From a9839e4127135169df339237bdbc2e9ea9487e41 Mon Sep 17 00:00:00 2001 From: wengbrian Date: Thu, 9 Nov 2017 16:15:40 +0800 Subject: [PATCH 1/4] updata code --- .DS_Store | Bin 0 -> 6148 bytes Lab3-policy-gradient.ipynb | 1186 +++++++++++++++++++++++++++++------- policy_gradient/policy.py | 30 + policy_gradient/util.py | 4 + 4 files changed, 1013 insertions(+), 207 deletions(-) create mode 100644 .DS_Store diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..3fc982675d181dc19018ab8fb09c70849f6dbded GIT binary patch literal 6148 zcmeHK!Ab)$5S_GDw}`X{!5(w*)Oco3xket?PH~)x$XHUVQ17XuJ5Zj@xr*%e-nOw8peSdHPkQ; z26~<&FX>{gUZ`CxO(t8rwX&?%Ytyov)a#Y9tZna1r;f9+S=~Flycyq3?xzorhHHVJ zrIv!l89br!#kOx-{WwzbHAc>6nPoFF1Iz$3@W&aj$C|VH$M?aH&kQgF-_HP@4-%Eo zwU`>zM+Y|a`$*$CLK3v;EvM?llS4Kwhg47>t;&{#MC literal 0 HcmV?d00001 diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..7ce9811 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -28,17 +28,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -46,16 +38,13 @@ "from policy_gradient import util\n", "from policy_gradient.policy import CategoricalPolicy\n", "from policy_gradient.baselines.linear_feature_baseline import LinearFeatureBaseline\n", - "\n", - "np.random.seed(0)\n", - "tf.set_random_seed(0)\n", - "\n", + "from gym.spaces import prng \n", "# CartPole-v0 is a MDP with finite state and action space. \n", "# In this environment, A pendulum is attached by an un-actuated joint to a cart, \n", "# and the goal is to prevent it from falling over. You can apply a force of +1 or -1 to the cart.\n", "# A reward of +1 is provided for every timestep that the pendulum remains upright. \n", "# To visualize CartPole-v0, please see https://gym.openai.com/envs/CartPole-v0\n", - "\n", + "seed = 1\n", "env = gym.make('CartPole-v0')" ] }, @@ -103,14 +92,16 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 3, + "metadata": { + "scrolled": 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", + "/Users/brian/anaconda3/lib/python3.6/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" ] } @@ -118,7 +109,8 @@ "source": [ "tf.reset_default_graph()\n", "sess = tf.Session()\n", - "# Construct a neural network to represent policy which maps observed state to action. \n", + "# Construct a neural network to represent policy which maps observed state to action.\n", + "# following function extract num feature and action from Box and Discreate\n", "in_dim = util.flatten_space(env.observation_space)\n", "out_dim = util.flatten_space(env.action_space)\n", "hidden_dim = 8\n", @@ -152,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -214,6 +206,7 @@ " Sample solution should be only 1 line.\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>\n", + " a = r-b\n", " # <<<<<<<<\n", "\n", " p[\"returns\"] = r\n", @@ -258,102 +251,202 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 14.85\n", - "Iteration 2: Average Return = 15.59\n", - "Iteration 3: Average Return = 16.61\n", - "Iteration 4: Average Return = 17.43\n", - "Iteration 5: Average Return = 17.08\n", - "Iteration 6: Average Return = 17.24\n", - "Iteration 7: Average Return = 21.3\n", - "Iteration 8: Average Return = 21.42\n", - "Iteration 9: Average Return = 20.62\n", - "Iteration 10: Average Return = 26.82\n", - "Iteration 11: Average Return = 28.0\n", - "Iteration 12: Average Return = 28.41\n", - "Iteration 13: Average Return = 28.96\n", - "Iteration 14: Average Return = 28.15\n", - "Iteration 15: Average Return = 30.64\n", - "Iteration 16: Average Return = 36.2\n", - "Iteration 17: Average Return = 38.13\n", - "Iteration 18: Average Return = 34.5\n", - "Iteration 19: Average Return = 40.37\n", - "Iteration 20: Average Return = 35.78\n", - "Iteration 21: Average Return = 47.81\n", - "Iteration 22: Average Return = 47.21\n", - "Iteration 23: Average Return = 43.34\n", - "Iteration 24: Average Return = 46.1\n", - "Iteration 25: Average Return = 50.25\n", - "Iteration 26: Average Return = 51.02\n", - "Iteration 27: Average Return = 59.81\n", - "Iteration 28: Average Return = 57.49\n", - "Iteration 29: Average Return = 61.39\n", - "Iteration 30: Average Return = 62.26\n", - "Iteration 31: Average Return = 61.98\n", - "Iteration 32: Average Return = 62.16\n", - "Iteration 33: Average Return = 59.89\n", - "Iteration 34: Average Return = 73.46\n", - "Iteration 35: Average Return = 78.51\n", - "Iteration 36: Average Return = 72.79\n", - "Iteration 37: Average Return = 78.74\n", - "Iteration 38: Average Return = 86.95\n", - "Iteration 39: Average Return = 94.08\n", - "Iteration 40: Average Return = 97.58\n", - "Iteration 41: Average Return = 103.42\n", - "Iteration 42: Average Return = 101.17\n", - "Iteration 43: Average Return = 112.39\n", - "Iteration 44: Average Return = 115.09\n", - "Iteration 45: Average Return = 134.65\n", - "Iteration 46: Average Return = 138.92\n", - "Iteration 47: Average Return = 147.15\n", - "Iteration 48: Average Return = 152.35\n", - "Iteration 49: Average Return = 149.66\n", - "Iteration 50: Average Return = 148.15\n", - "Iteration 51: Average Return = 144.82\n", - "Iteration 52: Average Return = 144.43\n", - "Iteration 53: Average Return = 153.21\n", - "Iteration 54: Average Return = 163.66\n", - "Iteration 55: Average Return = 154.28\n", - "Iteration 56: Average Return = 155.07\n", - "Iteration 57: Average Return = 161.53\n", - "Iteration 58: Average Return = 166.28\n", - "Iteration 59: Average Return = 174.05\n", - "Iteration 60: Average Return = 172.8\n", - "Iteration 61: Average Return = 170.78\n", - "Iteration 62: Average Return = 179.58\n", - "Iteration 63: Average Return = 174.84\n", - "Iteration 64: Average Return = 175.74\n", - "Iteration 65: Average Return = 174.99\n", - "Iteration 66: Average Return = 187.7\n", - "Iteration 67: Average Return = 178.94\n", - "Iteration 68: Average Return = 182.74\n", - "Iteration 69: Average Return = 181.42\n", - "Iteration 70: Average Return = 182.19\n", - "Iteration 71: Average Return = 184.58\n", - "Iteration 72: Average Return = 181.9\n", - "Iteration 73: Average Return = 184.29\n", - "Iteration 74: Average Return = 188.8\n", - "Iteration 75: Average Return = 190.46\n", - "Iteration 76: Average Return = 188.89\n", - "Iteration 77: Average Return = 187.9\n", - "Iteration 78: Average Return = 190.19\n", - "Iteration 79: Average Return = 186.28\n", - "Iteration 80: Average Return = 189.1\n", - "Iteration 81: Average Return = 188.16\n", - "Iteration 82: Average Return = 191.32\n", - "Iteration 83: Average Return = 192.03\n", - "Iteration 84: Average Return = 195.45\n", - "Solve at 84 iterations, which equals 8400 episodes.\n" + "Iteration 1: Average Return = 9.78\n", + "Iteration 2: Average Return = 9.91\n", + "Iteration 3: Average Return = 9.62\n", + "Iteration 4: Average Return = 9.89\n", + "Iteration 5: Average Return = 9.97\n", + "Iteration 6: Average Return = 9.88\n", + "Iteration 7: Average Return = 10.01\n", + "Iteration 8: Average Return = 10.0\n", + "Iteration 9: Average Return = 10.71\n", + "Iteration 10: Average Return = 10.35\n", + "Iteration 11: Average Return = 10.61\n", + "Iteration 12: Average Return = 11.29\n", + "Iteration 13: Average Return = 11.0\n", + "Iteration 14: Average Return = 10.8\n", + "Iteration 15: Average Return = 11.48\n", + "Iteration 16: Average Return = 12.4\n", + "Iteration 17: Average Return = 13.11\n", + "Iteration 18: Average Return = 13.61\n", + "Iteration 19: Average Return = 14.05\n", + "Iteration 20: Average Return = 13.94\n", + "Iteration 21: Average Return = 14.82\n", + "Iteration 22: Average Return = 16.58\n", + "Iteration 23: Average Return = 17.7\n", + "Iteration 24: Average Return = 19.3\n", + "Iteration 25: Average Return = 20.83\n", + "Iteration 26: Average Return = 23.03\n", + "Iteration 27: Average Return = 24.82\n", + "Iteration 28: Average Return = 24.66\n", + "Iteration 29: Average Return = 27.61\n", + "Iteration 30: Average Return = 28.64\n", + "Iteration 31: Average Return = 30.24\n", + "Iteration 32: Average Return = 32.75\n", + "Iteration 33: Average Return = 36.21\n", + "Iteration 34: Average Return = 34.75\n", + "Iteration 35: Average Return = 39.61\n", + "Iteration 36: Average Return = 38.22\n", + "Iteration 37: Average Return = 41.43\n", + "Iteration 38: Average Return = 45.36\n", + "Iteration 39: Average Return = 42.73\n", + "Iteration 40: Average Return = 43.14\n", + "Iteration 41: Average Return = 44.66\n", + "Iteration 42: Average Return = 43.44\n", + "Iteration 43: Average Return = 43.91\n", + "Iteration 44: Average Return = 46.07\n", + "Iteration 45: Average Return = 49.44\n", + "Iteration 46: Average Return = 51.79\n", + "Iteration 47: Average Return = 47.26\n", + "Iteration 48: Average Return = 48.62\n", + "Iteration 49: Average Return = 45.62\n", + "Iteration 50: Average Return = 50.1\n", + "Iteration 51: Average Return = 51.33\n", + "Iteration 52: Average Return = 51.93\n", + "Iteration 53: Average Return = 49.32\n", + "Iteration 54: Average Return = 48.41\n", + "Iteration 55: Average Return = 50.64\n", + "Iteration 56: Average Return = 52.25\n", + "Iteration 57: Average Return = 55.05\n", + "Iteration 58: Average Return = 52.15\n", + "Iteration 59: Average Return = 54.63\n", + "Iteration 60: Average Return = 53.32\n", + "Iteration 61: Average Return = 54.04\n", + "Iteration 62: Average Return = 53.59\n", + "Iteration 63: Average Return = 54.68\n", + "Iteration 64: Average Return = 58.52\n", + "Iteration 65: Average Return = 54.29\n", + "Iteration 66: Average Return = 57.1\n", + "Iteration 67: Average Return = 57.22\n", + "Iteration 68: Average Return = 55.15\n", + "Iteration 69: Average Return = 55.59\n", + "Iteration 70: Average Return = 58.47\n", + "Iteration 71: Average Return = 56.14\n", + "Iteration 72: Average Return = 58.26\n", + "Iteration 73: Average Return = 61.07\n", + "Iteration 74: Average Return = 60.72\n", + "Iteration 75: Average Return = 57.85\n", + "Iteration 76: Average Return = 59.45\n", + "Iteration 77: Average Return = 65.12\n", + "Iteration 78: Average Return = 57.5\n", + "Iteration 79: Average Return = 65.87\n", + "Iteration 80: Average Return = 63.98\n", + "Iteration 81: Average Return = 64.13\n", + "Iteration 82: Average Return = 63.81\n", + "Iteration 83: Average Return = 69.91\n", + "Iteration 84: Average Return = 62.56\n", + "Iteration 85: Average Return = 64.11\n", + "Iteration 86: Average Return = 66.72\n", + "Iteration 87: Average Return = 68.87\n", + "Iteration 88: Average Return = 64.46\n", + "Iteration 89: Average Return = 72.02\n", + "Iteration 90: Average Return = 63.88\n", + "Iteration 91: Average Return = 64.75\n", + "Iteration 92: Average Return = 75.53\n", + "Iteration 93: Average Return = 71.1\n", + "Iteration 94: Average Return = 70.51\n", + "Iteration 95: Average Return = 74.13\n", + "Iteration 96: Average Return = 74.27\n", + "Iteration 97: Average Return = 71.16\n", + "Iteration 98: Average Return = 69.6\n", + "Iteration 99: Average Return = 76.86\n", + "Iteration 100: Average Return = 79.23\n", + "Iteration 101: Average Return = 81.9\n", + "Iteration 102: Average Return = 79.63\n", + "Iteration 103: Average Return = 85.59\n", + "Iteration 104: Average Return = 89.9\n", + "Iteration 105: Average Return = 87.23\n", + "Iteration 106: Average Return = 92.44\n", + "Iteration 107: Average Return = 98.98\n", + "Iteration 108: Average Return = 96.52\n", + "Iteration 109: Average Return = 97.92\n", + "Iteration 110: Average Return = 108.71\n", + "Iteration 111: Average Return = 108.96\n", + "Iteration 112: Average Return = 118.2\n", + "Iteration 113: Average Return = 117.59\n", + "Iteration 114: Average Return = 129.08\n", + "Iteration 115: Average Return = 126.65\n", + "Iteration 116: Average Return = 129.7\n", + "Iteration 117: Average Return = 138.26\n", + "Iteration 118: Average Return = 140.57\n", + "Iteration 119: Average Return = 139.42\n", + "Iteration 120: Average Return = 148.91\n", + "Iteration 121: Average Return = 159.76\n", + "Iteration 122: Average Return = 149.16\n", + "Iteration 123: Average Return = 146.21\n", + "Iteration 124: Average Return = 153.46\n", + "Iteration 125: Average Return = 151.09\n", + "Iteration 126: Average Return = 161.77\n", + "Iteration 127: Average Return = 165.94\n", + "Iteration 128: Average Return = 164.25\n", + "Iteration 129: Average Return = 166.33\n", + "Iteration 130: Average Return = 166.18\n", + "Iteration 131: Average Return = 171.2\n", + "Iteration 132: Average Return = 177.02\n", + "Iteration 133: Average Return = 173.16\n", + "Iteration 134: Average Return = 176.46\n", + "Iteration 135: Average Return = 177.24\n", + "Iteration 136: Average Return = 172.08\n", + "Iteration 137: Average Return = 178.23\n", + "Iteration 138: Average Return = 184.34\n", + "Iteration 139: Average Return = 180.04\n", + "Iteration 140: Average Return = 184.14\n", + "Iteration 141: Average Return = 180.19\n", + "Iteration 142: Average Return = 183.2\n", + "Iteration 143: Average Return = 180.57\n", + "Iteration 144: Average Return = 182.69\n", + "Iteration 145: Average Return = 177.81\n", + "Iteration 146: Average Return = 178.39\n", + "Iteration 147: Average Return = 176.92\n", + "Iteration 148: Average Return = 181.84\n", + "Iteration 149: Average Return = 178.98\n", + "Iteration 150: Average Return = 184.1\n", + "Iteration 151: Average Return = 175.61\n", + "Iteration 152: Average Return = 174.43\n", + "Iteration 153: Average Return = 180.05\n", + "Iteration 154: Average Return = 182.6\n", + "Iteration 155: Average Return = 176.48\n", + "Iteration 156: Average Return = 183.03\n", + "Iteration 157: Average Return = 176.71\n", + "Iteration 158: Average Return = 175.56\n", + "Iteration 159: Average Return = 178.99\n", + "Iteration 160: Average Return = 179.27\n", + "Iteration 161: Average Return = 178.33\n", + "Iteration 162: Average Return = 180.35\n", + "Iteration 163: Average Return = 185.4\n", + "Iteration 164: Average Return = 183.54\n", + "Iteration 165: Average Return = 180.17\n", + "Iteration 166: Average Return = 180.46\n", + "Iteration 167: Average Return = 183.94\n", + "Iteration 168: Average Return = 188.49\n", + "Iteration 169: Average Return = 184.41\n", + "Iteration 170: Average Return = 186.91\n", + "Iteration 171: Average Return = 185.91\n", + "Iteration 172: Average Return = 188.38\n", + "Iteration 173: Average Return = 186.76\n", + "Iteration 174: Average Return = 188.51\n", + "Iteration 175: Average Return = 190.29\n", + "Iteration 176: Average Return = 192.84\n", + "Iteration 177: Average Return = 189.23\n", + "Iteration 178: Average Return = 193.89\n", + "Iteration 179: Average Return = 193.57\n", + "Iteration 180: Average Return = 197.19\n", + "Solve at 180 iterations, which equals 18000 episodes.\n" ] } ], "source": [ + "np.random.seed(seed)\n", + "tf.set_random_seed(seed)\n", + "prng.seed(seed)\n", + "\n", "sess.run(tf.global_variables_initializer())\n", "\n", "n_iter = 200\n", @@ -371,14 +464,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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8iI/tl5pHYWEhZWVlVFRU4PP5WLlyJUVFRX1+7jFJy4BD9eiaQOLKzDry8UfjTOq8NElH\nriRolZ0EhRDxr19aHg6Hg2uvvZaFCxdi2zYzZ84kPz+f119/HYA5c+ZQW1vLnXfeSVNTE0op/vGP\nf/Dggw+Smpra5bl9Lj0DbBtKS8x9d2QtpW4NSEUdbWKhK0lGWwkhEkK/1TymTJnClClTwh6bM2dO\n6HZmZia//e1vIz63z6VlAKD37oIBA6MqkHfFuu42M/z3SFwuaDx0TM8jhBD94bgqmPcmle5GA+zd\n2Xnxwp5cb3jh0Q9yJUFr7TE/lxBC9DVZnqQ7gZYHB2vbZpf3MeVKkuVJhBAJQZJHd9IzQjdVPyUP\nJHkIIRKEJI/upLUlj2MulkfK5ZLkIYRICJI8uqGSks2ChXDsw3QjJS0PIUSCkORxJMHWhzv7yMf1\nFhmqK4RIEJI8jiSQPPq15uH3oW1//zyfEEL0kCSPI0k3Owr212ir0Kq7rb7+eT4hhOghSR5HoELd\nVv1V8wjsFSJLlAgh4pwkjyMZNgJyh5rieX8ItTyk7iGEiG8yw/wI1Jy5qAsuOfqBvSW434eMuBJC\nxDlJHkeglALlOPqBvfV8SUlmSRRJHkKIOCfdVvHEKS0PIURikOQRT6TmIYRIEJI84onUPIQQCUKS\nRzxJkuQhhEgMkjziidQ8hBAJQpJHPAnUPLTUPIQQcS7iobobNmxg8ODBDB48mJqaGp566iksy+Lq\nq68mMzOzL2M8cYS6rWSGuRAivkXc8li2bBmWZQ5/8skn8fv9KKV45JFH+iy4E06oYC4tDyFEfIu4\n5VFdXY3X68Xv97N+/XqWLl2K0+nkxhtv7Mv4TixS8xBCJIiIk8eAAQOora2lpKSEYcOGkZKSgs/n\nw+eTFWB7jczzEEIkiIiTxxe+8AXuuusufD4f8+bNA2Dz5s0MHTq0r2I74SjLAqdTah5CiLgXcfKY\nO3cup59+OpZlkZubC4DH4+E73/lOnwV3QpLdBIUQCSCqhRHz8vJCtzds2IBlWYwfP77XgzqhOV1S\n8xBCxL2IR1vde++9bN68GYAXX3yRX/7yl/zyl7/k+eef77PgTkhJydAiyUMIEd8iTh4lJSWMHTsW\ngDfeeIN7772XhQsX8q9//avPgjshuVzgk24rIUR8i7jbSmsNQHl5OQDDhg0D4NChQ30Q1gnMmYSW\nbishRJyLOHmcdNJJPPbYY9TU1DB16lTAJJL09PQ+C+6E5DI1D32oHpIHoJyyX5cQIv5E3G110003\nkZqayvDhw7niiisAKC0t5Utf+lKfBXdCSkqG6krsu29Ev/SnWEcjhBBdivhjbXp6OldffXXYY1Om\nTOn1gE54LheUlQCg16+CS78Z44CEEKKziJOHz+fj+eefZ8WKFdTU1JCVlcX06dO59NJLcUrXSu8J\nLlGSmQ2le9C1VajM7NjGJIQQHUT8rv/HP/6R7du38+1vf5tBgwZx4MABnnvuORobG0MzzsWxU5lZ\naI8X67rbsRffhf5sPWra+bEOSwghwkScPD744AMWL14cKpDn5eUxcuRI7rjjDkkevUhdcR3qK9+A\nlFRId8OmdSDJQwgRZ6Ieqiv6lnIlhZZmVyefit60Hq01SqkYRyaEEG0iHm111lln8T//8z+sW7eO\nvXv3sm7dOhYvXsyZZ57Zl/Gd2MZPhroaKN0T60iEECJMxC2Pr3/96zz33HMsW7aMmpoaPB4P06ZN\n47LLLuvL+E5oKn8UGqCiDIYOj3U4QggRcsTksWHDhrD7EyZMYMKECWHdKJs3b+aUU07puwhPZO4s\nAHRdNdJpJYSIJ0dMHr/5zW+6fDyYOIJJ5KGHHur9yARkuEFZUFsd60iEECLMEZPHww8/3GtPtG7d\nOh5//HFs22bWrFnMnTs37Ptaax5//HHWrl1LcnIy8+fPZ9SoUYCZ3Z6SkoJlWTgcDhYtWtRrccUz\nZTkgI9PUPYQQIo70y+w+27ZZtmwZ99xzD9nZ2dx1110UFRWFFlcEWLt2LeXl5fzqV79i27Zt/O53\nv+NnP/tZ6Pv33nsvGRkZ/RFufHFnoSV5CCHiTMSjrY5FcXExubm55OTk4HQ6mTZtGqtXrw475qOP\nPmL69OkopRg7diyHDh2ipkbeNHFnQZ10Wwkh4ku/tDyqq6vJzm5bYiM7O5tt27Z1Osbr9YYdU11d\nTVaWKRovWLAAy7K44IILmD17dn+EHRdUpge9uzjWYQghRJiEWJRqwYIFeDwe6urquO+++8jLy+ty\n+9vly5ezfPlyABYtWhSWjKLhdDp7fG5vaxgyjEP//hfZWZkoR/ivK57iPJpEiTVR4oTEiTVR4oTE\niTUe4uyX5OHxeKiqqgrdr6qqwuPxdDqmsrKyy2OCX91uN1OnTqW4uLjL5DF79uywVkn760XD6/X2\n+NzeZruSQWsqd27vtEBiPMV5NIkSa6LECYkTa6LECYkTa1/FmZeXF/Gx/VLzKCwspKysjIqKCnw+\nHytXrqSoqCjsmKKiIlasWIHWmq1bt5KamkpWVhbNzc00NTUB0NzczCeffEJBQUF/hB0XVKbptpMR\nV0KIeNIvLQ+Hw8G1117LwoULsW2bmTNnkp+fz+uvvw7AnDlzOO200/j444+59dZbSUpKYv78+QDU\n1dVx//33A+D3+znnnHOYPHlyf4QdH9yBFlptDcgkcyFEnOi3mseUKVM6bR41Z86c0G2lFNdff32n\n83Jycli8eHGfxxe3QrPMq2SWuRAibvRLt5U4BhmBbqta6bYSQsQPSR5xTjmdZl8PqXkIIeKIJI9E\n4M5Cy0RBIUQckeSRCNxZ0vIQQsQVSR4JQLk9srKuECKuSPJIBB4v1NWgfa2xjkQIIQBJHolh0BDQ\nNlRWxDoSIYQAJHkkBDV4iLlxoCy2gQghRIAkj0QQSB66QpKHECI+SPJIBOluSBkAkjyEEHFCkkcC\nUErB4DxpeQgh4oYkjwShBg+RlocQIm5I8kgUg4dA1X603x/rSIQQQpJHwhg8BPx+qJLhukKI2JPk\nkSDUoMBwXem6EkLEAUkeiSI4XFfmeggh4oAkj0ThzoLkFGl5CCHigiSPBKGUAm8OunJ/rEMRQghJ\nHgklU1bXFULEB0keCURJ8hBCxAlJHonEnQ0Ha9C2zPUQQsSWJI9EkukB24b6g7GORAhxgpPkkUBU\npsfckK4rIUSMSfJIJKHkURXbOIQQJzxJHokkMxsALS0PIUSMSfJIJBmZoJR0WwkhYk6SRwJRDodJ\nIHWSPIQQsSXJI9G4PdJtJYSIOUkeiSbTAzVSMBdCxJYkjwSjMj3SbSVEAtEHa9CH6nv/utUH0PtL\ne/26kZLkkWgys6G+Du1rjXUkQogI2L9agP37X/fqNf1VB7AX3o794D1o2+7Va0dKkkeiCc71qKuN\nbRxCiKPShxpgdzHs2tp712xtpe5/74aDtVBdCcWbeu3a0ZDkkWCUTBQUInHs2Gy+1laje2lZIf23\nP9K6dSNq3nchKQm9+t1euW60JHkkGncgedRUxjaOOKcPlEvXnog53b5VsHdnr1xPv/4iA+Z8Gevs\nWahTT0eveQ/t7//FUiV5JJrcoeBwoncVxzqSXqcbG9CNDb1wnUPY996MfvOVXojKsD94C/vZx3vt\neuLEoIs3QfZgc3vfrmO7lt+P/fgvwTOItHk3A6BOn25qoG+8hO7nUZiSPBKMSkqGEaPRxZ/FOpRe\nZz/6APaj9x/7hUp2QmsL7N5+7NcK0KveRb/9SsyKkyIxaK3bbvt8sGsravIZZnJvya5ju3hFKVSU\noi76KtaAgeaxU6bA4CHoZx/H/uG12E8+1GvdY0fj7JdnEb1KjR6PXv4S+vDhWIfSa7Rtw/ZNkJp2\n7NcqMUlDl+455muF1FRBS4v5mj2o964rjhu6tQV74e2o06djfelyKNlh/mYKx0FZCXrvrq7Pa25E\nv/4iWBZkZqMKT4bcYWbr6fYqKwBQuUNDDylXEtZPHoK9O80HnDdfRn+yGmvhI6jklL76UQFJHglJ\njZmAfu15WrdthNyCWIfTOyr3Q1MjtLagte78Hycae3aYr+V70bYfZTmOPb5gjWn/Pkkexxl7+Uuw\nYwvWDXdEdLxevxq95j1ISUGdfxEqd5h5fPlLsG83euc2c3+7qXeo0eNg1zb0m39H+/1mmaH211vz\nPvrlP7fdB9S5c1DfvDn8uCqTPMjOCXtcuVwwcixq5Fj0ObPRO7b0eeIA6bZKTKPHgVK0fLY+1pH0\nnj2BLiafDxqOrdmt9+wAZYGvFQ7sP+bQ9OHDEJjkFctJWaL36dYW9Ct/QX/0Hro1sgEW9ktPodf8\nG/32P9FvvGyuU1uNfuVZc0BwEm9FGaQORGVlw7AR5u9x/77OF9y1FQakYi19DuunS1HnfRH97uvo\nj98PP66qAhxOcGd1G5saOhzr3DkR/RzHqt+Sx7p16/jud7/LLbfcwosvvtjp+1prHnvsMW655Ra+\n//3vs2PHjojPPdGogWmQV0DrZ+tiHQoQ3s/b42vsaft9H8vyK7rlMJSVwLhJ5oEedl1pXyv2Xx9H\n790ZPrKtq//8ImHp1e9CQz1oO6LfrW5tgX27UbMuhpMnoneY+Rv6tedNchgzPrTqta6phiwvAGrY\nCPPYS0+bv6n219y5DYaPRrlcqCHDUF/9NhQUYv9xaXj9oqoCPF6UFR+f+fslCtu2WbZsGXfffTdL\nlizhvffeY+/evWHHrF27lvLycn71q19xww038Lvf/S7ic09EaswEWrdsQB+siWkc+uOV2D+4tsdx\n6PJ9aK3Re7abT1VwbGt37dsDto06Y7q5fk+Tx7OPo197Af3em23JQym0JI+EpVs61wj1m69AoPis\nyyJ4X9mzA/x+1IgxqJFjYd8udMth9KdrYPxk1JgJUFdtang1lZBl9uBh6HDUzAvRn6zGXng7uuqA\nec7WFti7CzViTOgplNOJ9bXvmFFUG9e0xVpVAd7wLqtY6pfkUVxcTG5uLjk5OTidTqZNm8bq1avD\njvnoo4+YPn06SinGjh3LoUOHqKmpiejcE5E6exZaa+z/vTv0hxgLetc2qK1Cv/pc9OceKMf+8Xz0\nK8+Y/5QnnWIeP4YJkMFiuRozATyDoLSk6+Ns2/zH7YL94TvoN/9ukkXpHnQweRQUQoy6rfSBcvwP\nL+yVoczHI717O7ou/AOMbm4MJQx75RvYt3wV+5Vn0LY/dA67i1EXXmH2ySnr+m8l7JrBIfLB5OH3\no9d+APv3ocZPMitA2DY01EFtFSqwgZuyLKyrb8T67yXg86HXfWCuU7IT/D7UyDHhT5Q/0sRUUd72\nWFUFyhM/9bZ+SR7V1dVkZ2eH7mdnZ1NdXd3pGK/X2+mYSM49EakRY8i69xdwsAb7qd/ELpBgE/3t\nV6MfZ16yE7RG//0vUF+HmjDF1CqOZfb8nh3mk6Q3B/IKum156H+9iP2j73Q59Fb/83koGIUqOsd0\newV+LnXyRKisiLhvPBq6ttosZdHd9zesgXUfotfLB6eOdOMh7P+9E3vpz8K6UO3778F+eKE55s1X\nzPyoF/+IfvIh81hg9rc6fbr5e+kmeeiWw/h/9VP09s2wa5sZdpvlhVFjzfdf/au5zrjJqOAk3soK\ns3xIVnbYtdSQfBiSbxIOgQ9fACPGhh/nSjLnHjDJQ7e2QF0NeAf36DXqC8fVaKvly5ezfPlyABYt\nWhSWjKLhdDp7fG5/cubm0lR0Nq1bN8Ys3prGBnyDcrFrKkl+6+9k3HB7l8d19Zo21FVxCDNaRPt9\nZE6eSt0bL5HUdAi314v2+VDO6P5Eq0p2YI0+maxBg6gvHEvjP54jOyur0wiXmt3FtNRU4sGPGjiQ\n2vvuIP3aW7Ey0qB0NwMvn4dyJdGw+l2SKstpycgkfdypHHztBbJ8zTiHDOny+f37S2l+/21SL7oi\n4ti130flj27ENXYCmbf/tMtjDtZV0QQkb9uA++LLgQT6O+3jOBv/uYL6lsOwYwtpn33MgBmfx1ey\nk6rdppWQuuod6ncXk37df9G67TMOf/w+2dnZ1FdX0Jyahnf0WGqHF2IfKAvFajcewl9RhmvEaFo+\n+YiaTz/C0dKM3VCPY+wEsgYNgkGDODAoF3vfbqxMD95Tp9C6NZkaYGBVOfVak5Y/gtSOf/fTZnLo\nhafwJLnONPryAAAgAElEQVSoL9tDS1Y23jEndRphWJ1XADUH8Hi9+PbtoQpIH1HIAK83Ln73/ZI8\nPB4PVVVtnyarqqrweDydjqmsrOx0jN/vP+q5QbNnz2b27Nmh++2vFw2v19vjc/uT1+vl8MAMdFUF\nByoqYlJI81eUmyb20BE0rXmflm5et65eU7t4s5l9e8Fc+PvT1GV4sDOyaC7fR8uK5dgP3Yd1329R\nnsj+k+jGBuyd21AXX0VlZSW2ZzC0tlD51quoyWeGxx0YTlm9cT04nNifraPm73/Ffd7nwbZpGjLc\nFECBw+s+hCwvDakZANRs3ogakN75+f1+7EV3wa5tHNqzE+vqGyOLe/0q7IoyDjucYa+RbmwArVED\n0/HvNN1xzWs/oKViP/rNV8g87XQOZudG9Byx1Ff/n3RtNSrTg//V581oJoeTg7//NQ2F49H/etl0\n+6SmUf+7JeB0cuiUInRTM3rF61Ru24K9YxsMGUZVVRV29mD0ulW0Hm6msrwc+3/vgn27se7/Pfpj\n00po3bIBAPtzZ4d+Hj18NBwoR580kaqqKjTmQ0rDBjOY5ZArmcYOP7s+aRLYT1L5+t/QG9dBQWHY\ne1yQneVFr/uQyspKdPEWc92kARyqrOyz1zQvLy/iY/vl3aawsJCysjIqKirw+XysXLmSoqKisGOK\niopYsWIFWmu2bt1KamoqWVlZEZ17QvN4zfDW+rrYPH9tNcrtMaNJDpR3WZTsji4tgSH5WLMuwnrg\nSVTKALPkfE0Vet2HZpZ4sFnf3TW0Rq9fZZ5360bzZhuonagp06BgFPajD6B3bGk7p7nRzCvBFNR1\niRnppT/9iNbNn5g3nZFjIS8wh6ah3tRPckxro7uiuV7+kol37AT0W69gr3jNPL5jC/6f3NLtDpD2\nO/80NyrKwtYosh/7Rajbhf37YEAqNNSjX3kW/Zff0fjSn7u42olBb1qPfcc8/Et+DHt2oKZ/Huuq\nG6CuBv3nR9EfvQdjT0F94VLQNuq0s1BpGaFRT+zdCWUlqODveEg++Frxl+01qxzs2Q5+H3rjWrPE\nSO5Q838N02UcMjLQ3TRusvnqzjTx7QysopvVxQef4YWQmY1+6rdwoBw1sZv3s0FDTNG8qbHbOR6x\n1C/Jw+FwcO2117Jw4UK+973vcdZZZ5Gfn8/rr7/O66+/DsBpp53G4MGDufXWW3nkkUe4/vrrj3iu\nMEIFtOre+RSi9+7Ev/hudARzLfThZmg6ZIqEeQVmuGN5ZCPhtO2H8r2oPPO7DE7kU1keU4Dfaj7l\n6dLdnc/dtB772cdMveLTj7Afug/9j2fRWzaAKyn0H1qlDMD67r3gzjLHHAwsY9++iL5vD+wJDJ2s\nqqD5rVchrwCVOtC0ipKSA3Flo1LTIHswuosh0rrhIPpvT8HkM7Fuvw/GTUL/9femNfT3v5jJY6ve\naTu+uhL/T7+L/edHYcMaGJQLfh9UtZuXsns77NhiNhKqPoCaNssU8V/6EwC+Hq5vplsOm4UjE3ip\nlVCtYMunkJSEOmMGqvBk1BcvQ7+33CSGorNR530JTjvTJBEwLRTM3xD1dRD8+xtivtb94qewfhXq\nym9DWjp88hHs2IIaOxH15a9D6sBQrQNATTkLJhahJk01950uSHe3/T/oUPMAUzxXc7+GOnsW1h0/\nR03/fJc/oxoc6Bo9UBaY4+Fo25IhDvRbzWPKlClMmTIl7LE5c9omsyilQgkjknNFQPCTTfUB6Dhi\nowf0itdg6wb06ndRMy888sHByVCZHtTQAjTmk7wqKDz6E1XuN91CeR1myGd5ofGQ+QedRkvplsNm\ncbiaShg6Av1u4NP926+aQuaok0yxMUBlZGHd9CPs+27DfvIhrJt+hN4XSEjeHFNQbzlsRlLt2Y5/\nfylqxhfMuZZlPpHuLm4br3/uHFN0Ld3T9qkVTIujtQVr9iUoy4F12TXYC/4L/adH4NOPTIyr/w1z\nvmJub/gISnaiS3aCslBzv45+9H4zmmtwnmkdBQYO6FXvgtZQeDLs2AI7t8JJE/Fv3YB1uPmIs4n1\n3l3QeAg1dkLoMfuxJbBmJSQlo772n1jTzj/67yvelO8Dtwfrtp+any+wrI265GrzwWPHFtSUaaiU\nATjm3x06TQ1INb/31f8294cEfoeBWeK+7ZtRsy/BmnUx9s6tpgXj98HocVhnzUSfOSNsxQLlzcFx\n64/DY3N7TGJKTgkNA+7IOns2nD27y++FDAp0SVYEkkdmdqfaXSzFx2wT0XOBprSuOfbhutq20Ws/\nNLc/fOcoRxMaaaUyPTA4z8zTiHReRSApBD/xhWS2+6SWkdn2Rh+McflLJnFkD0Y//QgUb0JNPdfM\nAC8rQZ00sdNTqaHDUf/xLVi/ynwqLd1j3jgnFkHZHijfhzp1auhTKYXj2s4NJohg8pj+eXC6zFDe\n9nEFJznmjzTHFYyCyWeY19HpQs2Za5aoCIyeoXgzpLuxfroU6/sLUYFuD10e6BIrb+sa0yvfMNfM\nGYp18VWoS7+FNftik1C6WS8pyP7dA9iL78J+/km07TfzadasNK/ZoFz0y093PeJsy6fdDmXuL9q2\nsV/4I7q689+2Lt8LuUNReQVm+Y8A5XBg3fzfWD9YhMrI7PrCw0a2ffAJ/P2p1IEwdDjJZ8xAXX6N\n+d7EIpM4IPQcES11E2wdZGYf2zI7g03y0AfKzd9FHM3xAEkeiW9guulaqeqFbqvdxebT7rCRsH1z\n2xtdN0J9+O5sM7IoJ8/UMSIQGkLbIXmENrtKSkKdeR7s34f2teJf+jP8992G/sezMOl0sw5RcxOk\nu1HfurWtqypQ7+hInX8RFJ5sZvjuLjYtnqHDzcJ12kblj0RN/Jw5tvDkthOHmuQRLNqrdDfqjOno\n998KH1pbshO8OeZNKMC6+EpzzhkzQq04/ZH5xKu3b4bCk82M4rETTBdJalpolnNowlpSclvdJycP\nNfFzWF/8D8gfZY4r6X6PCF1RCvt2m6Ghr/4V+xc/wX7uSbNkxtf/E/X5S00LsMMKzbp8L/b9P0J/\n8Ha31+4XFWXofzyD/ve/wh7WWpuEnzO0y9PUwLTw32HH7wc/JAxIDetWsu55EPcPf9bWhTr+NFP/\ncmdF9cYd+hvuossqGiolFdLd5m9md3H3tZEYkeSR4JRS4PH2Tstj7ftgWVjX3Grur1px5BOCySPL\n/GdReQXmzSp4vY1rsf/5HFpr/LXV2C89jW5qNN8sK4Esr+lGaC/4H27UyeYN0u9Hf7gC1n5gaip5\nBViXXYMadRLqq9dhfe0/UcnJWF/5Bow/DUae1GWoyrKwLr7KtFq2fYYaWhDe7VQwCvX5/8D9g4Vt\nfc1g5p4UjGprlRBIRC2Hw2sYJTtDrY7QcQWFWLctQF1+LcqbAyPHoj98x9ReKkrD3uCUUpA7tF3L\nYy84HGY5bzDLUrTvnvIMQg1MD9tgSDc1hq20rNeZVqR1649R37oFtn0Gn61Fzf4yKjXN9NcnD0C/\n/1ZY3MGF/ago6/K17DcNZhCI3r6lw+MHobHBFLF7QOWPMDeG5Ie1DJTTFX4/PcPUMyafEV0LIpA8\n1DEmDwAGDzFzl1LTUDO6ro3EynE1z+OE5RnUKwVzvfYDOGmiqVmMHmdWDr3wiu5PqKuGpKS2ft2h\nBfDRv9GBfnj7jZdNf39tNbXFn5kZvampqNlfNm+2HbusALIGmVbHhNNQQ4ebOspLfwKnE+v2+0J9\n2wDW7C+Hbqtxk3AE17PqzvjJMGKM+SSfNzxULCVQHFdKkXLWTBraDYFUw0bg+O9fhF1GFRTCsJHo\n996AmReim5tMMjhjRqenVO1iUtM/j37i1+i/mYJ3++4WMN1SepMpxuvyvabPe8wEWLUCOnzKVkrh\nGDGa1nYtD3vJj1GeQajv/NBcY+2HkD8S5c1BnXMBOn8U+v03URdcYq6RnIL63DT0R/9GX3kDKtkM\nDiAwP4LgCJ9YORgYQbhzC9q224aiBxJscDXbqA0LdC12rLd1wXHLf0d//eBEwa5GWkVJDR6C3r4Z\ndf6FpiUSR6TlcRxQWd5jTh66rsZ0BQSaxuqkiWaEUBdDb+23/mGWV6itBrcn9Kks9J8xOFO3cj84\nneg3Xsa3b48ZnrjqXbMw3L7dqAmndf5ZkpOxfvIQavaXzSdLyzKDAU6dGpY4ekIphXXJVeb2yLHm\nk7s7C4aNjLpvWp19PuwuRu/bY1pbWqM6tDw6nXPmeeAZhF7xT1MfGj46/ICcPLPXdXMTlO01ezoE\nWieqi0/ZzhGjYe8uU8toOWzi+WQ1+vBh07rZvqmt5QKo4YVYV3477E1ITTsfmpvalssgsGwHoCuP\nfUXiY6EDLQ+aGsNmf+vgSKYetjzw5pgWxWlnHv3YHuitbivA/I2kpqHOv/jYr9XLpOVxPPAMgoM1\naF+rGSrYE4EahBo63HzNH2UKqfv2hI3i0raNfv4JdGqaed72QwcDyUOX7jF/9FX7UTO+CGkZuE87\nnbpPP0Y/94Tpd3e6UGfP6jIUNajdxLfBQ6B8H9YZ5/Xs5+p47YlFWP+zLDTE2fraf0J6RvTXOX2G\nGYr7/httY+8LRh35HKcL9cX/MOP7hxeGjQoD80lag3mjrChDTT7dtOYmn9FpkiOAa+QYmloOm/WP\nmhvNmkp2C2xej66uNAmtXfLo0pgJZvDByjfhjBlmCHVg3kvctDwwNaLg3ybl+8Dp6vG+KsqyOo+Q\n6k2BVmKnwSA9oGZeiDp7tpkDFWek5XE88HjNyJtjWco8+Gku2JVTECzI7gg/cP8+U6iuPgDbN4cW\nfgPMpCanyySig7WmGD14CNZFXyV50lTUVLPSLRvWoIrOMZ/8j0ING2kKyb1YLGy/uJw67UzU6PHR\nXyMjEyYWmX0X3n/TDFyIoJtCnX0B5Aw1o7s6ar+pkN9nWh6WA8dNP0KNn9zpcGegVaJ3bG4b7eVw\noNesNEuEjxgTKqx3G49loc6aCZvWm7XJ9pfC4WaTtA/WRjXps9fV10HKADNvYvvm0MN6/z4YPKR3\nNvnqA2rIMKxFy7oc+Rf1tSwrLhMHSPI4LgTfDPV7y7H/bzG6J7PNy0rM6JNgf232YHO/pIu9B8Ak\nCW23HY8ZJknuMNOVExip1b4VobIHmY2sIOLin7r8Wqw7Fprd0uKMdem3zOu0c6upLUTQ9aVcLqwF\nS7G6qiXl5aNOnx4aqHC0Pn1nwSjzxrppvfk9DUg1w4PffxOqKrAuvjKymM6aCdpGf/i2GYlGYHY+\nmA8JsVJfF5q7E1zEEDAtj57WO/qJOgF2m5TkcTwIzvV45Rn06nexn/h11Bs06dKSsH2TlWVB/sjO\nLY9d2yB5AOq8L5r7HWa8qrwCKN3T1l/eYYijdeFXUTO/FDaX4kiUx2taH3FIDRmGdc+DqOtvx7rs\nmsjP6+YNXSmFuu57ZlhvZnZbK7C761gWatwk9Kb15vc0bERb99bw0RG31tTgPDOMecVrZtBEUhLq\nFDNsObhvdizo+jozFLtwHJTvQ6/7EL1xrenSO8prI/qeJI/jQfZgM+z19OmoS79pJsO9/Wp01yjf\n26mPVuWPChVkg/Subaa/fsYXwJXU1g8dlJdvPq0GJ69lhy8hrU6ZgnX1d45t8lQcUZYD64wZqOER\nzKqP9HpX32jqMpGMrhk/2SzVvWMLqqAQdWoRDB+Nddm8qF5j66IrzSf9j983o5ECLUYdy7pHMHlM\n/zyMGIO99OfYv15gNlY6/6LYxSUAKZgfF5QrybzZKGUWCty6Af3c79GTpka0eYxubDBvQB0/zeWP\nNP3fFeVmDoKvFUp2oM6/GJU7DGvJH0NrP4ViCQ6v/WS1GYnV4fsiMpGukKzGTTZFdq1N11lqGo57\nHoz++U6ZgrXod+i3X0UNHw2ZWWZEWGCtLW37sR9ZjDXj82byXH+orzPzeQamYd1+H/ayB+FwM9aN\nPzRbMYuYkpbHcSLU3aSUGUFk2+hnHgt93+yc180mRoHZzCq3i5YH7Yrm+3abFXwDq4qq5JTOn27b\nD9eNo41rjlfK4w31/x9tqPBRrzUwHevCK1CnTDHFaI+3rduqeBN8vDK0UnBf07ZtJgOmu01sKQNw\n3PQjHLctkMQRJyR5HIeUNwf1pcvQa94zq4cC+u9/wb7nxrAlv4PalgrpUITMyzfzNJ5Zhv3kQ9gv\nmyXA1YgO8xPa8+aYiYN0GHIr+oya+DnTAhxy9ElvUfHmhLqtgmuesfnT/lmNt7HBDD0OJA8RfyR5\nHKfU5y+FTI+Z5Q3o1SvMRMLiTejDzaaoHly7qnyvWcq8Q0tBOV1Y138fho82s803fGz6w4+wzo9Z\niTbwJhZnC7kdr9QlV5vCfS+PSFPZg6GqwnSFrn3fJKhD9Z1G4PUWvbu4baBHfWBLAEkecUtqHscp\n5UpCFZ2LfvsVU+QOLOmgP1ltFhv897/M+kgXX2laHjlDuxw3rz43DcfnpkX33Hn5ZsinJI9+oVIG\ndL3Uy7HKHmxqYWvfh6oKs2z8i39Eb1rXawMEgvTuYuz7bsO69V6Y+DmoN3uvKEkecUtaHscxVXQ2\n+HzYf/yNeWBIPvqTVeh3zQZcettGtM9nljUf1fWCgj0SqHsor3RbJTI1+QxIHYj9m0WgLNS5cyCv\nINQV2pv0PtN1qveYpVFCLY8MSR7xSpLH8WzkWFP03F1shjfO+KJpgezaZmZEb98M2zdBc1OXM5h7\nSp06FcaM77TKrEgsatgIrHuWmL+jyaejMjLNQo/bPjO7G/amA4EVfAP1Nx1oeZAmySNeSfI4jinL\nQn3ubHP71KlmDgCYdaW+8g1oOYz9z+dAWXDyqb33vHkFOH6wKGxvC5GY1KBcHHffj/Wfd5n7U6ZB\nawv2nddj/+Fh7LdfDW3vq237iMuZaK2xX3mm0wZfgBkOTrvBG8GWR1r0646J/iHJ4zinpp1vNv85\n/Vwz+qnwZNRZM9sWzNvwMYwYLcMfxRGFhoKPnYD1379ATSxCf7gC/dRv0H99HAD9+gvYd327+wRy\nsNbUTALdpu3pYMujfK8ZEVhfa1aTdUpZNl7Jb+Y4p4aNxPHLp0P3rR8sMo9bltk6tqI0tAWqEJFQ\nBaNQN9xhRmH932L0xrWm1fHRe2ZBzE2fwKSpZmXf9svXBBbfDC3C2d6BMrM2V1OjuV1/UOodcU5a\nHicYZVmh2ctq7ATzdYIkDxE9pZQZGXWw1izOGFhUUa99H71nB/Zd14dtZauDe3K0258dAiscNNS3\nradVuie0rpWIX5I8TmBq2iyYfAb05kgrcUIJDrSwnw2sZpBXgF6/ykwotW3Y8mnbwcF92asqwrbL\nDa7ATKAmp7d9Bnu2owa1bQcs4o8kjxOYGjPe7BXR0w2kxAlPZWbD0OFm6ZqMTNRFV5plRdZ9AMoK\nW0o9rLtqf9ttHdgrXQ0baTamevPvZgTgnLn99nOI6EnyEEIcEzVhSuirmvg5s9dLcgpq9sVQvg87\nOHKqbG9o+LYu24v9xsv4H7rPbDAGZiXfvAKwbdQZMzqv2CziiiQPIcQxURNNrUJNmopKGYC65CrU\nV68P7ZbYunUjurkRaipRk84wQ8PL96L/9TezfcAbL5sVmJNTzMx1hxN18VWx/JFEBGS0lRDi2Jw0\nEevO/w3VzqwvXgaAbm4CZdG6ZQMElqxS+SPR3sHoD98xe6SnDoSGejOpFLMmmzp9Bmqw1DvinbQ8\nhBDHRCmFKjy50/L8KmUADBtO69YN6GCxfEi++XegHBxOrFt+DA4HKmdo6BzVcXVnEZek5SGE6DNq\n1Em0rloBSSngcMCgXFTuMLNA54TTUKPHYd12n+z9koCk5SGE6DPq9Ono1lb06ndhcJ6ZMZ4baGUE\nl84ZOyGiHS9FfJGWhxCiz6ixpzDo8ZepfOs1sz8IoCafCWUloeQhEpMkDyFEn7LSMrDOmhm6r9Iz\nUFdcF8OIRG+QbishhBBRk+QhhBAiapI8hBBCRE2ShxBCiKhJ8hBCCBE1SR5CCCGiJslDCCFE1CR5\nCCGEiJrSWutYByGEECKxSMujC3feeWesQ4hIosQJiRNrosQJiRNrosQJiRNrPMQpyUMIIUTUJHkI\nIYSImuMnP/nJT2IdRDwaNWpUrEOISKLECYkTa6LECYkTa6LECYkTa6zjlIK5EEKIqEm3lRBCiKjJ\nfh7trFu3jscffxzbtpk1axZz586NdUghlZWVPPzww9TW1qKUYvbs2XzpS1/imWee4Y033iAjIwOA\nq666iilTpsQ01ptuuomUlBQsy8LhcLBo0SIaGhpYsmQJBw4cYNCgQXzve98jLS0tpnGWlpayZMmS\n0P2KigquuOIKDh06FPPXdOnSpXz88ce43W4eeOABgCO+hi+88AJvvvkmlmVxzTXXMHny5JjG+oc/\n/IE1a9bgdDrJyclh/vz5DBw4kIqKCr73ve+Rl5cHwJgxY7jhhhtiFueR/v/E22u6ZMkSSktLAWhs\nbCQ1NZXFixfH7jXVQmuttd/v1zfffLM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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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t2pUnnnjCpUGFEOen03dAcBiERVgdRTRCThULwzAYPnw4w4cPP+t6Hx+feg0l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\nCCUlJTz55JNERdW+7aCUOuu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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -400,6 +493,33 @@ "util.plot_curve(avg_return_list, \"average return\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "np.random.seed(seed)\n", + "tf.set_random_seed(seed)\n", + "prng.seed(seed)\n", + "\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", + "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": "markdown", "metadata": {}, @@ -425,9 +545,218 @@ "\n", "can remove the baseline.\n", "\n", - "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. " + "Modify the code to compare the variance and performance before and after adding baseline. And explain why the baseline won't introduce bias. Then, write a report about your findings and explainations. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 15.56\n", + "Iteration 2: Average Return = 17.24\n", + "Iteration 3: Average Return = 18.47\n", + "Iteration 4: Average Return = 20.21\n", + "Iteration 5: Average Return = 22.23\n", + "Iteration 6: Average Return = 21.91\n", + "Iteration 7: Average Return = 23.87\n", + "Iteration 8: Average Return = 23.69\n", + "Iteration 9: Average Return = 26.19\n", + "Iteration 10: Average Return = 24.95\n", + "Iteration 11: Average Return = 26.79\n", + "Iteration 12: Average Return = 28.16\n", + "Iteration 13: Average Return = 30.11\n", + "Iteration 14: Average Return = 28.1\n", + "Iteration 15: Average Return = 34.36\n", + "Iteration 16: Average Return = 33.82\n", + "Iteration 17: Average Return = 34.09\n", + "Iteration 18: Average Return = 35.41\n", + "Iteration 19: Average Return = 33.7\n", + "Iteration 20: Average Return = 38.1\n", + "Iteration 21: Average Return = 43.15\n", + "Iteration 22: Average Return = 39.92\n", + "Iteration 23: Average Return = 39.9\n", + "Iteration 24: Average Return = 39.55\n", + "Iteration 25: Average Return = 42.06\n", + "Iteration 26: Average Return = 45.46\n", + "Iteration 27: Average Return = 42.36\n", + "Iteration 28: Average Return = 43.48\n", + "Iteration 29: Average Return = 46.17\n", + "Iteration 30: Average Return = 46.95\n", + "Iteration 31: Average Return = 45.55\n", + "Iteration 32: Average Return = 44.52\n", + "Iteration 33: Average Return = 48.75\n", + "Iteration 34: Average Return = 48.17\n", + "Iteration 35: Average Return = 47.81\n", + "Iteration 36: Average Return = 44.81\n", + "Iteration 37: Average Return = 43.88\n", + "Iteration 38: Average Return = 47.81\n", + "Iteration 39: Average Return = 49.59\n", + "Iteration 40: Average Return = 49.15\n", + "Iteration 41: Average Return = 50.89\n", + "Iteration 42: Average Return = 48.96\n", + "Iteration 43: Average Return = 50.64\n", + "Iteration 44: Average Return = 53.57\n", + "Iteration 45: Average Return = 47.83\n", + "Iteration 46: Average Return = 48.44\n", + "Iteration 47: Average Return = 52.62\n", + "Iteration 48: Average Return = 51.34\n", + "Iteration 49: Average Return = 52.72\n", + "Iteration 50: Average Return = 52.32\n", + "Iteration 51: Average Return = 50.88\n", + "Iteration 52: Average Return = 50.89\n", + "Iteration 53: Average Return = 57.9\n", + "Iteration 54: Average Return = 55.69\n", + "Iteration 55: Average Return = 53.77\n", + "Iteration 56: Average Return = 52.56\n", + "Iteration 57: Average Return = 58.32\n", + "Iteration 58: Average Return = 55.95\n", + "Iteration 59: Average Return = 59.54\n", + "Iteration 60: Average Return = 58.22\n", + "Iteration 61: Average Return = 58.21\n", + "Iteration 62: Average Return = 59.16\n", + "Iteration 63: Average Return = 59.78\n", + "Iteration 64: Average Return = 62.16\n", + "Iteration 65: Average Return = 60.29\n", + "Iteration 66: Average Return = 59.72\n", + "Iteration 67: Average Return = 58.1\n", + "Iteration 68: Average Return = 60.61\n", + "Iteration 69: Average Return = 64.86\n", + "Iteration 70: Average Return = 62.56\n", + "Iteration 71: Average Return = 63.08\n", + "Iteration 72: Average Return = 64.2\n", + "Iteration 73: Average Return = 61.77\n", + "Iteration 74: Average Return = 63.26\n", + "Iteration 75: Average Return = 67.74\n", + "Iteration 76: Average Return = 67.69\n", + "Iteration 77: Average Return = 65.79\n", + "Iteration 78: Average Return = 72.49\n", + "Iteration 79: Average Return = 68.62\n", + "Iteration 80: Average Return = 68.21\n", + "Iteration 81: Average Return = 74.62\n", + "Iteration 82: Average Return = 77.57\n", + "Iteration 83: Average Return = 77.46\n", + "Iteration 84: Average Return = 78.14\n", + "Iteration 85: Average Return = 79.17\n", + "Iteration 86: Average Return = 83.59\n", + "Iteration 87: Average Return = 76.72\n", + "Iteration 88: Average Return = 85.69\n", + "Iteration 89: Average Return = 94.28\n", + "Iteration 90: Average Return = 100.24\n", + "Iteration 91: Average Return = 101.14\n", + "Iteration 92: Average Return = 102.56\n", + "Iteration 93: Average Return = 106.51\n", + "Iteration 94: Average Return = 110.64\n", + "Iteration 95: Average Return = 111.64\n", + "Iteration 96: Average Return = 115.42\n", + "Iteration 97: Average Return = 115.08\n", + "Iteration 98: Average Return = 113.79\n", + "Iteration 99: Average Return = 112.82\n", + "Iteration 100: Average Return = 113.51\n", + "Iteration 101: Average Return = 116.17\n", + "Iteration 102: Average Return = 110.84\n", + "Iteration 103: Average Return = 111.84\n", + "Iteration 104: Average Return = 111.83\n", + "Iteration 105: Average Return = 111.47\n", + "Iteration 106: Average Return = 119.33\n", + "Iteration 107: Average Return = 116.92\n", + "Iteration 108: Average Return = 123.41\n", + "Iteration 109: Average Return = 119.64\n", + "Iteration 110: Average Return = 119.95\n", + "Iteration 111: Average Return = 117.22\n", + "Iteration 112: Average Return = 123.12\n", + "Iteration 113: Average Return = 123.63\n", + "Iteration 114: Average Return = 129.47\n", + "Iteration 115: Average Return = 129.03\n", + "Iteration 116: Average Return = 129.16\n", + "Iteration 117: Average Return = 130.86\n", + "Iteration 118: Average Return = 127.29\n", + "Iteration 119: Average Return = 132.11\n", + "Iteration 120: Average Return = 133.41\n", + "Iteration 121: Average Return = 135.12\n", + "Iteration 122: Average Return = 142.29\n", + "Iteration 123: Average Return = 140.19\n", + "Iteration 124: Average Return = 137.79\n", + "Iteration 125: Average Return = 140.94\n", + "Iteration 126: Average Return = 141.23\n", + "Iteration 127: Average Return = 137.84\n", + "Iteration 128: Average Return = 144.83\n", + "Iteration 129: Average Return = 144.11\n", + "Iteration 130: Average Return = 150.13\n", + "Iteration 131: Average Return = 156.81\n", + "Iteration 132: Average Return = 153.88\n", + "Iteration 133: Average Return = 162.66\n", + "Iteration 134: Average Return = 158.63\n", + "Iteration 135: Average Return = 163.06\n", + "Iteration 136: Average Return = 171.04\n", + "Iteration 137: Average Return = 182.55\n", + "Iteration 138: Average Return = 175.0\n", + "Iteration 139: Average Return = 175.44\n", + "Iteration 140: Average Return = 179.91\n", + "Iteration 141: Average Return = 185.5\n", + "Iteration 142: Average Return = 184.66\n", + "Iteration 143: Average Return = 188.3\n", + "Iteration 144: Average Return = 191.33\n", + "Iteration 145: Average Return = 190.91\n", + "Iteration 146: Average Return = 195.94\n", + "Solve at 146 iterations, which equals 14600 episodes.\n" + ] + }, + { + "data": { + "image/png": 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OnP/Gdlm3FnERhAuKlAiLAdTU1FBTUxN17pZbbgm9VkpFCUokf/M3fzOttk0U17vWYh06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\nLXv27IlZ09DQQFlZGaWlpbjdblavXh1al5MT7t4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "np.random.seed(seed)\n", + "tf.set_random_seed(seed)\n", + "prng.seed(seed)\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", + "baseline = None\n", + "\n", + "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate)\n", + "\n", + "# Train the policy optimizer\n", + "loss_list, avg_return_list = po.train()\n", + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" ] }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -453,6 +782,321 @@ "If you answer is right, your will solve CartPole with roughly ~ 80 iterations." ] }, + { + "cell_type": "code", + "execution_count": 8, + "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 13.04\n", + "Iteration 2: Average Return = 13.33\n", + "Iteration 3: Average Return = 13.5\n", + "Iteration 4: Average Return = 13.25\n", + "Iteration 5: Average Return = 14.7\n", + "Iteration 6: Average Return = 14.82\n", + "Iteration 7: Average Return = 15.63\n", + "Iteration 8: Average Return = 15.34\n", + "Iteration 9: Average Return = 15.65\n", + "Iteration 10: Average Return = 15.46\n", + "Iteration 11: Average Return = 16.44\n", + "Iteration 12: Average Return = 15.96\n", + "Iteration 13: Average Return = 18.14\n", + "Iteration 14: Average Return = 16.89\n", + "Iteration 15: Average Return = 16.83\n", + "Iteration 16: Average Return = 16.29\n", + "Iteration 17: Average Return = 16.78\n", + "Iteration 18: Average Return = 15.17\n", + "Iteration 19: Average Return = 15.17\n", + "Iteration 20: Average Return = 15.14\n", + "Iteration 21: Average Return = 16.42\n", + "Iteration 22: Average Return = 16.45\n", + "Iteration 23: Average Return = 15.65\n", + "Iteration 24: Average Return = 17.17\n", + "Iteration 25: Average Return = 15.01\n", + "Iteration 26: Average Return = 16.13\n", + "Iteration 27: Average Return = 15.69\n", + "Iteration 28: Average Return = 15.35\n", + "Iteration 29: Average Return = 15.6\n", + "Iteration 30: Average Return = 15.56\n", + "Iteration 31: Average Return = 14.3\n", + "Iteration 32: Average Return = 14.78\n", + "Iteration 33: Average Return = 15.74\n", + "Iteration 34: Average Return = 16.54\n", + "Iteration 35: Average Return = 16.12\n", + "Iteration 36: Average Return = 15.87\n", + "Iteration 37: Average Return = 16.6\n", + "Iteration 38: Average Return = 16.09\n", + "Iteration 39: Average Return = 15.54\n", + "Iteration 40: Average Return = 15.47\n", + "Iteration 41: Average Return = 14.62\n", + "Iteration 42: Average Return = 14.51\n", + "Iteration 43: Average Return = 15.94\n", + "Iteration 44: Average Return = 16.92\n", + "Iteration 45: Average Return = 14.93\n", + "Iteration 46: Average Return = 14.81\n", + "Iteration 47: Average Return = 15.34\n", + "Iteration 48: Average Return = 16.16\n", + "Iteration 49: Average Return = 16.45\n", + "Iteration 50: Average Return = 16.25\n", + "Iteration 51: Average Return = 14.47\n", + "Iteration 52: Average Return = 14.88\n", + "Iteration 53: Average Return = 14.26\n", + "Iteration 54: Average Return = 14.64\n", + "Iteration 55: Average Return = 15.21\n", + "Iteration 56: Average Return = 15.2\n", + "Iteration 57: Average Return = 14.98\n", + "Iteration 58: Average Return = 14.82\n", + "Iteration 59: Average Return = 14.21\n", + "Iteration 60: Average Return = 14.13\n", + "Iteration 61: Average Return = 14.32\n", + "Iteration 62: Average Return = 13.45\n", + "Iteration 63: Average Return = 13.05\n", + "Iteration 64: Average Return = 13.77\n", + "Iteration 65: Average Return = 13.41\n", + "Iteration 66: Average Return = 14.11\n", + "Iteration 67: Average Return = 13.57\n", + "Iteration 68: Average Return = 13.31\n", + "Iteration 69: Average Return = 12.98\n", + "Iteration 70: Average Return = 13.5\n", + "Iteration 71: Average Return = 14.09\n", + "Iteration 72: Average Return = 14.01\n", + "Iteration 73: Average Return = 13.79\n", + "Iteration 74: Average Return = 13.67\n", + "Iteration 75: Average Return = 13.98\n", + "Iteration 76: Average Return = 15.14\n", + "Iteration 77: Average Return = 14.51\n", + "Iteration 78: Average Return = 15.12\n", + "Iteration 79: Average Return = 13.54\n", + "Iteration 80: Average Return = 14.79\n", + "Iteration 81: Average Return = 14.71\n", + "Iteration 82: Average Return = 15.62\n", + "Iteration 83: Average Return = 14.48\n", + "Iteration 84: Average Return = 14.5\n", + "Iteration 85: Average Return = 14.95\n", + "Iteration 86: Average Return = 14.92\n", + "Iteration 87: Average Return = 15.19\n", + "Iteration 88: Average Return = 14.3\n", + "Iteration 89: Average Return = 14.7\n", + "Iteration 90: Average Return = 14.63\n", + "Iteration 91: Average Return = 14.82\n", + "Iteration 92: Average Return = 14.09\n", + "Iteration 93: Average Return = 13.96\n", + "Iteration 94: Average Return = 14.38\n", + "Iteration 95: Average Return = 14.05\n", + "Iteration 96: Average Return = 13.24\n", + "Iteration 97: Average Return = 14.68\n", + "Iteration 98: Average Return = 13.4\n", + "Iteration 99: Average Return = 14.12\n", + "Iteration 100: Average Return = 13.57\n", + "Iteration 101: Average Return = 13.15\n", + "Iteration 102: Average Return = 13.51\n", + "Iteration 103: Average Return = 13.74\n", + "Iteration 104: Average Return = 13.38\n", + "Iteration 105: Average Return = 14.17\n", + "Iteration 106: Average Return = 14.54\n", + "Iteration 107: Average Return = 14.25\n", + "Iteration 108: Average Return = 14.51\n", + "Iteration 109: Average Return = 14.59\n", + "Iteration 110: Average Return = 14.69\n", + "Iteration 111: Average Return = 14.52\n", + "Iteration 112: Average Return = 14.16\n", + "Iteration 113: Average Return = 14.28\n", + "Iteration 114: Average Return = 13.4\n", + "Iteration 115: Average Return = 13.32\n", + "Iteration 116: Average Return = 13.17\n", + "Iteration 117: Average Return = 14.2\n", + "Iteration 118: Average Return = 13.23\n", + "Iteration 119: Average Return = 13.25\n", + "Iteration 120: Average Return = 13.8\n", + "Iteration 121: Average Return = 13.71\n", + "Iteration 122: Average Return = 14.11\n", + "Iteration 123: Average Return = 14.19\n", + "Iteration 124: Average Return = 14.02\n", + "Iteration 125: Average Return = 13.85\n", + "Iteration 126: Average Return = 14.35\n", + "Iteration 127: Average Return = 14.25\n", + "Iteration 128: Average Return = 13.6\n", + "Iteration 129: Average Return = 14.21\n", + "Iteration 130: Average Return = 13.52\n", + "Iteration 131: Average Return = 14.58\n", + "Iteration 132: Average Return = 13.75\n", + "Iteration 133: Average Return = 14.25\n", + "Iteration 134: Average Return = 14.22\n", + "Iteration 135: Average Return = 15.76\n", + "Iteration 136: Average Return = 15.14\n", + "Iteration 137: Average Return = 14.83\n", + "Iteration 138: Average Return = 13.86\n", + "Iteration 139: Average Return = 14.37\n", + "Iteration 140: Average Return = 14.05\n", + "Iteration 141: Average Return = 14.62\n", + "Iteration 142: Average Return = 13.5\n", + "Iteration 143: Average Return = 14.46\n", + "Iteration 144: Average Return = 14.03\n", + "Iteration 145: Average Return = 12.68\n", + "Iteration 146: Average Return = 13.59\n", + "Iteration 147: Average Return = 13.09\n", + "Iteration 148: Average Return = 13.47\n", + "Iteration 149: Average Return = 13.17\n", + "Iteration 150: Average Return = 13.19\n", + "Iteration 151: Average Return = 12.7\n", + "Iteration 152: Average Return = 13.17\n", + "Iteration 153: Average Return = 13.26\n", + "Iteration 154: Average Return = 12.25\n", + "Iteration 155: Average Return = 13.21\n", + "Iteration 156: Average Return = 12.73\n", + "Iteration 157: Average Return = 13.68\n", + "Iteration 158: Average Return = 13.39\n", + "Iteration 159: Average Return = 13.2\n", + "Iteration 160: Average Return = 13.09\n", + "Iteration 161: Average Return = 13.5\n", + "Iteration 162: Average Return = 13.58\n", + "Iteration 163: Average Return = 13.54\n", + "Iteration 164: Average Return = 13.67\n", + "Iteration 165: Average Return = 13.94\n", + "Iteration 166: Average Return = 14.23\n", + "Iteration 167: Average Return = 13.15\n", + "Iteration 168: Average Return = 13.25\n", + "Iteration 169: Average Return = 13.56\n", + "Iteration 170: Average Return = 13.56\n", + "Iteration 171: Average Return = 13.46\n", + "Iteration 172: Average Return = 14.43\n", + "Iteration 173: Average Return = 13.39\n", + "Iteration 174: Average Return = 14.42\n", + "Iteration 175: Average Return = 15.01\n", + "Iteration 176: Average Return = 14.26\n", + "Iteration 177: Average Return = 14.28\n", + "Iteration 178: Average Return = 14.65\n", + "Iteration 179: Average Return = 13.98\n", + "Iteration 180: Average Return = 13.66\n", + "Iteration 181: Average Return = 13.84\n", + "Iteration 182: Average Return = 13.44\n", + "Iteration 183: Average Return = 14.55\n", + "Iteration 184: Average Return = 14.4\n", + "Iteration 185: Average Return = 13.91\n", + "Iteration 186: Average Return = 14.42\n", + "Iteration 187: Average Return = 14.44\n", + "Iteration 188: Average Return = 14.05\n", + "Iteration 189: Average Return = 15.29\n", + "Iteration 190: Average Return = 15.1\n", + "Iteration 191: Average Return = 14.58\n", + "Iteration 192: Average Return = 15.62\n", + "Iteration 193: Average Return = 15.9\n", + "Iteration 194: Average Return = 16.09\n", + "Iteration 195: Average Return = 16.81\n", + "Iteration 196: Average Return = 16.09\n", + "Iteration 197: Average Return = 16.93\n", + "Iteration 198: Average Return = 15.78\n", + "Iteration 199: Average Return = 16.44\n", + "Iteration 200: Average Return = 16.64\n" + ] + } + ], + "source": [ + "np.random.seed(seed)\n", + "tf.set_random_seed(seed)\n", + "prng.seed(seed)\n", + "\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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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v7YZ13zyraXXxF/QZwIhBXHO+iWdZ+FzEMdwQ80IiCQAgp6xjSoWbxRxKBZCkls6AGE7t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\nMxPlocgJ9n4+C+RzbONahSfDwT4XAAj3cnJpY7Vf0pthY6x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iR1MQQqEs+ILA5wdiUWZOEpQFlbPIrr8LdMdWbWMy2bA+BSEUONzhmr9itopu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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -477,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -523,6 +1167,14 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " \"\"\"\n", + " y[0] = x[0] + discount(x[1],1) + discount(x[2],2) + ... + discount(x[len(x)-1], len(x)-1)\n", + " y[1] = x[1] + discount(x[2],1) + discount(x[3],2) + ... + discount(x[len(x)-1], len(x)-2)\n", + " ...\n", + " y[n] = x[n] + discount(x[n], 1) + ... + discount(x[len(x)-1], len(x)-n+1)\n", + " = x[n] + discount(y[n+1],1)\n", + " \"\"\"\n", + " a = util.discount(a, discount_rate*LAMBDA)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -543,103 +1195,223 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 25.12\n", - "Iteration 2: Average Return = 31.17\n", - "Iteration 3: Average Return = 30.07\n", - "Iteration 4: Average Return = 31.98\n", - "Iteration 5: Average Return = 36.77\n", - "Iteration 6: Average Return = 36.22\n", - "Iteration 7: Average Return = 43.52\n", - "Iteration 8: Average Return = 45.12\n", - "Iteration 9: Average Return = 50.86\n", - "Iteration 10: Average Return = 58.81\n", - "Iteration 11: Average Return = 58.87\n", - "Iteration 12: Average Return = 65.66\n", - "Iteration 13: Average Return = 69.72\n", - "Iteration 14: Average Return = 76.32\n", - "Iteration 15: Average Return = 77.74\n", - "Iteration 16: Average Return = 78.17\n", - "Iteration 17: Average Return = 94.97\n", - "Iteration 18: Average Return = 89.34\n", - "Iteration 19: Average Return = 98.15\n", - "Iteration 20: Average Return = 103.35\n", - "Iteration 21: Average Return = 106.54\n", - "Iteration 22: Average Return = 109.03\n", - "Iteration 23: Average Return = 113.63\n", - "Iteration 24: Average Return = 119.11\n", - "Iteration 25: Average Return = 115.67\n", - "Iteration 26: Average Return = 126.51\n", - "Iteration 27: Average Return = 131.33\n", - "Iteration 28: Average Return = 138.83\n", - "Iteration 29: Average Return = 143.7\n", - "Iteration 30: Average Return = 146.15\n", - "Iteration 31: Average Return = 146.41\n", - "Iteration 32: Average Return = 157.34\n", - "Iteration 33: Average Return = 160.51\n", - "Iteration 34: Average Return = 159.67\n", - "Iteration 35: Average Return = 169.42\n", - "Iteration 36: Average Return = 170.71\n", - "Iteration 37: Average Return = 174.41\n", - "Iteration 38: Average Return = 172.93\n", - "Iteration 39: Average Return = 173.29\n", - "Iteration 40: Average Return = 177.32\n", - "Iteration 41: Average Return = 177.14\n", - "Iteration 42: Average Return = 179.85\n", - "Iteration 43: Average Return = 181.82\n", - "Iteration 44: Average Return = 182.0\n", - "Iteration 45: Average Return = 181.89\n", - "Iteration 46: Average Return = 183.19\n", - "Iteration 47: Average Return = 183.87\n", - "Iteration 48: Average Return = 183.26\n", - "Iteration 49: Average Return = 183.27\n", - "Iteration 50: Average Return = 189.11\n", - "Iteration 51: Average Return = 181.45\n", - "Iteration 52: Average Return = 186.91\n", - "Iteration 53: Average Return = 188.84\n", - "Iteration 54: Average Return = 189.76\n", - "Iteration 55: Average Return = 189.51\n", - "Iteration 56: Average Return = 186.36\n", - "Iteration 57: Average Return = 190.55\n", - "Iteration 58: Average Return = 189.35\n", - "Iteration 59: Average Return = 189.84\n", - "Iteration 60: Average Return = 187.14\n", - "Iteration 61: Average Return = 191.82\n", - "Iteration 62: Average Return = 189.32\n", - "Iteration 63: Average Return = 190.74\n", - "Iteration 64: Average Return = 188.13\n", - "Iteration 65: Average Return = 190.99\n", - "Iteration 66: Average Return = 189.23\n", - "Iteration 67: Average Return = 186.98\n", - "Iteration 68: Average Return = 188.0\n", - "Iteration 69: Average Return = 191.68\n", - "Iteration 70: Average Return = 188.03\n", - "Iteration 71: Average Return = 193.07\n", - "Iteration 72: Average Return = 191.96\n", - "Iteration 73: Average Return = 189.53\n", - "Iteration 74: Average Return = 186.71\n", - "Iteration 75: Average Return = 190.05\n", - "Iteration 76: Average Return = 191.1\n", - "Iteration 77: Average Return = 193.49\n", - "Iteration 78: Average Return = 188.66\n", - "Iteration 79: Average Return = 191.49\n", - "Iteration 80: Average Return = 191.68\n", - "Iteration 81: Average Return = 193.19\n", - "Iteration 82: Average Return = 193.87\n", - "Iteration 83: Average Return = 195.04\n", - "Solve at 83 iterations, which equals 8300 episodes.