1NQ+ex4ulwuHw+F9\n7HA4OHDAHLOvqKhg7969vPTSS4wbN4477riD6dOnj07wIjjpE7u/ljkXIcakUUkuK1asoL6+3u/5\nW265xeexUmrI5dcNw6CpqYmVK1dy6NAhiouL+eUvf9nnfUpLSyktLQVg1apVOJ1DPwYXwGq1Bn3t\nSDsXYmubeSGeXyHsmVlEOcIf77nwc4tEEltwzofYRiW5PPTQQ/2+lpKSQl1dHTabjbq6OpKTk/3a\n2O12amtrvY9ra2ux2+3e1+bPn49SiunTp2OxWGhsbOzzPoWFhRQWFnof19TUBPX9OJ3OoK8daedC\nbDq+++/G1dqGioB4z4WfWySS2IJzLseWmZkZ0H3CPueSn5/PunXrAFi3bh3z5s3zazNt2jQqKiqo\nqqqis7OTjRs3kp+fD8C8efPYvXs3AKdOnaKzs5OkpKTR+wbE0I2fYBawVApiYsMdjRBiBIQ9uRQV\nFbFjxw7uvfdedu7c6V1i7HK5ePTRRwGIiopiyZIlrFy5kmXLlrFgwQKys7MBuPbaazl9+jQ/+MEP\nePLJJ7n77rvlZMMIp6JjwDFeTqEUYgwL+4R+UlISDz/8sN/zdrud5cuXex/n5eWRl5fn185qtXLv\nvfeOaIxiBGRMhJPHwx2FEGKEhD25iPOT5bM3oWurwh2GEGKESHIRYaEumIMMiAkxdoV9zkUIIcTY\nI8lFCCFEyElyEUIIEXKSXIQQQoScJBchhBAhJ8lFCCFEyElyEUIIEXKSXIQQQoSc0lrrcAchhBBi\nbJGeSxB+9KMfhTuEfklswZHYgiOxBed8iE2SixBCiJCT5CKEECLkon784x//ONxBnItycnLCHUK/\nJLbgSGzBkdiCM9Zjkwl9IYQQISfDYkIIIUJOznMZom3btrFmzRoMw2Dx4sXeY5nD4ZlnnmHLli2k\npKTwxBNPANDU1ERxcTHV1dWkpaWxbNkyEhMTRz22mpoann76aerr61FKUVhYyI033hgR8bW3t/PI\nI4/Q2dlJV1cXV1xxBTfffHNExAZgGAY/+tGPsNvt/OhHP4qYuADuvvtuYmNjsVgsREVFsWrVqoiJ\nr7m5mV//+teUl5ejlOK73/0umZmZYY/t1KlTFBcXex9XVVVx8803U1BQEPbYAP72t7+xdu1alFJk\nZ2ezdOlS2tvbhx+bFgHr6urS3/ve93RlZaXu6OjQ9913ny4vLw9bPLt379aHDh3S3//+973PPf/8\n8/q1117TWmv92muv6eeffz4ssblcLn3o0CGttdYtLS363nvv1eXl5RERn2EYurW1VWutdUdHh16+\nfLnet29fRMSmtdZ//etf9c9//nP96KOPaq0j5+9Ua62XLl2qGxoafJ6LlPh+8Ytf6NLSUq21+ffa\n1NQUMbF5dHV16TvvvFNXVVVFRGy1tbV66dKl+uzZs1prrZ944gn97rvvhiQ2GRYbgoMHD5KRkUF6\nejpWq5WFCxdSVlYWtnhmz57t99tEWVkZBQUFABQUFIQtPpvN5p0UjIuLY+LEibhcroiITylFbGws\nAF1dXXR1daGUiojYamtr2bJlC4sXL/Y+FwlxDSQS4mtpaeHTTz/l2muvBcBqtZKQkBARsfW0c+dO\nMjIySEtLi5jYDMOgvb2drq4u2tvbsdlsIYlNhsWGwOVy4XA4vI8dDgcHDhwIY0T+GhoasNlsAKSm\nptLQ0BDmiMxhgCNHjjB9+vSIic8wDO6//34qKyv57Gc/y4wZMyIitt/97nfcfvvttLa2ep+LhLh6\nWrFiBRaLheuuu47CwsKIiK+qqork5GSeeeYZjh07Rk5ODt/85jcjIraeNmzYwJVXXglExt+r3W7n\ni1/8It/97neJjo5m7ty5zJ07NySxSXIZw5RSKBXek+rb2tp44okn+OY3v0l8fLzPa+GMz2Kx8Pjj\nj9Pc3MzPfvYzjh8/HvbYNm/eTEpKCjk5OezevbvPNuH+O12xYgV2u52GhgZ+8pOfkJmZ6fN6uOLr\n6uriyJEjLFmyhBkzZrBmzRpKSkoiIjaPzs5ONm/ezG233eb3Wrhia2pqoqysjKeffpr4+Hj+67/+\ni/Xr14ckNkkuQ/D/t3dvIVG1exzHvzo6WIrjOIMMWGKIVGQKoZmhkXSAMChFhYpqysrwVNhFXXUT\nYZB2sARNkgzMUALJi+giyggvJJrQsimLFGQyHR3RckbntC/E4XW3373H3fjOEP/P1Rxc6/mxmOWf\n51lrPU90dDTj4+Oe9+Pj40RHR/sx0a9UKhUWiwW1Wo3FYiEyMtJvWRwOBzU1NWRlZZGenh5w+QDC\nw8PZsGEDb9++9Xu2jx8/8vr1awwGA3Nzc1itVmpra/2e668Wfu8qlYq0tDQ+f/4cEPk0Gg0ajYbE\nxEQAtmzZQkdHR0BkW2AwGFizZg1RUVFAYJwLfX19xMTEeNpOT0/n06dPPskm11yWICEhgW/fvjE6\nOorD4aC7u5vU1FR/x1okNTWVrq4uALq6ukhLS/NLDrfbTX19PbGxsezduzeg8k1NTfHz509g/s6x\n3t5eYmNj/Z7t4MGD1NfXU1dXx9mzZ0lKSqKiosLvuRbYbDbPcJ3NZqO3t5e4uLiAyBcVFYVGo8Fk\nMgHz/zRXrVoVENkW/HVIDALjXNBqtQwMDDA7O4vb7aavr89n54I8RLlEb968obm5GZfLRXZ2Nnl5\neX7LcuPGDfr7+5menkalUlFYWEhaWhrXr1/HbDb79fZGo9HIxYsXiYuL83SpDxw4QGJiot/zDQ0N\nUVdXh8vlwu12k5GRQX5+PtPT037PtuD9+/d0dnZy4cKFgMn1/ft3qqurgflhqMzMTPLy8gIm3+Dg\nIPX19TgcDmJiYigpKcHtdgdENpvNRklJCbdv3/YMDwfKcWtra6O7uxuFQkF8fDynT5/GZrP9djYp\nLkIIIXxOhsWEEEL4nBQXIYQQPifFRQghhM9JcRFCCOFzUlyEEEL4nBQXIbxQWVn5t0/NLzez2czh\nw4dxuVx+aV+I/4fciizEErS1tTEyMkJFRcWytVFaWkpxcTHJycnL1oYQy016LkL8g5xOp78jCPGP\nkJ6LEF4oLS3l+PHjnifUQ0JC0Ol0XL16lZmZGZqbmzEYDAQFBZGdnU1hYSHBwcG8ePGCZ8+ekZCQ\nwMuXL9m9ezfbt2+noaGBoaEhgoKCSElJoaioiPDwcG7dusWrV68ICQkhODiY/Px8MjIyKCsro7W1\nFYVCwcTEBI2NjRiNRiIiIti3bx87d+4E5ntWw8PDKJVKenp60Gq1lJaWkpCQAEBHRwdPnjzBarWi\nVqs5ceIEGzdu9NtxFX8umbhSCC+FhoaSm5v7y7BYXV0dKpWK2tpaZmdnuXLlChqNhl27dgEwMDDA\n1q1baWxsxOl0MjExQW5uLuvXr8dqtVJTU0N7ezt6vZ7y8nKMRuOiYbHR0dFFOW7evMnq1atpaGjA\nZDJx6dIldDodSUlJwPzsyufOnaOkpISHDx/S1NTE5cuXMZlMPH36lKqqKqKjoxkdHZXrOGLZyLCY\nEL9hcnISg8GAXq8nLCwMlUpFTk4O3d3dnr9Rq9Xs2bMHhUKBUqlEp9ORnJxMaGgokZGR5OTk0N/f\n71V7ZrMZo9HIoUOHUCqVxMfHs2PHDs8kgwDr1q1j06ZNBAcHs23bNgYHB4H5ZQbsdjvDw8Oe+bd0\nOp1Pj4cQC6TnIsRvMJvNOJ1OTp065fnM7XYvWlROq9Uu2mZycpJ79+7x4cMHbDYbLpfL60kBLRYL\nERERrFixYtH+v3z54nmvUqk8r5VKJXa7HafTiU6nQ6/X097ezvDwMCkpKRw5ciTglo0QfwYpLkIs\nwb8vmqTRaAgJCeHu3bsoFAqv9tHa2gpATU0NERER9PT00NTU5NW2arWaHz9+YLVaPQXGbDZ7XSAy\nMzPJzMxkZmaGO3fu0NLSQnl5uVfbCrEUMiwmxBKoVCrGxsY81yrUajUpKSncv3+fmZkZXC4XIyMj\n/3WYy2q1EhYWxsqVK5mYmKCzs3PR91FRUb9cZ1mg1WpZu3YtDx48YG5ujqGhIZ4/f05WVtb/zG4y\nmXj37h12ux2lUolSqfT7SqXizyXFRYglyMjIAKCoqIjz588DUFZWhsPhoLKykmPHjnHt2jUsFsvf\n7qOgoICvX79y9OhRqqqq2Lx586Lv9+/fz6NHj9Dr9Tx+/PiX7c+cOcPY2BjFxcVUV1dTUFDg1TMx\ndrudlpYWioqKOHnyJFNTU/9xyV0hfEFuRRZCCOFz0nMRQgjhc1JchBBC+JwUFyGEED4nxUUIIYTP\nSXERQgjhc1JchBBC+JwUFyGEED4nxUUIIYTPSXERQgjhc/8CTyDNyM3Ri3EAAAAASUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x1278b4d30>"
+       "<matplotlib.figure.Figure at 0x7f606bf73588>"
       ]
      },
      "metadata": {},
@@ -673,9 +1086,9 @@
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nyo892QAAAABJRU5ErkJggg==\n",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAENCAYAAAD+CUlOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x127a02390>"
+       "<matplotlib.figure.Figure at 0x7f606beafd68>"
       ]
      },
      "metadata": {},
@@ -698,8 +1111,9 @@
   }
  ],
  "metadata": {
+  "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "Python [default]",
    "language": "python",
    "name": "python3"
   },
@@ -713,7 +1127,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.5.3"
+   "version": "3.5.2"
   }
  },
  "nbformat": 4,
diff --git a/pictures/DNNforQ.png b/pictures/DNNforQ.png
new file mode 100644
index 0000000..94e53a7
Binary files /dev/null and b/pictures/DNNforQ.png differ
diff --git a/pictures/p123.png b/pictures/p123.png
new file mode 100644
index 0000000..226a20a
Binary files /dev/null and b/pictures/p123.png differ
diff --git a/pictures/p4.png b/pictures/p4.png
new file mode 100644
index 0000000..0ee566a
Binary files /dev/null and b/pictures/p4.png differ
diff --git a/pictures/p5.png b/pictures/p5.png
new file mode 100644
index 0000000..ee672b3
Binary files /dev/null and b/pictures/p5.png differ
diff --git a/pictures/p6.png b/pictures/p6.png
new file mode 100644
index 0000000..71c499c
Binary files /dev/null and b/pictures/p6.png differ
diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py
index 99fecf3..f407400 100644
--- a/policy_gradient/policy.py
+++ b/policy_gradient/policy.py
@@ -30,6 +30,9 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session):
         Sample solution is about 2~4 lines.