\n" + "Iteration 1: Average Return = 25.53\n", + "Iteration 2: Average Return = 31.37\n", + "Iteration 3: Average Return = 34.29\n", + "Iteration 4: Average Return = 34.88\n", + "Iteration 5: Average Return = 41.4\n", + "Iteration 6: Average Return = 40.2\n", + "Iteration 7: Average Return = 46.82\n", + "Iteration 8: Average Return = 46.66\n", + "Iteration 9: Average Return = 48.57\n", + "Iteration 10: Average Return = 55.08\n", + "Iteration 11: Average Return = 52.67\n", + "Iteration 12: Average Return = 59.6\n", + "Iteration 13: Average Return = 51.23\n", + "Iteration 14: Average Return = 56.83\n", + "Iteration 15: Average Return = 57.68\n", + "Iteration 16: Average Return = 59.44\n", + "Iteration 17: Average Return = 61.41\n", + "Iteration 18: Average Return = 67.33\n", + "Iteration 19: Average Return = 66.32\n", + "Iteration 20: Average Return = 69.83\n", + "Iteration 21: Average Return = 68.95\n", + "Iteration 22: Average Return = 70.61\n", + "Iteration 23: Average Return = 78.92\n", + "Iteration 24: Average Return = 79.31\n", + "Iteration 25: Average Return = 76.66\n", + "Iteration 26: Average Return = 76.2\n", + "Iteration 27: Average Return = 86.2\n", + "Iteration 28: Average Return = 89.08\n", + "Iteration 29: Average Return = 95.4\n", + "Iteration 30: Average Return = 91.74\n", + "Iteration 31: Average Return = 103.5\n", + "Iteration 32: Average Return = 104.6\n", + "Iteration 33: Average Return = 118.86\n", + "Iteration 34: Average Return = 123.13\n", + "Iteration 35: Average Return = 130.14\n", + "Iteration 36: Average Return = 146.35\n", + "Iteration 37: Average Return = 153.55\n", + "Iteration 38: Average Return = 152.74\n", + "Iteration 39: Average Return = 161.34\n", + "Iteration 40: Average Return = 160.96\n", + "Iteration 41: Average Return = 145.61\n", + "Iteration 42: Average Return = 152.06\n", + "Iteration 43: Average Return = 159.83\n", + "Iteration 44: Average Return = 157.32\n", + "Iteration 45: Average Return = 162.76\n", + "Iteration 46: Average Return = 155.33\n", + "Iteration 47: Average Return = 167.84\n", + "Iteration 48: Average Return = 176.73\n", + "Iteration 49: Average Return = 174.29\n", + "Iteration 50: Average Return = 179.89\n", + "Iteration 51: Average Return = 171.8\n", + "Iteration 52: Average Return = 176.05\n", + "Iteration 53: Average Return = 173.84\n", + "Iteration 54: Average Return = 169.44\n", + "Iteration 55: Average Return = 170.96\n", + "Iteration 56: Average Return = 164.92\n", + "Iteration 57: Average Return = 158.49\n", + "Iteration 58: Average Return = 163.73\n", + "Iteration 59: Average Return = 158.61\n", + "Iteration 60: Average Return = 161.05\n", + "Iteration 61: Average Return = 165.82\n", + "Iteration 62: Average Return = 160.82\n", + "Iteration 63: Average Return = 169.3\n", + "Iteration 64: Average Return = 164.33\n", + "Iteration 65: Average Return = 171.14\n", + "Iteration 66: Average Return = 166.77\n", + "Iteration 67: Average Return = 160.26\n", + "Iteration 68: Average Return = 157.82\n", + "Iteration 69: Average Return = 148.6\n", + "Iteration 70: Average Return = 146.72\n", + "Iteration 71: Average Return = 144.75\n", + "Iteration 72: Average Return = 124.52\n", + "Iteration 73: Average Return = 119.5\n", + "Iteration 74: Average Return = 126.89\n", + "Iteration 75: Average Return = 120.23\n", + "Iteration 76: Average Return = 122.81\n", + "Iteration 77: Average Return = 122.22\n", + "Iteration 78: Average Return = 130.45\n", + "Iteration 79: Average Return = 128.67\n", + "Iteration 80: Average Return = 128.77\n", + "Iteration 81: Average Return = 120.16\n", + "Iteration 82: Average Return = 121.99\n", + "Iteration 83: Average Return = 111.23\n", + "Iteration 84: Average Return = 116.44\n", + "Iteration 85: Average Return = 105.62\n", + "Iteration 86: Average Return = 118.94\n", + "Iteration 87: Average Return = 126.52\n", + "Iteration 88: Average Return = 114.6\n", + "Iteration 89: Average Return = 119.72\n", + "Iteration 90: Average Return = 117.02\n", + "Iteration 91: Average Return = 119.07\n", + "Iteration 92: Average Return = 125.01\n", + "Iteration 93: Average Return = 113.49\n", + "Iteration 94: Average Return = 117.56\n", + "Iteration 95: Average Return = 117.51\n", + "Iteration 96: Average Return = 120.86\n", + "Iteration 97: Average Return = 107.71\n", + "Iteration 98: Average Return = 106.71\n", + "Iteration 99: Average Return = 101.24\n", + "Iteration 100: Average Return = 108.92\n", + "Iteration 101: Average Return = 107.69\n", + "Iteration 102: Average Return = 99.36\n", + "Iteration 103: Average Return = 107.93\n", + "Iteration 104: Average Return = 109.04\n", + "Iteration 105: Average Return = 106.95\n", + "Iteration 106: Average Return = 110.89\n", + "Iteration 107: Average Return = 114.76\n", + "Iteration 108: Average Return = 121.82\n", + "Iteration 109: Average Return = 124.3\n", + "Iteration 110: Average Return = 128.27\n", + "Iteration 111: Average Return = 138.37\n", + "Iteration 112: Average Return = 142.31\n", + "Iteration 113: Average Return = 148.54\n", + "Iteration 114: Average Return = 151.29\n", + "Iteration 115: Average Return = 145.98\n", + "Iteration 116: Average Return = 148.93\n", + "Iteration 117: Average Return = 151.53\n", + "Iteration 118: Average Return = 143.81\n", + "Iteration 119: Average Return = 148.03\n", + "Iteration 120: Average Return = 147.62\n", + "Iteration 121: Average Return = 141.63\n", + "Iteration 122: Average Return = 139.6\n", + "Iteration 123: Average Return = 127.71\n", + "Iteration 124: Average Return = 130.2\n", + "Iteration 125: Average Return = 121.71\n", + "Iteration 126: Average Return = 114.16\n", + "Iteration 127: Average Return = 112.36\n", + "Iteration 128: Average Return = 116.12\n", + "Iteration 129: Average Return = 114.3\n", + "Iteration 130: Average Return = 117.15\n", + "Iteration 131: Average Return = 128.88\n", + "Iteration 132: Average Return = 123.62\n", + "Iteration 133: Average Return = 129.99\n", + "Iteration 134: Average Return = 125.04\n", + "Iteration 135: Average Return = 131.42\n", + "Iteration 136: Average Return = 127.78\n", + "Iteration 137: Average Return = 128.03\n", + "Iteration 138: Average Return = 123.06\n", + "Iteration 139: Average Return = 118.73\n", + "Iteration 140: Average Return = 121.81\n", + "Iteration 141: Average Return = 131.12\n", + "Iteration 142: Average Return = 136.83\n", + "Iteration 143: Average Return = 136.78\n", + "Iteration 144: Average Return = 134.48\n", + "Iteration 145: Average Return = 126.52\n", + "Iteration 146: Average Return = 136.42\n", + "Iteration 147: Average Return = 135.87\n", + "Iteration 148: Average Return = 135.61\n", + "Iteration 149: Average Return = 141.36\n", + "Iteration 150: Average Return = 150.1\n", + "Iteration 151: Average Return = 146.61\n", + "Iteration 152: Average Return = 147.94\n", + "Iteration 153: Average Return = 150.84\n", + "Iteration 154: Average Return = 146.08\n", + "Iteration 155: Average Return = 143.8\n", + "Iteration 156: Average Return = 150.74\n", + "Iteration 157: Average Return = 144.83\n", + "Iteration 158: Average Return = 148.24\n", + "Iteration 159: Average Return = 146.53\n", + "Iteration 160: Average Return = 141.88\n", + "Iteration 161: Average Return = 144.06\n", + "Iteration 162: Average Return = 140.69\n", + "Iteration 163: Average Return = 147.89\n", + "Iteration 164: Average Return = 146.76\n", + "Iteration 165: Average Return = 144.28\n", + "Iteration 166: Average Return = 140.49\n", + "Iteration 167: Average Return = 138.74\n", + "Iteration 168: Average Return = 134.06\n", + "Iteration 169: Average Return = 129.78\n", + "Iteration 170: Average Return = 130.54\n", + "Iteration 171: Average Return = 122.87\n", + "Iteration 172: Average Return = 114.02\n", + "Iteration 173: Average Return = 114.84\n", + "Iteration 174: Average Return = 104.79\n", + "Iteration 175: Average Return = 111.36\n", + "Iteration 176: Average Return = 103.88\n", + "Iteration 177: Average Return = 107.02\n", + "Iteration 178: Average Return = 105.65\n", + "Iteration 179: Average Return = 103.24\n", + "Iteration 180: Average Return = 99.1\n", + "Iteration 181: Average Return = 114.61\n", + "Iteration 182: Average Return = 116.86\n", + "Iteration 183: Average Return = 110.43\n", + "Iteration 184: Average Return = 115.65\n", + "Iteration 185: Average Return = 111.54\n", + "Iteration 186: Average Return = 121.67\n", + "Iteration 187: Average Return = 119.26\n", + "Iteration 188: Average Return = 122.05\n", + "Iteration 189: Average Return = 120.66\n", + "Iteration 190: Average Return = 125.58\n", + "Iteration 191: Average Return = 126.43\n", + "Iteration 192: Average Return = 130.43\n", + "Iteration 193: Average Return = 126.92\n", + "Iteration 194: Average Return = 132.38\n", + "Iteration 195: Average Return = 135.63\n", + "Iteration 196: Average Return = 132.69\n", + "Iteration 197: Average Return = 140.79\n", + "Iteration 198: Average Return = 137.78\n", + "Iteration 199: Average Return = 136.23\n", + "Iteration 200: Average Return = 139.68\n" ] } ], "source": [ + "np.random.seed(seed)\n", + "tf.set_random_seed(seed)\n", + "prng.seed(seed)\n", + "\n", "sess.run(tf.global_variables_initializer())\n", "\n", "n_iter = 200\n", @@ -658,14 +1430,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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NnnIp+h+voj98xxgdFmnB9It7UAHNf2mdyjTlMpx5X6H/9TZE90N1YIkjFR6J6Z7Fxl/w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\nDTX+jM4LWgjRo/i85iLaToVFoC66Cv3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8BSooRCys6SR0mg7Az6E0oUPPn4Hyr9+uS8sSKXSsgCp0nOZCR9SzAlShQww+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\nAFRUVNDc3BxxTEtLC06nk8LCQkwmE+Xl5aHjZsyYwfTpkVe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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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cNBg7ATDfzPVffo/e/HdcKx8dcF01uqTQtJxqq3E+cicoG+rqhTBhsvu96FMnICAAmhrR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\nl8CcSwhyOMAPB9OEEEOLx+U+2lVWVlJ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -699,9 +1471,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [conda root]", "language": "python", - "name": "python3" + "name": "conda-root-py" }, "language_info": { "codemirror_mode": { @@ -713,7 +1485,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.6.1" } }, "nbformat": 4, diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 99fecf3..759ad66 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -30,6 +30,23 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> + with tf.variable_scope("fc1"): + weights = tf.get_variable(name="weights", + initializer=tf.truncated_normal(shape=[in_dim, hidden_dim])) + biases = tf.get_variable(name="biases", + initializer=tf.constant(0., shape=[hidden_dim])) + logit = tf.nn.xw_plus_b(self._observations, weights, biases) + act = tf.tanh(logit) + + with tf.variable_scope("fc2"): + weights = tf.get_variable(name="weights", + initializer=tf.truncated_normal(shape=[hidden_dim, out_dim])) + biases = tf.get_variable(name="biases", + initializer=tf.constant(0., shape=[out_dim])) + logit = tf.nn.xw_plus_b(act, weights, biases) + softmax = tf.nn.softmax(logit) + + probs = softmax # <<<<<<<< # -------------------------------------------------- @@ -50,6 +67,7 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): # 2. Add index of the action chosen at each timestep # e.g., if index of the action chosen at timestep t = 0 is 1, and index of the action # chosen at timestep = 1 is 0, then `action_idxs_flattened` == [0, 2] + [1, 0] = [1, 2] + action_idxs_flattened += self._actions # 3. Gather the probability of action at each timestep @@ -72,6 +90,7 @@ 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) @@ -93,6 +112,17 @@ def act(self, observation): # `action_probs` is an array that has shape [1, action_space], it contains the probability of each action # sample an action according to `action_probs` + """ + when + action_probs = [0.01, 0.01, 0.97, 0.01] + then + cs = [0.01, 0.02, 0.99, 1.] + cs < random() could return [True, True, False, False] with hige probability + idx = 2 + when + action_probs = [0.25, 0.25, 0.25, 0.25] + idx = randint(3) + """ cs = np.cumsum(action_probs) idx = sum(cs < np.random.rand()) return idx diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..e67b439 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,6 +32,9 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> + #b = np.concatenate([b,[0]]) + y = x + discount_rate*b + return y # <<<<<<<<<<<<<<<<<<<<<<<<<<<< def plot_curve(data, key, filename=None): @@ -46,5 +49,6 @@ def plot_curve(data, key, filename=None): plt.close() def discount(x, discount_factor): + """y[n] = x[n]+y[n-1]*discount_factor""" return scipy.signal.lfilter([1.0], [1.0, -discount_factor], x[::-1])[::-1] From 36ec90202145e385b87aaee9965d92e12455758c Mon Sep 17 00:00:00 2001 From: wengbrian Date: Thu, 9 Nov 2017 21:21:09 +0800 Subject: [PATCH 2/4] set same seed --- Lab3-policy-gradient.ipynb | 1489 +++++++++++++++--------------------- policy_gradient/policy.py | 21 +- policy_gradient/util.py | 4 +- 3 files changed, 619 insertions(+), 895 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 7ce9811..80e22c6 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -2,11 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# Automatically reload changes to external code\n", "%load_ext autoreload\n", @@ -28,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ @@ -38,7 +45,8 @@ "from policy_gradient import util\n", "from policy_gradient.policy import CategoricalPolicy\n", "from policy_gradient.baselines.linear_feature_baseline import LinearFeatureBaseline\n", - "from gym.spaces import prng \n", + "from gym.spaces import prng\n", + "\n", "# CartPole-v0 is a MDP with finite state and action space. \n", "# In this environment, A pendulum is attached by an un-actuated joint to a cart, \n", "# and the goal is to prevent it from falling over. You can apply a force of +1 or -1 to the cart.\n", @@ -92,33 +100,17 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 9, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/brian/anaconda3/lib/python3.6/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" - ] - } - ], + "outputs": [], "source": [ - "tf.reset_default_graph()\n", - "sess = tf.Session()\n", "# Construct a neural network to represent policy which maps observed state to action.\n", "# following function extract num feature and action from Box and Discreate\n", "in_dim = util.flatten_space(env.observation_space)\n", "out_dim = util.flatten_space(env.action_space)\n", - "hidden_dim = 8\n", - "\n", - "# Initialize your policy\n", - "with tf.variable_scope(\"policy\"):\n", - " opt_p = tf.train.AdamOptimizer(learning_rate=0.01)\n", - " policy = CategoricalPolicy(in_dim, out_dim, hidden_dim, opt_p, sess)\n" + "hidden_dim = 8" ] }, { @@ -144,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 10, "metadata": { "collapsed": true }, @@ -167,10 +159,11 @@ " actions = []\n", " rewards = []\n", " ob = self.env.reset()\n", - "\n", + " #print('ob:', ob)\n", " # sample a batch of trajectory\n", " for _ in range(self.path_length):\n", " a = self.policy.act(ob.reshape(1, -1))\n", + " #print('a', a)\n", " next_ob, r, done, _ = self.env.step(a)\n", " obs.append(ob)\n", " actions.append(a)\n", @@ -252,193 +245,124 @@ { "cell_type": "code", "execution_count": 5, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brian/anaconda3/lib/python3.6/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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 9.78\n", - "Iteration 2: Average Return = 9.91\n", - "Iteration 3: Average Return = 9.62\n", - "Iteration 4: Average Return = 9.89\n", - "Iteration 5: Average Return = 9.97\n", - "Iteration 6: Average Return = 9.88\n", - "Iteration 7: Average Return = 10.01\n", - "Iteration 8: Average Return = 10.0\n", - "Iteration 9: Average Return = 10.71\n", - "Iteration 10: Average Return = 10.35\n", - "Iteration 11: Average Return = 10.61\n", - "Iteration 12: Average Return = 11.29\n", - "Iteration 13: Average Return = 11.0\n", - "Iteration 14: Average Return = 10.8\n", - "Iteration 15: Average Return = 11.48\n", - "Iteration 16: Average Return = 12.4\n", - "Iteration 17: Average Return = 13.11\n", - "Iteration 18: Average Return = 13.61\n", - "Iteration 19: Average Return = 14.05\n", - "Iteration 20: Average Return = 13.94\n", - "Iteration 21: Average Return = 14.82\n", - "Iteration 22: Average Return = 16.58\n", - "Iteration 23: Average Return = 17.7\n", - "Iteration 24: Average Return = 19.3\n", - "Iteration 25: Average Return = 20.83\n", - "Iteration 26: Average Return = 23.03\n", - "Iteration 27: Average Return = 24.82\n", - "Iteration 28: Average Return = 24.66\n", - "Iteration 29: Average Return = 27.61\n", - "Iteration 30: Average Return = 28.64\n", - "Iteration 31: Average Return = 30.24\n", - "Iteration 32: Average Return = 32.75\n", - "Iteration 33: Average Return = 36.21\n", - "Iteration 34: Average Return = 34.75\n", - "Iteration 35: Average Return = 39.61\n", - "Iteration 36: Average Return = 38.22\n", - "Iteration 37: Average Return = 41.43\n", - "Iteration 38: Average Return = 45.36\n", - "Iteration 39: Average Return = 42.73\n", - "Iteration 40: Average Return = 43.14\n", - "Iteration 41: Average Return = 44.66\n", - "Iteration 42: Average Return = 43.44\n", - "Iteration 43: Average Return = 43.91\n", - "Iteration 44: Average Return = 46.07\n", - "Iteration 45: Average Return = 49.44\n", - "Iteration 46: Average Return = 51.79\n", - "Iteration 47: Average Return = 47.26\n", - "Iteration 48: Average Return = 48.62\n", - "Iteration 49: Average Return = 45.62\n", - "Iteration 50: Average Return = 50.1\n", - "Iteration 51: Average Return = 51.33\n", - "Iteration 52: Average Return = 51.93\n", - "Iteration 53: Average Return = 49.32\n", - "Iteration 54: Average Return = 48.41\n", - "Iteration 55: Average Return = 50.64\n", - "Iteration 56: Average Return = 52.25\n", - "Iteration 57: Average Return = 55.05\n", - "Iteration 58: Average Return = 52.15\n", - "Iteration 59: Average Return = 54.63\n", - "Iteration 60: Average Return = 53.32\n", - "Iteration 61: Average Return = 54.04\n", - "Iteration 62: Average Return = 53.59\n", - "Iteration 63: Average Return = 54.68\n", - "Iteration 64: Average Return = 58.52\n", - "Iteration 65: Average Return = 54.29\n", - "Iteration 66: Average Return = 57.1\n", - "Iteration 67: Average Return = 57.22\n", - "Iteration 68: Average Return = 55.15\n", - "Iteration 69: Average Return = 55.59\n", - "Iteration 70: Average Return = 58.47\n", - "Iteration 71: Average Return = 56.14\n", - "Iteration 72: Average Return = 58.26\n", - "Iteration 73: Average Return = 61.07\n", - "Iteration 74: Average Return = 60.72\n", - "Iteration 75: Average Return = 57.85\n", - "Iteration 76: Average Return = 59.45\n", - "Iteration 77: Average Return = 65.12\n", - "Iteration 78: Average Return = 57.5\n", - "Iteration 79: Average Return = 65.87\n", - "Iteration 80: Average Return = 63.98\n", - "Iteration 81: Average Return = 64.13\n", - "Iteration 82: Average Return = 63.81\n", - "Iteration 83: Average Return = 69.91\n", - "Iteration 84: Average Return = 62.56\n", - "Iteration 85: Average Return = 64.11\n", - "Iteration 86: Average Return = 66.72\n", - "Iteration 87: Average Return = 68.87\n", - "Iteration 88: Average Return = 64.46\n", - "Iteration 89: Average Return = 72.02\n", - "Iteration 90: Average Return = 63.88\n", - "Iteration 91: Average Return = 64.75\n", - "Iteration 92: Average Return = 75.53\n", - "Iteration 93: Average Return = 71.1\n", - "Iteration 94: Average Return = 70.51\n", - "Iteration 95: Average Return = 74.13\n", - "Iteration 96: Average Return = 74.27\n", - "Iteration 97: Average Return = 71.16\n", - "Iteration 98: Average Return = 69.6\n", - "Iteration 99: Average Return = 76.86\n", - "Iteration 100: Average Return = 79.23\n", - "Iteration 101: Average Return = 81.9\n", - "Iteration 102: Average Return = 79.63\n", - "Iteration 103: Average Return = 85.59\n", - "Iteration 104: Average Return = 89.9\n", - "Iteration 105: Average Return = 87.23\n", - "Iteration 106: Average Return = 92.44\n", - "Iteration 107: Average Return = 98.98\n", - "Iteration 108: Average Return = 96.52\n", - "Iteration 109: Average Return = 97.92\n", - "Iteration 110: Average Return = 108.71\n", - "Iteration 111: Average Return = 108.96\n", - "Iteration 112: Average Return = 118.2\n", - "Iteration 113: Average Return = 117.59\n", - "Iteration 114: Average Return = 129.08\n", - "Iteration 115: Average Return = 126.65\n", - "Iteration 116: Average Return = 129.7\n", - "Iteration 117: Average Return = 138.26\n", - "Iteration 118: Average Return = 140.57\n", - "Iteration 119: Average Return = 139.42\n", - "Iteration 120: Average Return = 148.91\n", - "Iteration 121: Average Return = 159.76\n", - "Iteration 122: Average Return = 149.16\n", - "Iteration 123: Average Return = 146.21\n", - "Iteration 124: Average Return = 153.46\n", - "Iteration 125: Average Return = 151.09\n", - "Iteration 126: Average Return = 161.77\n", - "Iteration 127: Average Return = 165.94\n", - "Iteration 128: Average Return = 164.25\n", - "Iteration 129: Average Return = 166.33\n", - "Iteration 130: Average Return = 166.18\n", - "Iteration 131: Average Return = 171.2\n", - "Iteration 132: Average Return = 177.02\n", - "Iteration 133: Average Return = 173.16\n", - "Iteration 134: Average Return = 176.46\n", - "Iteration 135: Average Return = 177.24\n", - "Iteration 136: Average Return = 172.08\n", - "Iteration 137: Average Return = 178.23\n", - "Iteration 138: Average Return = 184.34\n", - "Iteration 139: Average Return = 180.04\n", - "Iteration 140: Average Return = 184.14\n", - "Iteration 141: Average Return = 180.19\n", - "Iteration 142: Average Return = 183.2\n", - "Iteration 143: Average Return = 180.57\n", - "Iteration 144: Average Return = 182.69\n", - "Iteration 145: Average Return = 177.81\n", - "Iteration 146: Average Return = 178.39\n", - "Iteration 147: Average Return = 176.92\n", - "Iteration 148: Average Return = 181.84\n", - "Iteration 149: Average Return = 178.98\n", - "Iteration 150: Average Return = 184.1\n", - "Iteration 151: Average Return = 175.61\n", - "Iteration 152: Average Return = 174.43\n", - "Iteration 153: Average Return = 180.05\n", - "Iteration 154: Average Return = 182.6\n", - "Iteration 155: Average Return = 176.48\n", - "Iteration 156: Average Return = 183.03\n", - "Iteration 157: Average Return = 176.71\n", - "Iteration 158: Average Return = 175.56\n", - "Iteration 159: Average Return = 178.99\n", - "Iteration 160: Average Return = 179.27\n", - "Iteration 161: Average Return = 178.33\n", - "Iteration 162: Average Return = 180.35\n", - "Iteration 163: Average Return = 185.4\n", - "Iteration 164: Average Return = 183.54\n", - "Iteration 165: Average Return = 180.17\n", - "Iteration 166: Average Return = 180.46\n", - "Iteration 167: Average Return = 183.94\n", - "Iteration 168: Average Return = 188.49\n", - "Iteration 169: Average Return = 184.41\n", - "Iteration 170: Average Return = 186.91\n", - "Iteration 171: Average Return = 185.91\n", - "Iteration 172: Average Return = 188.38\n", - "Iteration 173: Average Return = 186.76\n", - "Iteration 174: Average Return = 188.51\n", - "Iteration 175: Average Return = 190.29\n", - "Iteration 176: Average Return = 192.84\n", - "Iteration 177: Average Return = 189.23\n", - "Iteration 178: Average Return = 193.89\n", - "Iteration 179: Average Return = 193.57\n", - "Iteration 180: Average Return = 197.19\n", - "Solve at 180 iterations, which equals 18000 episodes.\n" + "Iteration 1: Average Return = 16.59\n", + "Iteration 2: Average Return = 17.78\n", + "Iteration 3: Average Return = 18.87\n", + "Iteration 4: Average Return = 20.87\n", + "Iteration 5: Average Return = 23.36\n", + "Iteration 6: Average Return = 22.52\n", + "Iteration 7: Average Return = 26.58\n", + "Iteration 8: Average Return = 27.34\n", + "Iteration 9: Average Return = 30.36\n", + "Iteration 10: Average Return = 37.65\n", + "Iteration 11: Average Return = 42.74\n", + "Iteration 12: Average Return = 51.35\n", + "Iteration 13: Average Return = 55.92\n", + "Iteration 14: Average Return = 57.83\n", + "Iteration 15: Average Return = 72.88\n", + "Iteration 16: Average Return = 69.98\n", + "Iteration 17: Average Return = 70.99\n", + "Iteration 18: Average Return = 74.38\n", + "Iteration 19: Average Return = 63.78\n", + "Iteration 20: Average Return = 66.51\n", + "Iteration 21: Average Return = 67.76\n", + "Iteration 22: Average Return = 70.43\n", + "Iteration 23: Average Return = 71.89\n", + "Iteration 24: Average Return = 73.42\n", + "Iteration 25: Average Return = 74.67\n", + "Iteration 26: Average Return = 80.32\n", + "Iteration 27: Average Return = 75.76\n", + "Iteration 28: Average Return = 88.74\n", + "Iteration 29: Average Return = 96.77\n", + "Iteration 30: Average Return = 97.64\n", + "Iteration 31: Average Return = 104.28\n", + "Iteration 32: Average Return = 109.01\n", + "Iteration 33: Average Return = 108.02\n", + "Iteration 34: Average Return = 122.58\n", + "Iteration 35: Average Return = 116.91\n", + "Iteration 36: Average Return = 122.66\n", + "Iteration 37: Average Return = 123.93\n", + "Iteration 38: Average Return = 123.32\n", + "Iteration 39: Average Return = 131.29\n", + "Iteration 40: Average Return = 121.08\n", + "Iteration 41: Average Return = 130.99\n", + "Iteration 42: Average Return = 134.25\n", + "Iteration 43: Average Return = 127.61\n", + "Iteration 44: Average Return = 132.07\n", + "Iteration 45: Average Return = 147.72\n", + "Iteration 46: Average Return = 138.03\n", + "Iteration 47: Average Return = 146.3\n", + "Iteration 48: Average Return = 149.87\n", + "Iteration 49: Average Return = 153.92\n", + "Iteration 50: Average Return = 152.69\n", + "Iteration 51: Average Return = 156.2\n", + "Iteration 52: Average Return = 150.4\n", + "Iteration 53: Average Return = 155.99\n", + "Iteration 54: Average Return = 167.31\n", + "Iteration 55: Average Return = 167.25\n", + "Iteration 56: Average Return = 171.6\n", + "Iteration 57: Average Return = 172.6\n", + "Iteration 58: Average Return = 168.7\n", + "Iteration 59: Average Return = 172.77\n", + "Iteration 60: Average Return = 167.4\n", + "Iteration 61: Average Return = 172.7\n", + "Iteration 62: Average Return = 173.75\n", + "Iteration 63: Average Return = 172.14\n", + "Iteration 64: Average Return = 179.4\n", + "Iteration 65: Average Return = 173.13\n", + "Iteration 66: Average Return = 182.1\n", + "Iteration 67: Average Return = 183.92\n", + "Iteration 68: Average Return = 180.05\n", + "Iteration 69: Average Return = 185.32\n", + "Iteration 70: Average Return = 181.05\n", + "Iteration 71: Average Return = 183.22\n", + "Iteration 72: Average Return = 185.14\n", + "Iteration 73: Average Return = 180.9\n", + "Iteration 74: Average Return = 188.63\n", + "Iteration 75: Average Return = 179.38\n", + "Iteration 76: Average Return = 184.54\n", + "Iteration 77: Average Return = 187.71\n", + "Iteration 78: Average Return = 186.6\n", + "Iteration 79: Average Return = 187.07\n", + "Iteration 80: Average Return = 192.91\n", + "Iteration 81: Average Return = 190.42\n", + "Iteration 82: Average Return = 183.82\n", + "Iteration 83: Average Return = 190.4\n", + "Iteration 84: Average Return = 190.11\n", + "Iteration 85: Average Return = 187.51\n", + "Iteration 86: Average Return = 193.74\n", + "Iteration 87: Average Return = 191.96\n", + "Iteration 88: Average Return = 190.64\n", + "Iteration 89: Average Return = 188.75\n", + "Iteration 90: Average Return = 188.89\n", + "Iteration 91: Average Return = 189.87\n", + "Iteration 92: Average Return = 194.76\n", + "Iteration 93: Average Return = 194.31\n", + "Iteration 94: Average Return = 191.81\n", + "Iteration 95: Average Return = 190.99\n", + "Iteration 96: Average Return = 191.44\n", + "Iteration 97: Average Return = 191.17\n", + "Iteration 98: Average Return = 192.09\n", + "Iteration 99: Average Return = 194.89\n", + "Iteration 100: Average Return = 194.06\n", + "Iteration 101: Average Return = 195.41\n", + "Solve at 101 iterations, which equals 10100 episodes.\n" ] } ], @@ -446,6 +370,13 @@ "np.random.seed(seed)\n", "tf.set_random_seed(seed)\n", "prng.seed(seed)\n", + "env.seed(seed)\n", + "\n", + "tf.reset_default_graph()\n", + "sess = tf.Session()\n", + "with tf.variable_scope(\"policy\"):\n", + " opt_p = tf.train.AdamOptimizer(learning_rate=0.01)\n", + " policy = CategoricalPolicy(in_dim, out_dim, hidden_dim, opt_p, sess)\n", "\n", "sess.run(tf.global_variables_initializer())\n", "\n", @@ -459,67 +390,12 @@ " discount_rate)\n", "\n", "# Train the policy optimizer\n", - "loss_list, avg_return_list = po.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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xqJFjYd8udMth9KdrYPxk1JgJUFdtang1lZBl9uBh6HDUzAvRn6zGXng7uuqA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\nbCQ1NZXFixfH7jXVQmuttd/v1zfffLM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t2pUnnnjCpUGFEOen03dAcBiERVgdRTRCThULwzAYPnw4w4cPP+t6Hx+feg0l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\nCCUlJTz55JNERdW+7aCUOuu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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "loss_list, avg_return_list = po.train()\n", + "\n", "util.plot_curve(loss_list, \"loss\")\n", "util.plot_curve(avg_return_list, \"average return\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "np.random.seed(seed)\n", - "tf.set_random_seed(seed)\n", - "prng.seed(seed)\n", - "\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", - "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": "markdown", "metadata": {}, @@ -552,167 +428,106 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "scrolled": false + "scrolled": true }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brian/anaconda3/lib/python3.6/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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 15.56\n", - "Iteration 2: Average Return = 17.24\n", - "Iteration 3: Average Return = 18.47\n", - "Iteration 4: Average Return = 20.21\n", - "Iteration 5: Average Return = 22.23\n", - "Iteration 6: Average Return = 21.91\n", - "Iteration 7: Average Return = 23.87\n", - "Iteration 8: Average Return = 23.69\n", - "Iteration 9: Average Return = 26.19\n", - "Iteration 10: Average Return = 24.95\n", - "Iteration 11: Average Return = 26.79\n", - "Iteration 12: Average Return = 28.16\n", - "Iteration 13: Average Return = 30.11\n", - "Iteration 14: Average Return = 28.1\n", - "Iteration 15: Average Return = 34.36\n", - "Iteration 16: Average Return = 33.82\n", - "Iteration 17: Average Return = 34.09\n", - "Iteration 18: Average Return = 35.41\n", - "Iteration 19: Average Return = 33.7\n", - "Iteration 20: Average Return = 38.1\n", - "Iteration 21: Average Return = 43.15\n", - "Iteration 22: Average Return = 39.92\n", - "Iteration 23: Average Return = 39.9\n", - "Iteration 24: Average Return = 39.55\n", - "Iteration 25: Average Return = 42.06\n", - "Iteration 26: Average Return = 45.46\n", - "Iteration 27: Average Return = 42.36\n", - "Iteration 28: Average Return = 43.48\n", - "Iteration 29: Average Return = 46.17\n", - "Iteration 30: Average Return = 46.95\n", - "Iteration 31: Average Return = 45.55\n", - "Iteration 32: Average Return = 44.52\n", - "Iteration 33: Average Return = 48.75\n", - "Iteration 34: Average Return = 48.17\n", - "Iteration 35: Average Return = 47.81\n", - "Iteration 36: Average Return = 44.81\n", - "Iteration 37: Average Return = 43.88\n", - "Iteration 38: Average Return = 47.81\n", - "Iteration 39: Average Return = 49.59\n", - "Iteration 40: Average Return = 49.15\n", - "Iteration 41: Average Return = 50.89\n", - "Iteration 42: Average Return = 48.96\n", - "Iteration 43: Average Return = 50.64\n", - "Iteration 44: Average Return = 53.57\n", - "Iteration 45: Average Return = 47.83\n", - "Iteration 46: Average Return = 48.44\n", - "Iteration 47: Average Return = 52.62\n", - "Iteration 48: Average Return = 51.34\n", - "Iteration 49: Average Return = 52.72\n", - "Iteration 50: Average Return = 52.32\n", - "Iteration 51: Average Return = 50.88\n", - "Iteration 52: Average Return = 50.89\n", - "Iteration 53: Average Return = 57.9\n", - "Iteration 54: Average Return = 55.69\n", - "Iteration 55: Average Return = 53.77\n", - "Iteration 56: Average Return = 52.56\n", - "Iteration 57: Average Return = 58.32\n", - "Iteration 58: Average Return = 55.95\n", - "Iteration 59: Average Return = 59.54\n", - "Iteration 60: Average Return = 58.22\n", - "Iteration 61: Average Return = 58.21\n", - "Iteration 62: Average Return = 59.16\n", - "Iteration 63: Average Return = 59.78\n", - "Iteration 64: Average Return = 62.16\n", - "Iteration 65: Average Return = 60.29\n", - "Iteration 66: Average Return = 59.72\n", - "Iteration 67: Average Return = 58.1\n", - "Iteration 68: Average Return = 60.61\n", - "Iteration 69: Average Return = 64.86\n", - "Iteration 70: Average Return = 62.56\n", - "Iteration 71: Average Return = 63.08\n", - "Iteration 72: Average Return = 64.2\n", - "Iteration 73: Average Return = 61.77\n", - "Iteration 74: Average Return = 63.26\n", - "Iteration 75: Average Return = 67.74\n", - "Iteration 76: Average Return = 67.69\n", - "Iteration 77: Average Return = 65.79\n", - "Iteration 78: Average Return = 72.49\n", - "Iteration 79: Average Return = 68.62\n", - "Iteration 80: Average Return = 68.21\n", - "Iteration 81: Average Return = 74.62\n", - "Iteration 82: Average Return = 77.57\n", - "Iteration 83: Average Return = 77.46\n", - "Iteration 84: Average Return = 78.14\n", - "Iteration 85: Average Return = 79.17\n", - "Iteration 86: Average Return = 83.59\n", - "Iteration 87: Average Return = 76.72\n", - "Iteration 88: Average Return = 85.69\n", - "Iteration 89: Average Return = 94.28\n", - "Iteration 90: Average Return = 100.24\n", - "Iteration 91: Average Return = 101.14\n", - "Iteration 92: Average Return = 102.56\n", - "Iteration 93: Average Return = 106.51\n", - "Iteration 94: Average Return = 110.64\n", - "Iteration 95: Average Return = 111.64\n", - "Iteration 96: Average Return = 115.42\n", - "Iteration 97: Average Return = 115.08\n", - "Iteration 98: Average Return = 113.79\n", - "Iteration 99: Average Return = 112.82\n", - "Iteration 100: Average Return = 113.51\n", - "Iteration 101: Average Return = 116.17\n", - "Iteration 102: Average Return = 110.84\n", - "Iteration 103: Average Return = 111.84\n", - "Iteration 104: Average Return = 111.83\n", - "Iteration 105: Average Return = 111.47\n", - "Iteration 106: Average Return = 119.33\n", - "Iteration 107: Average Return = 116.92\n", - "Iteration 108: Average Return = 123.41\n", - "Iteration 109: Average Return = 119.64\n", - "Iteration 110: Average Return = 119.95\n", - "Iteration 111: Average Return = 117.22\n", - "Iteration 112: Average Return = 123.12\n", - "Iteration 113: Average Return = 123.63\n", - "Iteration 114: Average Return = 129.47\n", - "Iteration 115: Average Return = 129.03\n", - "Iteration 116: Average Return = 129.16\n", - "Iteration 117: Average Return = 130.86\n", - "Iteration 118: Average Return = 127.29\n", - "Iteration 119: Average Return = 132.11\n", - "Iteration 120: Average Return = 133.41\n", - "Iteration 121: Average Return = 135.12\n", - "Iteration 122: Average Return = 142.29\n", - "Iteration 123: Average Return = 140.19\n", - "Iteration 124: Average Return = 137.79\n", - "Iteration 125: Average Return = 140.94\n", - "Iteration 126: Average Return = 141.23\n", - "Iteration 127: Average Return = 137.84\n", - "Iteration 128: Average Return = 144.83\n", - "Iteration 129: Average Return = 144.11\n", - "Iteration 130: Average Return = 150.13\n", - "Iteration 131: Average Return = 156.81\n", - "Iteration 132: Average Return = 153.88\n", - "Iteration 133: Average Return = 162.66\n", - "Iteration 134: Average Return = 158.63\n", - "Iteration 135: Average Return = 163.06\n", - "Iteration 136: Average Return = 171.04\n", - "Iteration 137: Average Return = 182.55\n", - "Iteration 138: Average Return = 175.0\n", - "Iteration 139: Average Return = 175.44\n", - "Iteration 140: Average Return = 179.91\n", - "Iteration 141: Average Return = 185.5\n", - "Iteration 142: Average Return = 184.66\n", - "Iteration 143: Average Return = 188.3\n", - "Iteration 144: Average Return = 191.33\n", - "Iteration 145: Average Return = 190.91\n", - "Iteration 146: Average Return = 195.94\n", - "Solve at 146 iterations, which equals 14600 episodes.