         """
         # YOUR CODE HERE >>>>>>
+        fc1 = tf.contrib.layers.fully_connected(self._observations, num_outputs=hidden_dim, activation_fn=tf.tanh)
+        fc2 = tf.contrib.layers.fully_connected(fc1, num_outputs=out_dim, activation_fn=None)
+        probs = tf.nn.softmax(fc2)
         # <<<<<<<<
 
         # --------------------------------------------------
@@ -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..3731d00 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 >>>>>>>>>>>>>>>>>>>
+    new_b = np.append(b[1:], 0)
+    return x + discount_rate * new_b
     # <<<<<<<<<<<<<<<<<<<<<<<<<<<<
  
 def plot_curve(data, key, filename=None):
diff --git a/report.md b/report.md
index 1e5017e..092143f 100644
--- a/report.md
+++ b/report.md
@@ -1,3 +1,57 @@
 # Homework3-Policy-Gradient report
+## problem1 construct a neural network to represent policy
+   In more complex tasks(atari games, and even in real-world tasks), it's hard to apply policy iteration/ value iteration directly due to large state/action space, requiring large storage and hard to calculate the Q values for all. So we "learn" the Q values or plicy by neural network. Here in problem 1, we want to use a simple neural network <img src="https://latex.codecogs.com/gif.latex?f_{Q^*}(s,%20a;\Theta)%22"> to represent <img src='https://latex.codecogs.com/gif.latex?Q^*%20(s,%20a)'> where <img src='https://latex.codecogs.com/gif.latex?\Theta'> is the parameters of the nerual network, just like the figure showed below:
+   <img src='pictures/DNNforQ.png' width='300'>
+   
+   To implement this, I added two fully connected layers in policy.py file:
+```python
+    fc1 = tf.contrib.layers.fully_connected(self._observations, num_outputs=hidden_dim, activation_fn = tf.tanh)
+    fc2 = tf.contrib.layers.fully_connected(fc1, num_outputs=out_dim, activation_fn=None)
+    probs = tf.nn.nsoftmax(fc2)
+```
+   
+   
+   where the nn's output ```probs```(output of nn) is the logits of each action's probability conditioned on the ```self._observations```(input of nn)
 
-TA: try to elaborate the algorithms that you implemented and any details worth mentioned.
+## problem2 compute the surrogate loss
+In reinforcement learning, our goal is to maximize the accumulated discounted reward <img src='https://imgur.com/zNVb7qv.png'> .
+We want to maximize the reward, it's the same as minimize the negative reward. In problem 2 here, I added a line to compute the <b>negative</b> surrogate loss <img src='https://imgur.com/vZ393ks.png'>. and <b>minimize</b> it.
+
+```python
+surr_loss = tf.reduce_mean((-log_prob)*self._advantages)
+```
+## problem3 
+In problem3, we're going to reduce the variance of the gradient estimate. To achieve this goal, we can change the ```Reward``` in surrogate loss into ```Reward-Basline```. This trick is somehow a similar idea with residual nets, which would help the learning more stable.
+```python
+a = r - b
+```
+## Verifying Solution
+After implemented problem 1, 2, and 3, I trained my agent and got the average return curve for episodes as below:
+<img src ='pictures/p123.png'>
+It's clear that the reward increases during training and terminated(converged) in 83 episodes.
+## problem4 compare results in problem 3 with no baseline
+<img src='pictures/p4.png'>
+The results without baseline converges faster(solved in 76 iterations) than the one with baseline. It's possible that in this task, the unstable of gradient somehow pushed the agent to act like "exploration" and thus finding the solution faster than with baseline subtraction one. 
+## problem5 actor-critic implementation with bootstrapping
+   In Actor-Critic algorithm, actors takes actions based on policy-iteration and critics evaluates the actions based on value-iteration(Q-learning). Actor-Critic alorithm combines actor and critic where actor improves the current policy, and critic evaluates(criticizes) the current policy. Here in problem 5, we changed the advantage function in problem3 into <img src='https://imgur.com/ovyr0s7.png'> using one-step bootstrap in policy_gradient/util.py
+```python
+   new_b = np.append(b[1:], 0)
+   return x + discount_rate * new_b
+```
+,which replaced the total reward by immediate reward and the estimated baseline.
+
+The boostrapping actor-critic agent doesn't converge since it's not stable enough.
+<img src ='pictures/p5.png'>
+## Problem6 generalized advantage estimation (GAE)
+Since the original actor-critic is not stable, in problem6, we introduce λ to compromise the advantage function based on problem3 and 5.
+Assume the <img src='https://imgur.com/qjRZqX8.png'> represent the i-step bootstrapping (e.g. <img src='https://imgur.com/jViBLdI.png'>). The generalized advantage estimation will be:
+
+<img src='https://imgur.com/6AQ2kDs.png'>
+
+Here we use ```util.discount``` to calculate the advantages by discount_rate and LAMBDA
+
+```python
+a = util.discount(a, self.discount_rate * LAMBDA)
+```
+The GAE agent converged in 73 episodes.
+<img src ='pictures/p6.png'>
\ No newline at end of file