\n" + "Iteration 1: Average Return = 16.59\n", + "Iteration 2: Average Return = 17.78\n", + "Iteration 3: Average Return = 18.54\n", + "Iteration 4: Average Return = 21.0\n", + "Iteration 5: Average Return = 23.4\n", + "Iteration 6: Average Return = 23.3\n", + "Iteration 7: Average Return = 25.58\n", + "Iteration 8: Average Return = 28.07\n", + "Iteration 9: Average Return = 31.3\n", + "Iteration 10: Average Return = 36.9\n", + "Iteration 11: Average Return = 45.22\n", + "Iteration 12: Average Return = 52.95\n", + "Iteration 13: Average Return = 55.14\n", + "Iteration 14: Average Return = 61.25\n", + "Iteration 15: Average Return = 71.94\n", + "Iteration 16: Average Return = 81.39\n", + "Iteration 17: Average Return = 88.89\n", + "Iteration 18: Average Return = 83.89\n", + "Iteration 19: Average Return = 70.98\n", + "Iteration 20: Average Return = 79.75\n", + "Iteration 21: Average Return = 85.25\n", + "Iteration 22: Average Return = 80.94\n", + "Iteration 23: Average Return = 91.65\n", + "Iteration 24: Average Return = 103.62\n", + "Iteration 25: Average Return = 92.59\n", + "Iteration 26: Average Return = 106.96\n", + "Iteration 27: Average Return = 121.39\n", + "Iteration 28: Average Return = 125.58\n", + "Iteration 29: Average Return = 134.03\n", + "Iteration 30: Average Return = 142.72\n", + "Iteration 31: Average Return = 148.92\n", + "Iteration 32: Average Return = 147.45\n", + "Iteration 33: Average Return = 154.6\n", + "Iteration 34: Average Return = 144.77\n", + "Iteration 35: Average Return = 146.08\n", + "Iteration 36: Average Return = 162.57\n", + "Iteration 37: Average Return = 156.5\n", + "Iteration 38: Average Return = 160.93\n", + "Iteration 39: Average Return = 174.5\n", + "Iteration 40: Average Return = 161.14\n", + "Iteration 41: Average Return = 172.84\n", + "Iteration 42: Average Return = 172.15\n", + "Iteration 43: Average Return = 165.96\n", + "Iteration 44: Average Return = 174.6\n", + "Iteration 45: Average Return = 180.55\n", + "Iteration 46: Average Return = 180.49\n", + "Iteration 47: Average Return = 178.33\n", + "Iteration 48: Average Return = 179.02\n", + "Iteration 49: Average Return = 181.44\n", + "Iteration 50: Average Return = 181.76\n", + "Iteration 51: Average Return = 186.25\n", + "Iteration 52: Average Return = 181.67\n", + "Iteration 53: Average Return = 182.92\n", + "Iteration 54: Average Return = 186.09\n", + "Iteration 55: Average Return = 186.28\n", + "Iteration 56: Average Return = 185.09\n", + "Iteration 57: Average Return = 189.19\n", + "Iteration 58: Average Return = 187.61\n", + "Iteration 59: Average Return = 183.4\n", + "Iteration 60: Average Return = 188.0\n", + "Iteration 61: Average Return = 186.27\n", + "Iteration 62: Average Return = 186.72\n", + "Iteration 63: Average Return = 188.12\n", + "Iteration 64: Average Return = 191.78\n", + "Iteration 65: Average Return = 191.93\n", + "Iteration 66: Average Return = 190.8\n", + "Iteration 67: Average Return = 193.67\n", + "Iteration 68: Average Return = 188.19\n", + "Iteration 69: Average Return = 192.28\n", + "Iteration 70: Average Return = 192.37\n", + "Iteration 71: Average Return = 191.72\n", + "Iteration 72: Average Return = 189.93\n", + "Iteration 73: Average Return = 193.37\n", + "Iteration 74: Average Return = 191.95\n", + "Iteration 75: Average Return = 191.58\n", + "Iteration 76: Average Return = 189.95\n", + "Iteration 77: Average Return = 195.31\n", + "Solve at 77 iterations, which equals 7700 episodes.\n" ] }, { "data": { - "image/png": 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OnP/Gdlm3FnERhAuKlAiLAdTU1FBTUxN17pZbbgm9VkpFCUokf/M3fzOttk0U17vWYh06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\nLXv27IlZ09DQQFlZGaWlpbjdblavXh1al5MT7t4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J0+0kf37cryVKZyLgbXQZztLBX6szxNpxN5kBabMGLA+VfwWcO2UsqEg5fQL9+u+xntsZ\nfp0/XTr2SgeAvLnoUO61E4dBW6hrVg+8luG1dCSZQBDCos98YNpWTY1f0bgPUToTAH935TAxHX+a\nsbexpx9v5lqAuyt/AVy+NPI4hMEyeEcV8F4t+uSx4dc1RdFdehSovLlw/lxQcoA+fhhSUmB+4cCL\nXqWjxb0mCOE58wEqAfEcEKUzMag/A9nOkJlrfvKvAHsq+khgDY4eVKPjQ+UvMD9Ek8HWWA/KBtMz\nsH75kzDrGkzDTmfoWpsxkzsXLnUFucz0iSOQfwVqStrAixlmjpLEdCYG1o//FX1g73iLMenQly6a\nh1NROoIPXX8awrnWAJWWBlcVod97K/CNpvOQOsVfBwOYcykVXVynsQFcs1B/+jnY/xb6g2Gsnabz\n4JiJsqdGfu4oUHneZqeD4jq6vx9OHkMtvDJwrT0V0qeL0pkA6HYP+tVfo9/ePd6iTD683wNq3sKE\nXE6UTpIzkLk2ct84dc1qaKwPyO7SjefBlWumcPrWTZ0GM/OiUjr6Qj3Mmo1ae5uxdl4Mbe3oUaRL\nR0WuUToBGWznTg3fsy0je0IqHd3eSv+Wr6L3vTHeoiQG7wgKnaBx6sIA2lebI5aOAAzKXBt5FIEq\nMo1O9f5B1k7TMEogfwGcPRWRCFpraKxHzZqDmpqOuqXca+0cDV7c2ICKp9JxzgR7akCtjj5xBCDI\n0gEgIwvdPrGy17TVj/XUE2bE+Bs14y1OQtCnvMMFPaJ0Es6ZD8z04awoOoiMAVE6yU64nmtDUK5c\nmDPPr3S01qZGJ0TNjJq7wFhF3cO3lPHT0WYSD7x1Murjt8KMDKwXfxqwTHd1wMVOGEWNTqQoW4q3\n8ecgS+fEYZM04K1FCiAza8JZOvrFn5pCV4cLffTApOiooH0Tbd3Nk+J+kwl95gMouCLygYtjRJRO\nkuPPXIvAvQZea+foARMc9NXMhFI6+QtMSxnf+cPRaDLXfBaMsXY+b6ydwQ04G88HrIsbuXMDYzrH\nj0DhVSH/06gZE0vp6AN70b96FvWRtajP3GVk9/Xd+zBzqs7UiXRfMokiQkLQfX1w7hSqIDHxHBCl\nk1SEfMKrPw3ZDlT6jIjOoa4phv5+OLgvdLq0D2/QUH9wZGS5LgR3jVZrboVsJ9aP/gXd12vWhciU\niwcqby40n0f39aI7243rL5RrDYyl09mRkBHdY0W7m7Aqn4A581B3PYC6ssi8fiS+XcHHG93qhlY3\n+H6HEtdJHBfOmZEpCYrngCidpEG3tmB99S/Rh98LfL3hTETxHD+FSyF9Onp/bUB36aEoVy64ctGH\n3gt6L4jGem8a9KyB/VOnYfvzB0yR6W9f8K7zKh1X/NxrgLF0LMvMCfLHc0IkEYBxu2kLujrjK9MY\n0f39WP+2Dfr6sD3wDyYb0ZULDhcc2T/e4o2IbvNgPbNjxNETIfEmEahrbzDHEtdJGNrX/kYsnUnI\n+XPQ0YZV9Yzf4tGWBQ1nIorn+FApKahlq0zqdGO9cVk4Q8Q6ALV0BRzZb1KOw3Gh3mTADemLplb+\nCar4o+j/+1PTHqexwdQTpaUNc6LY4E+bvnDOJBHYbLBgUejFvlY4Sd6VQNf8Bo4fRt29wX9/SinU\nkqKkj+vo92qx/ulBdM1L6Dd+H/3+U3WgbKgV15tjsXQSx5kPTGKO7/9UAkiaIW779u1j586dWJbF\n2rVrKS8vD3hfa83OnTt55513SEtLY8OGDSxcuDDs3s7OTioqKmhqamLmzJk89NBDzJgRmZsq4XR1\nmH+PH4bD78HSFd7Mte7oLB2Aa4qh9g/ot3abcQipw9TMLF0Bf3jZ+NOHc08BurF+2IFs6q770Af3\nYf3w+8aimBVnKwf8/0G0T+nkL0ClTQ0tX2YWGrzFpFF+jglCt3vQVT+Gq1eirr858M0rl8Mbvzdx\nnWj/DuKM7u1B//zf0a+8aLIhU6eMSmHoU3XmdzprtnmAEKWTMPSZEzB3PiolijEkYyQpLB3Lsqis\nrGTTpk1UVFSwe/duzp4NbCz5zjvvcP78ebZv387999/PU089NeLeqqoqioqK2L59O0VFRVRVVSX8\n3iJFd3qVzrR0rP/rzQrzZa5FmETgQy27znQPOH82bCaZuuoac+1D7w4vl3d+jcodRulk5qDuvBfq\nDpon9TjHcwAT38rIMp/PiaPDu9ZgQrTC0c/9H+jpxnbXl4OSIZI1rqN7e7C+9Q/oV15Erf0Mtk3f\nhrnzR6cwTtWhFiwymYnZDvCEHtEhxBatdULb3/hICqVTV1dHXl4eubm52O12SkpKqK2tDVjz1ltv\ncfPNN6OUYsmSJXR1deHxeMLura2tpbS0FIDS0tKgcyYVne0AqE9/AY6+b1wqDb7MteiecFVGJixc\nYn4Ok0mmMrKg4IqwSoc2z4hdo1XJx43VBHFNlw4gby76vVqT7RTGSktk08+oGqj69hx9H/3671Gf\n+PyA23AwSRrX0S/+FE7VYfvy32Nbdx8qdQrK4Yo6HqNbW8zf2PzF5oUcV9zca9a/bcN6PXr334eW\nVrf53kmw0kkK95rb7cbpdPqPnU4nx44dC1rjcrkC1rjd7rB729rayMkx7V+ys7Npawv9xVNdXU11\ndTUAW7duDbhOtNjt9lHt7+jv5dLUacz8wl/R/MqL2F9+AVuOix6Hi5nzFkR9vq4bP0bn8cNMn7+Q\n6YPkGSpfx6obufirn+HMmBHSRdXTeBYPkLX4KtLC3Fffg9+g9X9+lczrP8qUBHx+7fMLuXTsIACO\n4huxD7NH5+TQaLOR3t/LjDHINZJ81sUumv/6C8xY/9+ZVvqJiM6l+/pw/+f/hpm5uP7iAdMpIgRt\n1xTTvfcNnE7niLUUo/37i4beD47i/u0LTP34rWR9csAN3lWwgM6al3DOmD7svQyV7/KJQ7QB2Suu\nY4rLRevsufQdPxLze9B9fTS+9UdSuzpwfOYLw65LxOc3FmIpX/fJI7QC2cuvHdP/2WhJCqWTCJRS\nw/6HLSsro6yszH/c3Dz6Jy2XyzWq/VZzIzp9Bi0dneiyz9Hz3E6YngHzFo7qfHpJEdhsXHTmcWnQ\n/qHy6QVLoK+X5jf+gFp2bbBcRw8B0J42HRVODnsa/OP3aQdIwOdnZXv/k8zIxGNPCy/b9AwuXmjg\n8hjkGkk+ve8NdHsrHW/U0LXsuojOZVX/An36BLYNm2jp7ILO0PUp1vzF6Fdfonn/O+HHW4SRL1bo\n/n6s726GGRn0fPbPA65lpRlF03zsCGp2fkTyWfv3grLRluFANTdjpWeimxtpamqKabGidjeB1vQe\nPUBT/bnAxrBh5Es2Yimf9b7xcLRl5IT//xMhc+aEdsEPJSncaw6Hg5aWFv9xS0sLDocjaM3gD9u3\nJtzerKwsPB4PAB6Ph8zMzHjexpjQnR0wIwMAVfpJ83NXx4hfMsOhZhdg+/a/QwhFEsDiq8FuRx/a\nF/r9xnozMmBQunQy4HdFLbxy5C+nzGx0nAe56ffN56dPhGgNFGr95YvoX/wHLL8OVv5J2LXJFNfR\n1b+A08exffHLqOmBSTkqx9tZPIqYjD51HOYUDGQ8OlzQ2+N3N8cMj/c7oq/PJOsIJnNtZh5qWvxn\n6AwmKZROYWEhDQ0NNDY20tfXx549eyguLg5YU1xczGuvvYbWmqNHj5Kenk5OTk7YvcXFxdTUmN5V\nNTU1rF69OujaSUNnu+l/hKmBUWWfM69HmUQwGJWRNeIXskqbCoVLh43r6AsN4MpLaHZLROSZJ+lh\ni0IHkxH/rgT6oFdpN9abdkAjrX+3Fi5fwvbpL4ysNF254JiJPhJBTVUc0RfqjaJceQP4RoIPxmGs\nz0hjMlpr0x18/kC6u8rxWrCxjuu0DjyY6iSLj40H+twp9MF9AZ99okgK91pKSgrr169ny5YtWJbF\nmjVrKCgo4OWXXwbglltu4dprr2Xv3r08+OCDTJkyhQ0bNoTdC1BeXk5FRQW7du3yp0wnLV0dpmDT\ni1r7GehsR628Ie6XVktXoKueQXe0mySEwTTWx7dr9ChRuXNQ9/ydv7Yj7NqMLPNEHSd08wXzOa24\nHt59E07WjWhh6to/mNHboTpjD0EphbpyOXr/22itR+120pcuQlqayRIbBdaPfwD2VGx/HpxlB5j7\ngcgVhqfFPAwMrrHyKi48TYED+caI9lk6M/OSwmIcT/SFeqzvPGr+Fj7/Fwm/flIoHYBVq1axatWq\ngNduueUW/89KKb70pS9FvBcgIyODxx57LLaCxotB7jXwWjt/Fvp+Y41f6Rx+D7X6o/7X/enS3tTq\nZMNW8vHIFsbZ0vFZObZP3YH1Xi36gyMh42P+9Rc74f29qI/dGjByIixXFsHrvzdp4nOjd7nq3l6s\nTfdBthPb3RtQESi7wP09cOhd1K13orKdIdeo1FTIzI48g83bWTrgaXuQtRTT9pOtLWBPRa0qQVf/\nEt3dHfci5mREtzRifecboDW2r2yOf5/EECSFe22yo/v7TZPD6eMUc5q/CKZNh6FxnVa3KU4dpjB0\nwpCRBZe6/D3iRoPWGv3um1ih2ukc3Gee8hdeCXn5ww+4851r35vQ14cq/kjE11dLlpu9R0f5lF5/\nyjzYXKjH+tbXsX60Ax1NayDfeIiRYnuOmZG7107WmXihb5ItwIwssNtj717zuE0Pw6uKoL8Pjh+K\n7fkBq7IC64+/i/l5Y4VubcF64hvGrft3/xRVp5NYIkonGbjYaTo+zxgfpaNSUuDK5cFxHW8vNZWb\nfO61qMj01up0jD44rf/wW6zv/086nv5u4OtWP/rQu6hlK40bbMFi+OBo2LY1+q0/mi/vSOJRPly5\nMC3dFPyORn6ve9G2cRuq7LPoP76M9ehfh56JFIo2k5CjBk+gDYXDBe7IEgn06TqYPS8gk0zZbJAT\nfb3PiNdqbTEPBouuBpstZFxHH9xH/4X60Z2/ow39xu/R/y955x9ZT/4vaG/D9t//MWFTQkMhSicZ\n8HUjGOReSzRq6QpovjDQJBRv+xuIe9foeKN8/ddGmcGmz55E//QpmJLG5Vd/G/AZceqEeWhYutIc\nL1xiXHktjaHP1dUBB99BFX8kqtiMUso0aB187Wg4fdxYs/kLsN15L7ZHvgPaQlf/MrL9PksnMzu8\nnF6FMVKvOJNEUIcKFbcZpkBUa41urMf6w8tYlRX0P/rXWL/8SWR96TzNqBynqR9asDhI6eiGM1jf\n/Uc8jz9oYl/R4rOcTh9Pyj55uqMd6g6hPnl7ZMk3cUSUTjLQ5e1GMH0clc7yVaAU1rNPDzQAvVBv\nXB3OmeMmV0wYQ1cC3X3ZPCGmT8f29W9BSgr6N88NvH/wHQDU1UbpqCtMJ4jhXGz6nTegvx9V/NGQ\n74dlZh40X4h+H6BPn4B5C/2KTs1biFpejD64L6JOCrrdWDojKR0cLtPB4uIIM3HOnTQZm0uWBb0V\nqrOB1hrriW9gPfIA+offR7+/F6ZNR7/4E9MdIZzsWhtXcY6JRakri8xU1suX/Gus538EU6bQ33QB\n/cwPolYc+phX6VzsGvXvKK543bLJEJ8VpZMMJIOlM2sO6s/ug31voH/8A/9TJa68UWc7JQ2Zo++/\npv/jSbhwDtu9X0HNW8i0P/0ses8raK8low/uM1/mPsU2d77p2nsytNtK1/7RKI9RpKoql1E60bbb\n0X19psfWUKti2Urzxe9tbx8Wn6WTMYLSibBWx5d8oXwWYsA5XNDaEjgDqf40HNmPWnMrtv+xA9sT\nP8T2D/8L9ZG16Bd/guUbrxGKi52m9id7kNLp7/fX6+jjh2HfG6hP3s70devRb9ago2yXo+sOmmJu\nMFblKNDtrWEn+epLF01Cx2jOfWQ/TEkbvht7AhGlkwRoXyHcOMV0fNjW3ob69J3oP7yMrnrGxHSG\nafQ5oZjhi+lE516z9uxC73nFZGx5e8tN//zdoBT6N8+ZJ+Xjh1FXD2SqKXsqzC8MWSSqO9rg8Luo\n4o+OLu15Zq758vRZHZFy/qwZ1DUvUOn4rDP9/t6Rz9HeCukzhu9Y7junI7I6G/3+PphdgMoJkQnn\ncBml0N46aL2RUX3yv6Bm55v4mc2G+sv/hlp9E/q5nVi//3Xoi/nSpX1Zd4uWGov1yHvGgnr+3yEz\nG1X2Oab/HsmRAAAgAElEQVTf/pewZDn6P55ERxjf0d3dpmnpjR835x1ler619e+xdlaEvkZvL9Y/\nPUjHzv9vVOfWRw/AoqXm73OcEaWTDHSNv6XjQ5X/OeqmW9C//hnUnx6XlMqYMy3duAmjSCTQly4a\nK2fJMtRt6/yvp7hmoT5Shv5jNfqNV6G/z//l7UMtWAyn64LmFOm9r4Nljc61htfSATO8Lgq098lb\nDVU6mTmm4evBYbpRDD5HWyuMlEQA4DCWTrgMNt3bA8feD/rc/HL5rKVB59Dvv2OUlCOwR5iypaDW\nPwQrrkf/x78a9+VQvErHp+BU2lRvXOcAHHgbjr6Puu3PTJlCSgq2e78CdjvW//52ZBmPJ48al+nS\na2DOPP/nHQ261W0m/e59HT1oFLv//TdroKWR3lEUtuqONjOS2tvZYrwRpZMMdHZAih3SQjdJTCRK\nKdSf/zVce4PJqJvo6dJ4g/AZ2dFZOnWHoPsSttvWBXVjUJ8yDSP1s5UwZYp5ch7MFUugpwfOnQp4\nWb/1RzP1dLRdfb0dvKNOJjh9AtKmQogsRHX1tVB3KCC+EZL21pHjOQBZ2SYNOlwGW90h6O0ZVukM\nFIgapaO7u41iWBZciweg7HZsX/57yHaaz3gI2teNYJBVpa68Bk4ew/rZTtMK5qZBNYEOF7a/+ls4\nVYf+3S/C3Kz3/N7GsxQuNYr9VOhkAn30wPC/O591pHXQNbVl+afz9p3+YOShi0PxxXNE6Qh+ukxh\naCwbHI4FlZKC7b6HUV+4J6pakqQmIws9KJFA9/ZgPf1dE2APgT52wHx5hpjVo5wzzTiH3h5YvAyV\nOiXwfV8ywaC4jj6yH44cQK2+afS/Z+dMMwm2OTqlo08dN4PuQsTm1LJrTd3KSFX67a2oCJSOsqVA\nliNsyrM+uM88ZHlrj4IY2k7n2AHo60UtG0ZJgfkdFFyB9s6gCsDnXhtkqakrl5uR5w1nUJ/78yC3\nk7r2Bpi3EH14ZMtC1x00g9CmzzCxus724ESI7m6s7/0T1nM7Q5/j1DFQCnV9qYkZDnItcuBtM8Rv\n2bXGTXoh2BIKK9+R/eahYxxa3oRClE4SoDvaxz2eMxSVOgXbLZ9HJZlcoyYzsCuBfu1l9Ou70DUv\nhVyuj74PCxYPW7WuPnUHpE5BrQjRrHNmnnGVejPYdEsj1r9+C3LnoG4pD14fIcqeaoLsUbjXtGXB\nmRNBrjU/i66GKVP8WXjD0u6JzNIBcISfiaMP7oPCK4cdf0D6DBP09p5Dv7/PJGcsHkZJeVGzC+D8\n2cAEBDDdCDKzAxVL4VLjcp23ELX6ptDny7/CZNmFQVsmIUEtvtrs8dW/DHWxHdpnCq2PHQxtBZ06\nDnn5qM/8GfT3oX//K/971m+fB4cLm7dljT4bXqagcx/xxXOSowGNKJ1koKt9IPNFiAtqUCsc3dON\n/s3PzM/v7w36EtDd3aaGZHFwOq//fDPzsH3radMRfOh7SsGCJegPjqJ7urF2fBP6+7D9zaaxd/Sd\nmYeOxtJprDcpzMP0MVOpqbCkKKzS0d3dcPlSxEpHhSnutNo8cPp4QPJF0H6ljOLyZsDp9/ea2NpI\nbWvmFBhLYEjKsva0DCQR+K6RlobtgX/Adt/Xhm9FlL8A2jyBVsdQzp40n82iq717rgBlQ58KtKD1\nvv9nfuho8xddB3CqDjV/ESov38Snfv9rdHe3Kd49+r5pADx3vrG+o1A6ur3VxGaTxLUGonSSgyF9\n14Q4kJHlj+nompegzYO6vtQUcQ4N3H5wxCQIhKghGYzKyBz2C0tdsRjqT2M9XQFnTmD70lfNF8oY\nUa7c6CydU6GTCALOuWwlnD/nTwMPItIaHR/eOptQqd09771lrjlcPMdHjgvczWYOTsOZsL3sfPjb\nugx1sbW2BMRz/OtXXB96WqvvfV97niGxucH46nOUV+motDSYnY/29pUDb9eK92r9sTxdF9iCxz89\n1ZvObLvl89DVgd5TbaycadNRN/0pyp6KPX9BdJaOL54znCtzHBClkwx0dYxrYeikICMLenpMLcRv\nnoOlK1CfvxsA/f7bAUv10fdN7KRwaagzRYS64kqTiPH2HtRnv4i6JkZjNWbmQZs7bD1HAKdPGDdS\nmD5bPqtDvz+MteN90h+xBY4Px0wzt6YzuC6q+91a4z4boYO0r0DUJ9NwSQQBeO9RNwQrHZXtCLFh\nBLxKJ+yXfN1BcLhQgwqo1bxC87n7OHEEOtpQn7jd3HvdwcBz+B4MfJ/JoqWw8Er0r34Ge19HfexT\nqKnGQrbPLxzR5TcYfeSASVBKkngOiNIZd7TW3kSCD0nsJFnxFjXqF38CHW3YPnuXsRry8tEHAutU\n9LH3oeAKVPr00V/visUmWH7tDahPDz8eOWp84y9aIrN29OnjMHdBeH/+7ALTemY4F5sv6y9S99ow\ntTpaa3rerYWrrhm54DhnpnFt7X8Lsh0QwTBDNS3dWEiDLB3d22M8CTmuMDuHOV9GlrnnYZSO1hpd\ndxC1aIhFPL/QPBi0us26ff8PUuyoomJYtNQkHgw+z8k6UDYoMPEgpRS2T3we2tyQkoL6+G3+tfYF\ni4wFGGGzVn1kPyxOnngOiNIZfy5dNIVw4l6LK8rXlaDmJVh27YA7ZPl1cOSA33LQfb1w4nDYeE5E\n15uRie3x72G7P0zMYDTnnRl5rY7W2sRPRrIqlDLurkPvBgfh8dboAGRGaOn462yGpE2fP4fVfGFk\n1xoYF53W8N5bqKuvjTzjb3ZBoKUztDA0WsK5s5ovmPY6iwMtYr8r84yxdvS+N+HK5aj06ebv7vy5\ngO4Y+lQdzM439UM+Vv4J5F+BKv1UgJVm91ksEVg7ut1jXJNLkieeA0kwT6ezs5OKigqampr8g9Zm\nzJgRtG7fvn3s3LkTy7JYu3Yt5eXlYfc3Njby0EMP+ed2L168mPvvvz+h9xYRvsLQ8RprMFnwtanR\nGttnv+h/WS1fZUYwHz0ARdeZAWw9PTHxgceldbyvVqf5/MjzZpovmF5gYeI5fpZdC7urTcbd0Fk7\n/hY4WZHJOMxMHH/rmwiUjspxocGkc0cQz/Hvm1OAfu23aMsyyr7VVxg6CvcaJq6jd/0K3d8fVK/l\nq8/xPcD4meeN3ZyqA1ceXDiHWmusFbV4qbmv44dg5Q3mweBUXZD7UNlSSHn8e0Hy2BeY36U+e3LE\nv1F95H1zrquSS+mMu6VTVVVFUVER27dvp6ioiKqqqqA1lmVRWVnJpk2bqKioYPfu3Zw9e3bE/Xl5\neWzbto1t27Ylp8IBf981JZZOfPH1DCsqDuyyu2SZSRn2tlnRx8x/VBYP+SJJFmZkGh99JAWi3rhC\nuCQCH2rpCtPe53CIkdjtHpieEbmLZkYmpE4JrlU5tI+UvLkD1lo4fC46nxUWKbMLTGqy18rSY7V0\n5i4wGXGNIVri1B00nbuHuP7U1HTInYs+dcKfteafcDt/MdhTBxqEtrqNUo8w5mLLcRmvSCTJBEf3\nm7+VSB46Esi4K53a2lpKS0sBKC0tpba2NmhNXV0deXl55ObmYrfbKSkp8a+LZH9SkyR91z70OFyo\nz6zDtu6+gJdV6hSTMuyN6+ij75t2K5E+1ScYpRTMjGzEgT59HGw2yJ8/8nlnZJoEgBDFlTrSbgSD\nZcxxBrax6eqAw+8xJYLx4sCA0pm/KKpaMTXHa136XGzeuMpoYjowkMEWysWmjx009S8h3KdqfqEZ\nc7DvDXMP3vZAKjUVFiwaiOucMrVcKsJGnEopmBs+g0339mJV/9LM9lmyLMhCG2/GXem0tbWRk2N8\nxdnZ2bS1BWe8uN1unM6BJxWn04nb7R5xf2NjI1/72td4/PHHOXQo9pMCY4H2jjWQOp34opTC9tkv\nhuwlp5avggvnTIPHuoMjpkqPO668iCwdffo4zJkX1DFhWHLnokNVu0epdACTmDDI0tG/eQ56ukn/\n1O0RbVdT082X9Q1roruuL4PNpzw9zZA2bfT1UbMLjOIe8iWvz5+D82eHt8LmFRpr68QR1MpARasW\nXW1a5XR3m5R2ZTP1PRGi8hfAuVNBKena6sfa/QrWNx5A/+dTsGAxtgSNvI+GhMR0Nm/eTGtrcIHV\nunXrAo6VUmNqBTN4f05ODjt27CAjI4MTJ06wbds2nnjiCdLTg//4qqurqa6uBmDr1q24XKN7KgKw\n2+1R7b+oLToA57wF2DLj/3QdrXyJZjzk6/voWlp++r9Je/VXXLp8iYzrbmDaMDIkw+fXMW8BFw/u\nxel0Bv1/8clndbTTfPoEacUlZEUob/uCQi7v+nXQeZs727EvWkp2FPfdNqeAnvfewuVy0d/SRPPv\nf8XU0k8ytfBKXH19kZ3kuz+M+Hp+XC6ash1McTeS5XLRerGTPtfMiH9noX6/zXPnk9JYT86g17tq\nfk0n4Cy7lZQQ5+4puhaPt+VNzsc+SeqgNd2rbqD1pZ+T5Wmkq/401rwrcM4dvl5oqHwZVxXR/sqL\n5PT3YJ9lar+01rQ+/iA9+9/GvugqZjz4DdJWxChNP8YkROk8+uijw76XlZWFx+MhJycHj8dDZmaw\nKe1wOGhpafEft7S04HA4wu5PTU0l1duGfeHCheTm5tLQ0EBhYbB/s6ysjLKyMv9xc/PoR+W6XK6o\n9luN50HZaLl8GdUTQUfbMRKtfIlmPOTTU6aBK5dLu0zrkc7ceXQNI0MyfH7WDFNz1Hz8WFD9icvl\noqn6V1jP7ICuDnqWr45YXivLgb58kebjR1GDYiD9HjfW1PTo/q7TZ6DdzTRduID+j3+FfoueT9xO\nX19f3D8/K3culz84Rm9zM/2NDZCRHfE1Q/1+rdkF9NcdCni9/7XfwcIr8ZACIc6ts7xKxjmL1ulZ\nqEFrtLeJbuvbr6OPHUQVFUclX6f3d+PZvxeVajLe9LtvYu1/G/Vf/grrE7fToRQdCf479SVtjcS4\nu9eKi4upqTFzxWtqali9Olg7FxYW0tDQQGNjI319fezZs4fi4uKw+9vb27G85ueFCxdoaGggNzc3\nEbcUHZ3tMH36xB+UNoFRSpnUacsyHYcdyWsJwqARB0Pa4eiuDtq++09Y/7IFMrKxbXoCVXRd5Of1\nVecP6tCguy9Dd+QtcPw4XKAtOLIf/cffmQJHV2L+/6k5BdBwxmSGeVpCz+yJhvwF4G5CXzS1Mbrp\nvElFv65keBmmzzAFyDd/IsgaVdMzzAiE2j+YtjjRDlabPc+02vG6/LTWWC/+FFy5qLLPJU3j4OEY\n95Tp8vJyKioq2LVrlz/lGUwc58knn2Tjxo2kpKSwfv16tmzZgmVZrFmzhoKCgrD7Dx48yLPPPktK\nSgo2m4377rsvZCr2uNPZIenSSYBavgr96q/HXJ+TEGaaL2/ddMGfrqsvX8La/BCXW1tQn1mH+vQX\noh/Ylet11Zw/NzDWuD3KGh0vKmcmGrB+/APTGDWWBbIjMXue6YfmbjYFlqPNXPOi8heYNOezp2DJ\nMvTePeb168J3YE/5yubhz7loKfq135qfo8wuU2lpkDt7IJlg/1sm7fov/1tSFYEOx7hLmJGRwWOP\nPRb0usPhYOPGjf7jVatWsWpVcCuM4fbfcMMN3HDDDbEVNg7oLum7lhRcdY0pxls9ugFrCcU5y7Tp\nGZRMoGt+Ay2NZP/TdjrmLBjdeXOcZj7Q4GQCXwuc0Vg6AI0NZkBatPvHgJpTgMZbjd/fH7LvWlTM\nXQCAPncStWQZ+u09pgO5c9boz7noanjttyZJYRTzldTcBegzJwasHOcsM7l0AjDu7rVJT2fyjTWY\njKi0qaQ8/j3jZktyVOoU8/Tuda/p7m4z5OvqlaRdUzz689psMGtu4OTKtiibffrwpSjPyED96ejH\nOYwKX1HuIW8x6hgtHXKcpmfa2ZOmKeoHR8O61iJB+Qb/zZmHmjJC9+xQ5C/wThrdAyePeS3bcbch\nIkKUTpzQly/S/4Ot/v5Lw9IpzT6FUTAzF+1thaP/8JLpJ3fbuhE2jYzKmxtg6fjb+mdFp3RU+nRY\nfh3qC+vH1sNuNGRkwYwM9KF3zfEYLR2llL8djt77unlt1diUDq5cmDVn1J0vVP4C0Brrx/8KDu9Q\nwQmCKJ14cfaUeQo5PkJ9UFe7uNeEqFGuPGg+j+7tQb/0AlxZ5B8kNiby5kJzI7rXm0npUzozok/n\nT/nvj2MrWTt2maJEKWWsHZ+VNlZLh0G1MW/90Qx+C1HvFa2MtkeeQN1xz+hO4Bu70NGGunUU8btx\nJGKlc+DAARobzbwNj8fD97//fXbs2BGy/kbAtOIA9MWuYZfonm7o6ZHCUCF6ZuZCq9tMmGxzY7v1\nzticN3euyTpr8g4aa/fAjMwJ47rxoWZ7W9PYbGZq7FjJX2CG4Z04MnYrx4tKn246FIwG5yyYOs1r\n5SResY+FiJVOZWUlNm+7hx/+8If09/ejlOLJJ5+Mm3ATmt4e8+/lS8Ov8fZdk5iOEDXetGn9y5+Y\n+Su+bLMxMjRtOtoWOEnDbO/AvCxHTMoR/APdGDlrLREopVB3fRnb+ocmlJUDUWSvud1uU13c38+7\n777Ljh07sNvtfPnLX46nfBMW7bV0uDS8pePruybNPoVoUTPzTBpv92Vst/5Z7Gozco3S0RfOmQ7R\nE1Tp+DLYxpy55mPOPJMxOGde2GmjicQ2geI4g4lY6UybNo3W1lbOnDlDfn4+U6dOpa+vj75IW1pM\nNnq8ls6lMJaOjDUQRou3VocrlkTV+n8k1LR0yHIMFIi2t5opqBMNn3stBvEcMNmN6qZPJG/38QlE\nxErnk5/8JBs3bqSvr4//+l//KwCHDx9mboQ9gyYdPkvn8sVhl2i/e00sHSFKMrJRn/g8qvijsa9A\nzxvU+HOCWjpkOyDHNdB1OgbY/mJDzM41mYlY6ZSXl3P99ddjs9nIyzP+ZIfDwQMPPBA34SY0vd5E\ngnDutS4ZayCMDqXU6DOfRjp37lz027vRly+Z4PkEVDpKKWyPfdfMkxGSiqhSUgY3dDtw4AA2m42r\nrxZzMyT+mE64RALfWIMkbM8jTF7y5hrXb/1pcxxljU6yEM0cHiFxRJy99vjjj3P48GHATOv83ve+\nx/e+9z2ef/75uAk3ofHHdMIlEnTA1GkTLvtE+HDjC5T7pqgmsoWN8OEnYqVz5swZlixZAsArr7zC\n448/zpYtW/jd734XN+EmNP6YzgiJBFKjIyQbvgy2o97R3VE2+xSEcETsXtNaA3D+vOn3lJ9v8uC7\nusI8yU9meke2dHRnh8RzhOTDNQvsdjjmHakslo4QQyJWOldeeSVPP/00Ho/HP7Pm/PnzZGTIk3pI\nIo3pSOaakGQoWwrMnA0NZ0xtSkb8J9oKk4eI3Wt/8zd/Q3p6OvPnz+fOO03Ljfr6ej796U/HTbiJ\njPbFdLovoa3+0Iu6OlBSoyMkI74CyBmZqBQZMCjEjogtnYyMDL74xS8GvBZqvo3gxWfpgInrpIfI\nUOuUWTpCcqLy5pqKfnGtCTEmYqXT19fH888/z2uvvYbH4yEnJ4ebb76Z22+/HfsYmgF2dnZSUVFB\nU1OTf/JnqAmf+/btY+fOnViWxdq1aykvNzM6Xn/9dX72s59x7tw5/vmf/5nCwoEpfC+88AK7du3C\nZrNxzz33sHLlylHLGTW+mA7ApYtBSkf39Zl4j8R0hGTEO0VUlI4QayJ2rz3zzDPs37+f++67j23b\ntnHfffdx4MABnnnmmTEJUFVVRVFREdu3b6eoqIiqqqqgNZZlUVlZyaZNm6ioqGD37t2cPXsWgIKC\nAh5++GGWLl0asOfs2bPs2bOH73znOzzyyCNUVlZiWdaYZI2KwZbOpRBdCS5KNwIheVG5piZP0qWF\nWBOx0nnjjTf4+7//e1asWMGcOXNYsWIFDz/8MK+//vqYBKitraW0tBSA0tJSamtrg9bU1dWRl5dH\nbm4udrudkpIS/7r8/PyAotXB5y0pKSE1NZVZs2aRl5dHXV3dmGSNip5u8E0EDNUKx9cCR1KmhWTE\nF9MRpSPEmKhTpmNNW1sbOTmmDiA7O5u2tragNW63G6dzoHGf0+nk2LFjYc/rdrtZvHix/9jhcOB2\nh57iWV1dTXV1NQBbt27F5XJFfR8+7HY7LpeL5v4+yHHSf6GezFQ7aUPO2dN4Fg+QNSc/6L144pMv\nWRH5xkbM5HO56LzrPtKKS0iN4f1Oms8vTiS7fJEQsdK58cYb+da3vsUdd9xhvlSbm/n5z3/ODTfc\nMOLezZs3hxz2tm5d4HhdpVTsmxdGQFlZGWVlZf7j5ubmUZ/L99n0X75kBi1dqKftwnlsQ86pzxn3\nYHu/Ro3heqOVL1kR+cZGTOX7+Ge4DBDD+51Un18cSGb5QnmcQhGx0rn77rv5+c9/TmVlJR6PB4fD\nQUlJCXfccceIex999NFh38vKyvInJng8HjIzgwPrDoeDlpYW/3FLSwsOhyPsNYfucbvdI+6JKT3d\nkOWt5A4R09G+vmsS0xEEYRIRVukcOHAg4HjZsmUsW7YMrbXfIjl8+DDLly8ftQDFxcXU1NRQXl5O\nTU2Nv/B0MIWFhTQ0NNDY2IjD4WDPnj08+OCDI553+/bt3HbbbXg8HhoaGli0aNGo5Yyanh5UZo5J\nOw0V0+mSmI4gCJOPsErnBz/4QcjXfQrHp3y+//3vj1qA8vJyKioq2LVrlz9lGoxl8uSTT7Jx40ZS\nUlJYv349W7ZswbIs1qxZQ0GBmZPx5ptv8vTTT9Pe3s7WrVtZsGABjzzyCAUFBdx444185StfwWaz\nce+99/rHbccbbfVDX69Jh1Y2uDhMIoHdDmlTEyKTIAhCMhBW6fzLv/xL3AXIyMjgscceC3rd4XCw\nceNG//GqVatCFqNef/31XH/99SHPffvtt3P77bfHTthI6e01/6alwbRpw1s60zPHJYYlCIIwXiTm\n0X+y4avRSU2Dqekhm35q6UYgCMIkRJROPPD1XZsyBaalo0M1/exql3iOIAiTDlE68cBn6UxJg2np\nwxeHiqUjCMIkQ5ROPOg1SkdNSYNp00O3wenqQImlIwjCJEOUTjzwx3SmoKZOC1I6WmuTSCCWjiAI\nkwxROvHAH9PxWTpDEgkuX4L+fpBZOoIgTDJE6cQDf0xnijdlekgigXQjEARhkiJKJw7o3kGWztR0\n6O1B9/UOLPB2I5CYjiAIkw1ROvHA515LnWLcawCD06Y7ZZaOIAiTE1E68cDnXvN1JICAuI72912T\nmI4gCJMLUTrxoHegI4HyWTqDa3XE0hEEYZIiSiceDEqZZqrP0hnkXuvyJhKkz0isXIIgCOOMKJ14\n0NMDKSkoux3SfTGdQWnTnR2QPh2VkjI+8gmCIIwTonTiQU+3yVwDk70G6MHuta4O6bsmCMKkRJRO\nPOjtMa41ML3XIKArgekwLUkEgiBMPiIeVx0vOjs7qaiooKmpyT/EbcaM4FjHvn372LlzJ5ZlsXbt\nWsrLywF4/fXX+dnPfsa5c+f453/+ZwoLCwFobGzkoYce8s/tXrx4Mffff39ibmqwpRNC6dDVARlZ\niZFFEAQhiRh3pVNVVUVRURHl5eVUVVVRVVXF3XffHbDGsiwqKyv5xje+gdPpZOPGjRQXF5Ofn09B\nQQEPP/ww//Zv/xZ07ry8PLZt25aoW/GjewZZOvZUSLEHKp3OdtTs/ITLJQiCMN6Mu3uttraW0tJS\nAEpLS6mtrQ1aU1dXR15eHrm5udjtdkpKSvzr8vPz/dZM0jDI0lFKBY83kJiOIAiTlHG3dNra2sjJ\nyQEgOzubtra2oDVutxun0+k/djqdHDt2bMRzNzY28rWvfY309HTWrVvH0qVLYyd4OHp7TN81H9PS\n4aJROrqv1/RikxodQRAmIQlROps3b6a1tTXo9XXr1gUcK6WMZRADcnJy2LFjBxkZGZw4cYJt27bx\nxBNPkJ6eHrS2urqa6upqALZu3YrL5Rr1de12O3arH9uMDHK852nJyMRm9ZHjctHvaaEZmJE7m/Qx\nXGcs8o3l/uKNyDc2RL6xIfLFn4QonUcffXTY97KysvB4POTk5ODxeMjMDM7qcjgctLS0+I9bWlpw\nOBxhr5mamkpqaioACxcuJDc3l4aGBn+iwWDKysooKyvzHzc3N494T8Phcrnou9gF0zP95+m3T4E2\nD83NzehzpwHoxMbFMVxnLPKN5f7ijcg3NkS+sSHyjZ5IwxzjHtMpLi6mpqYGgJqaGlavXh20prCw\nkIaGBhobG+nr62PPnj0UFxeHPW97ezuWZQFw4cIFGhoayM3Njf0NhKKn20wN9TEtfaAjgbcbgXSY\nFgRhMjLuMZ3y8nIqKirYtWuXP2UaTBznySefZOPGjaSkpLB+/Xq2bNmCZVmsWbOGgoICAN58802e\nfvpp2tvb2bp1KwsWLOCRRx7h4MGDPPvss6SkpGCz2bjvvvtCpmLHhSExHTUtfaA4VPquCYIwiRl3\npZORkcFjjz0W9LrD4WDjxo3+41WrVrFq1aqgdddffz3XX3990Os33HADN9xwQ2yFjZTBdTrgtXS8\niQTSYVoQhEnMuLvXPpQMrtMB0wrn8kW01mLpCIIwqRGlE2N0fz/09QZbOv39Rhl1dZiC0cHvC4Ig\nTBJE6cQa/6jqIXU6YApEuzpgRkbMUsMFQRAmEqJ0Yozuvmx+GGzJTPX1X+syzT4lc00QhEmKKJ0Y\nowcPcPPinx566ZJJmZYO04IgTFJE6cSYkJbONN/00C6TSCCWjiAIkxRROjHGZ+kEFod6LR1vTEdJ\n5pogCJMUUToxRnd73WsBMR1j6ehLF6XDtCAIkxpROjEmVEyHdK+l42kxqdNi6QiCMEkRpRNj/DGd\ntEGWTpo3ptN83vwr3QgEQZikiNKJNaGy1+x2mDIF3dxojsXSEQRhkiJKJ8aEzF4Dk0zQ5LN0ROkI\ngjA5EaUTY/wxncEdCcAUiHq8M4HE0hEEYZIiSifGhMxeA9MKR5v5PhLTEQRhsiJKJ8YMZK+FUDoA\nSlwbS/QAABQfSURBVMH06YkVShAEIUkQpRNjdPdlSLGjUlIC3/ApnWnTUbaU4I2CIAiTgHEf4tbZ\n2UlFRQVNTU3+yaGhJnzu27ePnTt3YlkWa9eupby8HIAf/ehHvP3229jtdnJzc9mwYQPTvZbECy+8\nwK5du7DZbNxzzz2sXLky/jfU0x0czwHU1HQ0SDxHEIRJzbhbOlVVVRQVFbF9+3aKioqoqqoKWmNZ\nFpWVlWzatImKigp2797N2bNnAbjmmmt44okn+Pa3v83s2bN54YUXADh79ix79uzhO9/5Do888giV\nlZVYlhX3+9Hdl0PPyvFZOpK5JgjCJGbclU5tbS2lpaUAlJaWUltbG7Smrq6OvLw8cnNzsdvtlJSU\n+NetWLGCFK8ra8mSJbjdbv95S0pKSE1NZdasWeTl5VFXVxf3+9E93YHdCHz4lI50mBYEYRIz7u61\ntrY2cnJyAMjOzqatrS1ojdvtxul0+o+dTifHjh0LWrdr1y5KSkr8exYvXux/z+Fw+BXSUKqrq6mu\nrgZg69atuFyu0d9PTw8p09KDztHlnEknMNXpImsM5x8rdrt9TPcXb0S+sSHyjQ2RL/4kROls3ryZ\n1tbWoNfXrVsXcKyUGvVEzeeff56UlBRuuummqPeWlZVRVlbmP25ubh6VDAAp3Zfpt6UEncOyNADd\n9iljOv9Ycblc43r9kRD5xobINzZEvtEzZ86ciNYlROk8+uijw76XlZWFx+MhJycHj8dDZmaw+8nh\ncNDS0uI/bmlpweFw+I9fffVV3n77bR577DG/0hq6x+12B+yJF7r7cmDfNR8S0xEEQRj/mE5xcTE1\nNTUA1NTUsHr16qA1hYWFNDQ00NjYSF9fH3v27KG4uBgwWW2/+MUv+PrXv07aoC/74uJi9uzZQ29v\nL42NjTQ0NLBo0aK4389wMR3lj+mI0hEEYfIy7jGd8vJyKioq2LVrlz9lGoxl8uSTT7Jx40ZSUlJY\nv349W7ZswbIs1qxZQ0FBAQCVlZX09fWxefNmABYvXsz9999PQUEBN954I1/5ylew2Wzce++92GwJ\n0LHdl0MnC0z1WTqSSCAIwuRFaa31eAuRbNTX149+86N/jTVvEbb7vhrwsu7tQT/3f1CfvQs1ji62\nZPYJg8g3VkS+sSHyjZ6kiulMJnR3d8iYjkqdgrrr/nGQSBAEIXkY95jOh41h63QEQRAEUTqxZtiO\nBIIgCIIonViirX7o6xVLRxAEYRhE6cSSnh7zb6g6HUEQBEGUTkzp9SodsXQEQRBCIkonlvQMMzVU\nEARBAETpxJYesXQEQRDCIUonlngtHSUxHUEQhJCI0oklvV73WqooHUEQhFCI0oklEtMRBEEIiyid\nWOKL6UyRmI4gCEIoROnEEC2WjiAIQlhE6cQSqdMRBEEIiyidWCKWjiAIQljGfbRBZ2cnFRUVNDU1\n+Ye4zZgxI2jdvn372LlzJ5ZlsXbtWsrLywH40Y9+xNtvv43dbic3N5cNGzYwffp0Ghsbeeihh/wz\nHnzD3eKKxHQEQRDCMu5Kp6qqiqKiIsrLy6mqqqKqqoq77747YI1lWVRWVvKNb3wDp9PJxo0bKS4u\nJj8/n2uuuYYvfvGLpKSk8Mwzz/DCCy/49+fl5bFt27bE3UyPpEwLgiCEY9zda7W1tZSWlgJQWlpK\nbW1t0Jq6ujry8vLIzc3FbrdTUlLiX7dixQpSUlIAWLJkCW63O3HCD6W3G+x2lFceQRAEIZBxVzpt\nbW3k5OQAkJ2dTVtbW9Aat9uN0+n0HzudzpDKZdeuXaxcudJ/3NjYyNe+9jUef/xxDh06FAfph9DT\ng5J4jiAIwrAkxL22efNmWltbg15ft25dwLFSCqXUqK7x/PPPk5KSwk033QRATk4OO3bsICMjgxMn\nTrBt2zaeeOIJ0tPTg/ZWV1dTXV0NwNatW3G5XKOSod2m6E6bOur9icBut4t8Y0DkGxsi39hIdvki\nISFK59FHHx32vaysLDweDzk5OXg8HjIzM4PWOBwOWlpa/MctLS04HA7/8auvvsrbb7/NY4895lda\nqamppKamArBw4UJyc3NpaGigsLAw6PxlZWWUlZX5j5ubm6O/ScBqb8M2JW3U+xOBy+US+caAyDc2\nRL6xkczy+ZK2RmLc3WvFxcXU1NQAUFNTw+rVq4PWFBYW0tDQQGNjI319fezZs4fi4mLAZLX94he/\n4Otf/zppgxpttre3Y1kWABcuXKChoYHc3Ny43ovu7ZF0aUEQhDCMe/ZaeXk5FRUV7Nq1y58yDSaO\n8+STT7Jx40ZSUlJYv349W7ZswbIs1qxZQ0FBAQCVlZX09fWxefNmYCA1+uDBgzz77LOkpKRgs9m4\n7777QqZix5SeblRaGjq+VxEEQZiwKK21fEcOob6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WmVs/R7/7Cu7tOmJ2btid1dXhULEwTfOy+2RGWCGEK9NFheh/vo3e/Cl06YX/nxeSeTHf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\nxRdffAHAhx9+WG6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f2267jYULF9q3rVq1ig4dOjBy5EhWrVrFqlWrGDduHCdPnmTLli3MnTuX7Oxs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\nBw5QWFiIaZoOTxKXnZ1NQEAAdevWLXX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -733,6 +548,14 @@ "np.random.seed(seed)\n", "tf.set_random_seed(seed)\n", "prng.seed(seed)\n", + "env.seed(seed)\n", + "\n", + "tf.reset_default_graph()\n", + "sess = tf.Session()\n", + "with tf.variable_scope(\"policy\"):\n", + " opt_p = tf.train.AdamOptimizer(learning_rate=0.01)\n", + " policy = CategoricalPolicy(in_dim, out_dim, hidden_dim, opt_p, sess)\n", + "\n", "sess.run(tf.global_variables_initializer())\n", "\n", "n_iter = 200\n", @@ -784,7 +607,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": { "collapsed": true }, @@ -835,220 +658,255 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brian/anaconda3/lib/python3.6/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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 13.04\n", - "Iteration 2: Average Return = 13.33\n", - "Iteration 3: Average Return = 13.5\n", - "Iteration 4: Average Return = 13.25\n", - "Iteration 5: Average Return = 14.7\n", - "Iteration 6: Average Return = 14.82\n", - "Iteration 7: Average Return = 15.63\n", - "Iteration 8: Average Return = 15.34\n", - "Iteration 9: Average Return = 15.65\n", - "Iteration 10: Average Return = 15.46\n", - "Iteration 11: Average Return = 16.44\n", - "Iteration 12: Average Return = 15.96\n", - "Iteration 13: Average Return = 18.14\n", - "Iteration 14: Average Return = 16.89\n", - "Iteration 15: Average Return = 16.83\n", - "Iteration 16: Average Return = 16.29\n", - "Iteration 17: Average Return = 16.78\n", - "Iteration 18: Average Return = 15.17\n", - "Iteration 19: Average Return = 15.17\n", - "Iteration 20: Average Return = 15.14\n", - "Iteration 21: Average Return = 16.42\n", - "Iteration 22: Average Return = 16.45\n", - "Iteration 23: Average Return = 15.65\n", - "Iteration 24: Average Return = 17.17\n", - "Iteration 25: Average Return = 15.01\n", - "Iteration 26: Average Return = 16.13\n", - "Iteration 27: Average Return = 15.69\n", - "Iteration 28: Average Return = 15.35\n", - "Iteration 29: Average Return = 15.6\n", - "Iteration 30: Average Return = 15.56\n", - "Iteration 31: Average Return = 14.3\n", - "Iteration 32: Average Return = 14.78\n", - "Iteration 33: Average Return = 15.74\n", - "Iteration 34: Average Return = 16.54\n", - "Iteration 35: Average Return = 16.12\n", - "Iteration 36: Average Return = 15.87\n", - "Iteration 37: Average Return = 16.6\n", - "Iteration 38: Average Return = 16.09\n", - "Iteration 39: Average Return = 15.54\n", - "Iteration 40: Average Return = 15.47\n", - "Iteration 41: Average Return = 14.62\n", - "Iteration 42: Average Return = 14.51\n", - "Iteration 43: Average Return = 15.94\n", - "Iteration 44: Average Return = 16.92\n", - "Iteration 45: Average Return = 14.93\n", - "Iteration 46: Average Return = 14.81\n", - "Iteration 47: Average Return = 15.34\n", - "Iteration 48: Average Return = 16.16\n", - "Iteration 49: Average Return = 16.45\n", - "Iteration 50: Average Return = 16.25\n", - "Iteration 51: Average Return = 14.47\n", - "Iteration 52: Average Return = 14.88\n", - "Iteration 53: Average Return = 14.26\n", - "Iteration 54: Average Return = 14.64\n", - "Iteration 55: Average Return = 15.21\n", - "Iteration 56: Average Return = 15.2\n", - "Iteration 57: Average Return = 14.98\n", - "Iteration 58: Average Return = 14.82\n", - "Iteration 59: Average Return = 14.21\n", - "Iteration 60: Average Return = 14.13\n", - "Iteration 61: Average Return = 14.32\n", - "Iteration 62: Average Return = 13.45\n", - "Iteration 63: Average Return = 13.05\n", - "Iteration 64: Average Return = 13.77\n", - "Iteration 65: Average Return = 13.41\n", - "Iteration 66: Average Return = 14.11\n", - "Iteration 67: Average Return = 13.57\n", - "Iteration 68: Average Return = 13.31\n", - "Iteration 69: Average Return = 12.98\n", - "Iteration 70: Average Return = 13.5\n", - "Iteration 71: Average Return = 14.09\n", - "Iteration 72: Average Return = 14.01\n", - "Iteration 73: Average Return = 13.79\n", - "Iteration 74: Average Return = 13.67\n", - "Iteration 75: Average Return = 13.98\n", - "Iteration 76: Average Return = 15.14\n", - "Iteration 77: Average Return = 14.51\n", - "Iteration 78: Average Return = 15.12\n", - "Iteration 79: Average Return = 13.54\n", - "Iteration 80: Average Return = 14.79\n", - "Iteration 81: Average Return = 14.71\n", - "Iteration 82: Average Return = 15.62\n", - "Iteration 83: Average Return = 14.48\n", - "Iteration 84: Average Return = 14.5\n", - "Iteration 85: Average Return = 14.95\n", - "Iteration 86: Average Return = 14.92\n", - "Iteration 87: Average Return = 15.19\n", - "Iteration 88: Average Return = 14.3\n", - "Iteration 89: Average Return = 14.7\n", - "Iteration 90: Average Return = 14.63\n", - "Iteration 91: Average Return = 14.82\n", - "Iteration 92: Average Return = 14.09\n", - "Iteration 93: Average Return = 13.96\n", - "Iteration 94: Average Return = 14.38\n", - "Iteration 95: Average Return = 14.05\n", - "Iteration 96: Average Return = 13.24\n", - "Iteration 97: Average Return = 14.68\n", - "Iteration 98: Average Return = 13.4\n", - "Iteration 99: Average Return = 14.12\n", - "Iteration 100: Average Return = 13.57\n", - "Iteration 101: Average Return = 13.15\n", - "Iteration 102: Average Return = 13.51\n", - "Iteration 103: Average Return = 13.74\n", - "Iteration 104: Average Return = 13.38\n", - "Iteration 105: Average Return = 14.17\n", - "Iteration 106: Average Return = 14.54\n", - "Iteration 107: Average Return = 14.25\n", - "Iteration 108: Average Return = 14.51\n", - "Iteration 109: Average Return = 14.59\n", - "Iteration 110: Average Return = 14.69\n", - "Iteration 111: Average Return = 14.52\n", - "Iteration 112: Average Return = 14.16\n", - "Iteration 113: Average Return = 14.28\n", - "Iteration 114: Average Return = 13.4\n", - "Iteration 115: Average Return = 13.32\n", - "Iteration 116: Average Return = 13.17\n", - "Iteration 117: Average Return = 14.2\n", - "Iteration 118: Average Return = 13.23\n", - "Iteration 119: Average Return = 13.25\n", - "Iteration 120: Average Return = 13.8\n", - "Iteration 121: Average Return = 13.71\n", - "Iteration 122: Average Return = 14.11\n", - "Iteration 123: Average Return = 14.19\n", - "Iteration 124: Average Return = 14.02\n", - "Iteration 125: Average Return = 13.85\n", - "Iteration 126: Average Return = 14.35\n", - "Iteration 127: Average Return = 14.25\n", - "Iteration 128: Average Return = 13.6\n", - "Iteration 129: Average Return = 14.21\n", - "Iteration 130: Average Return = 13.52\n", - "Iteration 131: Average Return = 14.58\n", - "Iteration 132: Average Return = 13.75\n", - "Iteration 133: Average Return = 14.25\n", - "Iteration 134: Average Return = 14.22\n", - "Iteration 135: Average Return = 15.76\n", - "Iteration 136: Average Return = 15.14\n", - "Iteration 137: Average Return = 14.83\n", - "Iteration 138: Average Return = 13.86\n", - "Iteration 139: Average Return = 14.37\n", - "Iteration 140: Average Return = 14.05\n", - "Iteration 141: Average Return = 14.62\n", - "Iteration 142: Average Return = 13.5\n", - "Iteration 143: Average Return = 14.46\n", - "Iteration 144: Average Return = 14.03\n", - "Iteration 145: Average Return = 12.68\n", - "Iteration 146: Average Return = 13.59\n", - "Iteration 147: Average Return = 13.09\n", - "Iteration 148: Average Return = 13.47\n", - "Iteration 149: Average Return = 13.17\n", - "Iteration 150: Average Return = 13.19\n", - "Iteration 151: Average Return = 12.7\n", - "Iteration 152: Average Return = 13.17\n", - "Iteration 153: Average Return = 13.26\n", - "Iteration 154: Average Return = 12.25\n", - "Iteration 155: Average Return = 13.21\n", - "Iteration 156: Average Return = 12.73\n", - "Iteration 157: Average Return = 13.68\n", - "Iteration 158: Average Return = 13.39\n", - "Iteration 159: Average Return = 13.2\n", - "Iteration 160: Average Return = 13.09\n", - "Iteration 161: Average Return = 13.5\n", - "Iteration 162: Average Return = 13.58\n", - "Iteration 163: Average Return = 13.54\n", - "Iteration 164: Average Return = 13.67\n", - "Iteration 165: Average Return = 13.94\n", - "Iteration 166: Average Return = 14.23\n", - "Iteration 167: Average Return = 13.15\n", - "Iteration 168: Average Return = 13.25\n", - "Iteration 169: Average Return = 13.56\n", - "Iteration 170: Average Return = 13.56\n", - "Iteration 171: Average Return = 13.46\n", - "Iteration 172: Average Return = 14.43\n", - "Iteration 173: Average Return = 13.39\n", - "Iteration 174: Average Return = 14.42\n", - "Iteration 175: Average Return = 15.01\n", - "Iteration 176: Average Return = 14.26\n", - "Iteration 177: Average Return = 14.28\n", - "Iteration 178: Average Return = 14.65\n", - "Iteration 179: Average Return = 13.98\n", - "Iteration 180: Average Return = 13.66\n", - "Iteration 181: Average Return = 13.84\n", - "Iteration 182: Average Return = 13.44\n", - "Iteration 183: Average Return = 14.55\n", - "Iteration 184: Average Return = 14.4\n", - "Iteration 185: Average Return = 13.91\n", - "Iteration 186: Average Return = 14.42\n", - "Iteration 187: Average Return = 14.44\n", - "Iteration 188: Average Return = 14.05\n", - "Iteration 189: Average Return = 15.29\n", - "Iteration 190: Average Return = 15.1\n", - "Iteration 191: Average Return = 14.58\n", - "Iteration 192: Average Return = 15.62\n", - "Iteration 193: Average Return = 15.9\n", - "Iteration 194: Average Return = 16.09\n", - "Iteration 195: Average Return = 16.81\n", - "Iteration 196: Average Return = 16.09\n", - "Iteration 197: Average Return = 16.93\n", - "Iteration 198: Average Return = 15.78\n", - "Iteration 199: Average Return = 16.44\n", - "Iteration 200: Average Return = 16.64\n" + "Iteration 1: Average Return = 10.08\n", + "Iteration 2: Average Return = 10.46\n", + "Iteration 3: Average Return = 10.14\n", + "Iteration 4: Average Return = 9.85\n", + "Iteration 5: Average Return = 10.22\n", + "Iteration 6: Average Return = 10.14\n", + "Iteration 7: Average Return = 9.87\n", + "Iteration 8: Average Return = 10.11\n", + "Iteration 9: Average Return = 10.25\n", + "Iteration 10: Average Return = 10.15\n", + "Iteration 11: Average Return = 10.26\n", + "Iteration 12: Average Return = 10.5\n", + "Iteration 13: Average Return = 10.48\n", + "Iteration 14: Average Return = 10.22\n", + "Iteration 15: Average Return = 10.57\n", + "Iteration 16: Average Return = 10.9\n", + "Iteration 17: Average Return = 10.26\n", + "Iteration 18: Average Return = 10.56\n", + "Iteration 19: Average Return = 10.62\n", + "Iteration 20: Average Return = 10.4\n", + "Iteration 21: Average Return = 10.76\n", + "Iteration 22: Average Return = 10.52\n", + "Iteration 23: Average Return = 10.58\n", + "Iteration 24: Average Return = 10.59\n", + "Iteration 25: Average Return = 10.72\n", + "Iteration 26: Average Return = 10.81\n", + "Iteration 27: Average Return = 11.24\n", + "Iteration 28: Average Return = 11.11\n", + "Iteration 29: Average Return = 10.75\n", + "Iteration 30: Average Return = 10.87\n", + "Iteration 31: Average Return = 11.04\n", + "Iteration 32: Average Return = 10.85\n", + "Iteration 33: Average Return = 11.04\n", + "Iteration 34: Average Return = 11.65\n", + "Iteration 35: Average Return = 11.21\n", + "Iteration 36: Average Return = 11.28\n", + "Iteration 37: Average Return = 11.91\n", + "Iteration 38: Average Return = 11.61\n", + "Iteration 39: Average Return = 11.11\n", + "Iteration 40: Average Return = 11.31\n", + "Iteration 41: Average Return = 12.1\n", + "Iteration 42: Average Return = 11.88\n", + "Iteration 43: Average Return = 12.03\n", + "Iteration 44: Average Return = 11.89\n", + "Iteration 45: Average Return = 12.44\n", + "Iteration 46: Average Return = 11.83\n", + "Iteration 47: Average Return = 12.3\n", + "Iteration 48: Average Return = 12.74\n", + "Iteration 49: Average Return = 12.17\n", + "Iteration 50: Average Return = 13.56\n", + "Iteration 51: Average Return = 13.71\n", + "Iteration 52: Average Return = 14.01\n", + "Iteration 53: Average Return = 14.51\n", + "Iteration 54: Average Return = 14.55\n", + "Iteration 55: Average Return = 14.76\n", + "Iteration 56: Average Return = 15.93\n", + "Iteration 57: Average Return = 15.93\n", + "Iteration 58: Average Return = 16.11\n", + "Iteration 59: Average Return = 17.79\n", + "Iteration 60: Average Return = 16.7\n", + "Iteration 61: Average Return = 19.77\n", + "Iteration 62: Average Return = 18.77\n", + "Iteration 63: Average Return = 19.52\n", + "Iteration 64: Average Return = 18.45\n", + "Iteration 65: Average Return = 18.48\n", + "Iteration 66: Average Return = 22.25\n", + "Iteration 67: Average Return = 20.18\n", + "Iteration 68: Average Return = 21.35\n", + "Iteration 69: Average Return = 20.37\n", + "Iteration 70: Average Return = 21.85\n", + "Iteration 71: Average Return = 21.94\n", + "Iteration 72: Average Return = 20.89\n", + "Iteration 73: Average Return = 24.0\n", + "Iteration 74: Average Return = 25.24\n", + "Iteration 75: Average Return = 23.86\n", + "Iteration 76: Average Return = 24.11\n", + "Iteration 77: Average Return = 23.75\n", + "Iteration 78: Average Return = 26.01\n", + "Iteration 79: Average Return = 25.59\n", + "Iteration 80: Average Return = 23.17\n", + "Iteration 81: Average Return = 26.62\n", + "Iteration 82: Average Return = 27.59\n", + "Iteration 83: Average Return = 27.92\n", + "Iteration 84: Average Return = 27.11\n", + "Iteration 85: Average Return = 26.93\n", + "Iteration 86: Average Return = 29.08\n", + "Iteration 87: Average Return = 26.2\n", + "Iteration 88: Average Return = 29.16\n", + "Iteration 89: Average Return = 27.14\n", + "Iteration 90: Average Return = 30.51\n", + "Iteration 91: Average Return = 28.48\n", + "Iteration 92: Average Return = 30.9\n", + "Iteration 93: Average Return = 31.75\n", + "Iteration 94: Average Return = 29.56\n", + "Iteration 95: Average Return = 31.15\n", + "Iteration 96: Average Return = 28.82\n", + "Iteration 97: Average Return = 33.28\n", + "Iteration 98: Average Return = 33.0\n", + "Iteration 99: Average Return = 34.55\n", + "Iteration 100: Average Return = 33.29\n", + "Iteration 101: Average Return = 33.43\n", + "Iteration 102: Average Return = 32.23\n", + "Iteration 103: Average Return = 33.32\n", + "Iteration 104: Average Return = 33.15\n", + "Iteration 105: Average Return = 32.65\n", + "Iteration 106: Average Return = 35.68\n", + "Iteration 107: Average Return = 37.33\n", + "Iteration 108: Average Return = 37.43\n", + "Iteration 109: Average Return = 35.63\n", + "Iteration 110: Average Return = 34.88\n", + "Iteration 111: Average Return = 37.45\n", + "Iteration 112: Average Return = 40.42\n", + "Iteration 113: Average Return = 34.93\n", + "Iteration 114: Average Return = 35.56\n", + "Iteration 115: Average Return = 37.47\n", + "Iteration 116: Average Return = 35.77\n", + "Iteration 117: Average Return = 36.11\n", + "Iteration 118: Average Return = 37.27\n", + "Iteration 119: Average Return = 41.93\n", + "Iteration 120: Average Return = 39.01\n", + "Iteration 121: Average Return = 41.16\n", + "Iteration 122: Average Return = 39.38\n", + "Iteration 123: Average Return = 42.63\n", + "Iteration 124: Average Return = 35.98\n", + "Iteration 125: Average Return = 46.2\n", + "Iteration 126: Average Return = 39.58\n", + "Iteration 127: Average Return = 45.05\n", + "Iteration 128: Average Return = 41.33\n", + "Iteration 129: Average Return = 38.68\n", + "Iteration 130: Average Return = 44.06\n", + "Iteration 131: Average Return = 39.44\n", + "Iteration 132: Average Return = 42.38\n", + "Iteration 133: Average Return = 40.69\n", + "Iteration 134: Average Return = 39.62\n", + "Iteration 135: Average Return = 43.6\n", + "Iteration 136: Average Return = 46.57\n", + "Iteration 137: Average Return = 49.14\n", + "Iteration 138: Average Return = 46.44\n", + "Iteration 139: Average Return = 46.99\n", + "Iteration 140: Average Return = 45.56\n", + "Iteration 141: Average Return = 44.56\n", + "Iteration 142: Average Return = 46.3\n", + "Iteration 143: Average Return = 44.84\n", + "Iteration 144: Average Return = 46.95\n", + "Iteration 145: Average Return = 46.7\n", + "Iteration 146: Average Return = 45.69\n", + "Iteration 147: Average Return = 48.15\n", + "Iteration 148: Average Return = 46.98\n", + "Iteration 149: Average Return = 44.28\n", + "Iteration 150: Average Return = 50.21\n", + "Iteration 151: Average Return = 49.16\n", + "Iteration 152: Average Return = 46.78\n", + "Iteration 153: Average Return = 55.6\n", + "Iteration 154: Average Return = 47.32\n", + "Iteration 155: Average Return = 47.01\n", + "Iteration 156: Average Return = 44.32\n", + "Iteration 157: Average Return = 52.46\n", + "Iteration 158: Average Return = 49.88\n", + "Iteration 159: Average Return = 48.62\n", + "Iteration 160: Average Return = 49.89\n", + "Iteration 161: Average Return = 51.35\n", + "Iteration 162: Average Return = 51.09\n", + "Iteration 163: Average Return = 49.62\n", + "Iteration 164: Average Return = 48.84\n", + "Iteration 165: Average Return = 48.38\n", + "Iteration 166: Average Return = 50.41\n", + "Iteration 167: Average Return = 50.26\n", + "Iteration 168: Average Return = 51.77\n", + "Iteration 169: Average Return = 50.28\n", + "Iteration 170: Average Return = 51.23\n", + "Iteration 171: Average Return = 52.21\n", + "Iteration 172: Average Return = 49.03\n", + "Iteration 173: Average Return = 52.14\n", + "Iteration 174: Average Return = 55.93\n", + "Iteration 175: Average Return = 54.51\n", + "Iteration 176: Average Return = 55.64\n", + "Iteration 177: Average Return = 49.3\n", + "Iteration 178: Average Return = 50.79\n", + "Iteration 179: Average Return = 52.75\n", + "Iteration 180: Average Return = 56.72\n", + "Iteration 181: Average Return = 52.66\n", + "Iteration 182: Average Return = 51.31\n", + "Iteration 183: Average Return = 51.35\n", + "Iteration 184: Average Return = 55.02\n", + "Iteration 185: Average Return = 55.12\n", + "Iteration 186: Average Return = 51.75\n", + "Iteration 187: Average Return = 55.05\n", + "Iteration 188: Average Return = 52.61\n", + "Iteration 189: Average Return = 57.72\n", + "Iteration 190: Average Return = 54.77\n", + "Iteration 191: Average Return = 62.41\n", + "Iteration 192: Average Return = 55.29\n", + "Iteration 193: Average Return = 56.2\n", + "Iteration 194: Average Return = 55.68\n", + "Iteration 195: Average Return = 56.26\n", + "Iteration 196: Average Return = 55.76\n", + "Iteration 197: Average Return = 49.72\n", + "Iteration 198: Average Return = 51.59\n", + "Iteration 199: Average Return = 52.11\n", + "Iteration 200: Average Return = 54.24\n" ] + }, + { + "data": { + "image/png": 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JU2aipl6AcuWh8ovMfBKbLdDsURCEkY+ISF/4F19q9nkebS3m0es1PalS0yIeGkist7ei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\ncwKeiEoeYE6kudEUAjQ3ouuqGcRsoSE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NN3DDDTcAsHXrVv7xj39w991388Ybb5Cbm8usWbPIzc1lwoQJnXp9IboDXeha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\nxMXFYbfbSUhIwG63Exvb9fP1q6Cmt6s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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ "np.random.seed(seed)\n", "tf.set_random_seed(seed)\n", "prng.seed(seed)\n", + "env.seed(seed)\n", + "\n", + "tf.reset_default_graph()\n", + "sess = tf.Session()\n", + "with tf.variable_scope(\"policy\"):\n", + " opt_p = tf.train.GradientDescentOptimizer(learning_rate=0.01)\n", + " policy = CategoricalPolicy(in_dim, out_dim, hidden_dim, opt_p, sess)\n", "\n", "sess.run(tf.global_variables_initializer())\n", "\n", @@ -1063,36 +921,8 @@ " 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": 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v7YZ13zyraXXxF/QZwIhBXHO+iWdZ+FzEMdwQ80IiCQAgp6xjSoWbxRxKBZCkls6AGE7t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\nMxPlocgJ9n4+C+RzbONahSfDwT4XAAj3cnJpY7Vf0pthY6x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iR1MQQqEs+ILA5wdiUWZOEpQFlbPIrr8LdMdWbWMy2bA+BSEUONzhmr9itopu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\nCBYFIpp84zjR7JnnSnenKMhXn93oGephhJmGHoplmOLXqOR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "loss_list, avg_return_list = po.train()\n", + "\n", "util.plot_curve(loss_list, \"loss\")\n", "util.plot_curve(avg_return_list, \"average return\")" ] @@ -1195,215 +1025,131 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "scrolled": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/brian/anaconda3/lib/python3.6/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" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 25.53\n", - "Iteration 2: Average Return = 31.37\n", - "Iteration 3: Average Return = 34.29\n", - "Iteration 4: Average Return = 34.88\n", - "Iteration 5: Average Return = 41.4\n", - "Iteration 6: Average Return = 40.2\n", - "Iteration 7: Average Return = 46.82\n", - "Iteration 8: Average Return = 46.66\n", - "Iteration 9: Average Return = 48.57\n", - "Iteration 10: Average Return = 55.08\n", - "Iteration 11: Average Return = 52.67\n", - "Iteration 12: Average Return = 59.6\n", - "Iteration 13: Average Return = 51.23\n", - "Iteration 14: Average Return = 56.83\n", - "Iteration 15: Average Return = 57.68\n", - "Iteration 16: Average Return = 59.44\n", - "Iteration 17: Average Return = 61.41\n", - "Iteration 18: Average Return = 67.33\n", - "Iteration 19: Average Return = 66.32\n", - "Iteration 20: Average Return = 69.83\n", - "Iteration 21: Average Return = 68.95\n", - "Iteration 22: Average Return = 70.61\n", - "Iteration 23: Average Return = 78.92\n", - "Iteration 24: Average Return = 79.31\n", - "Iteration 25: Average Return = 76.66\n", - "Iteration 26: Average Return = 76.2\n", - "Iteration 27: Average Return = 86.2\n", - "Iteration 28: Average Return = 89.08\n", - "Iteration 29: Average Return = 95.4\n", - "Iteration 30: Average Return = 91.74\n", - "Iteration 31: Average Return = 103.5\n", - "Iteration 32: Average Return = 104.6\n", - "Iteration 33: Average Return = 118.86\n", - "Iteration 34: Average Return = 123.13\n", - "Iteration 35: Average Return = 130.14\n", - "Iteration 36: Average Return = 146.35\n", - "Iteration 37: Average Return = 153.55\n", - "Iteration 38: Average Return = 152.74\n", - "Iteration 39: Average Return = 161.34\n", - "Iteration 40: Average Return = 160.96\n", - "Iteration 41: Average Return = 145.61\n", - "Iteration 42: Average Return = 152.06\n", - "Iteration 43: Average Return = 159.83\n", - "Iteration 44: Average Return = 157.32\n", - "Iteration 45: Average Return = 162.76\n", - "Iteration 46: Average Return = 155.33\n", - "Iteration 47: Average Return = 167.84\n", - "Iteration 48: Average Return = 176.73\n", - "Iteration 49: Average Return = 174.29\n", - "Iteration 50: Average Return = 179.89\n", - "Iteration 51: Average Return = 171.8\n", - "Iteration 52: Average Return = 176.05\n", - "Iteration 53: Average Return = 173.84\n", - "Iteration 54: Average Return = 169.44\n", - "Iteration 55: Average Return = 170.96\n", - "Iteration 56: Average Return = 164.92\n", - "Iteration 57: Average Return = 158.49\n", - "Iteration 58: Average Return = 163.73\n", - "Iteration 59: Average Return = 158.61\n", - "Iteration 60: Average Return = 161.05\n", - "Iteration 61: Average Return = 165.82\n", - "Iteration 62: Average Return = 160.82\n", - "Iteration 63: Average Return = 169.3\n", - "Iteration 64: Average Return = 164.33\n", - "Iteration 65: Average Return = 171.14\n", - "Iteration 66: Average Return = 166.77\n", - "Iteration 67: Average Return = 160.26\n", - "Iteration 68: Average Return = 157.82\n", - "Iteration 69: Average Return = 148.6\n", - "Iteration 70: Average Return = 146.72\n", - "Iteration 71: Average Return = 144.75\n", - "Iteration 72: Average Return = 124.52\n", - "Iteration 73: Average Return = 119.5\n", - "Iteration 74: Average Return = 126.89\n", - "Iteration 75: Average Return = 120.23\n", - "Iteration 76: Average Return = 122.81\n", - "Iteration 77: Average Return = 122.22\n", - "Iteration 78: Average Return = 130.45\n", - "Iteration 79: Average Return = 128.67\n", - "Iteration 80: Average Return = 128.77\n", - "Iteration 81: Average Return = 120.16\n", - "Iteration 82: Average Return = 121.99\n", - "Iteration 83: Average Return = 111.23\n", - "Iteration 84: Average Return = 116.44\n", - "Iteration 85: Average Return = 105.62\n", - "Iteration 86: Average Return = 118.94\n", - "Iteration 87: Average Return = 126.52\n", - "Iteration 88: Average Return = 114.6\n", - "Iteration 89: Average Return = 119.72\n", - "Iteration 90: Average Return = 117.02\n", - "Iteration 91: Average Return = 119.07\n", - "Iteration 92: Average Return = 125.01\n", - "Iteration 93: Average Return = 113.49\n", - "Iteration 94: Average Return = 117.56\n", - "Iteration 95: Average Return = 117.51\n", - "Iteration 96: Average Return = 120.86\n", - "Iteration 97: Average Return = 107.71\n", - "Iteration 98: Average Return = 106.71\n", - "Iteration 99: Average Return = 101.24\n", - "Iteration 100: Average Return = 108.92\n", - "Iteration 101: Average Return = 107.69\n", - "Iteration 102: Average Return = 99.36\n", - "Iteration 103: Average Return = 107.93\n", - "Iteration 104: Average Return = 109.04\n", - "Iteration 105: Average Return = 106.95\n", - "Iteration 106: Average Return = 110.89\n", - "Iteration 107: Average Return = 114.76\n", - "Iteration 108: Average Return = 121.82\n", - "Iteration 109: Average Return = 124.3\n", - "Iteration 110: Average Return = 128.27\n", - "Iteration 111: Average Return = 138.37\n", - "Iteration 112: Average Return = 142.31\n", - "Iteration 113: Average Return = 148.54\n", - "Iteration 114: Average Return = 151.29\n", - "Iteration 115: Average Return = 145.98\n", - "Iteration 116: Average Return = 148.93\n", - "Iteration 117: Average Return = 151.53\n", - "Iteration 118: Average Return = 143.81\n", - "Iteration 119: Average Return = 148.03\n", - "Iteration 120: Average Return = 147.62\n", - "Iteration 121: Average Return = 141.63\n", - "Iteration 122: Average Return = 139.6\n", - "Iteration 123: Average Return = 127.71\n", - "Iteration 124: Average Return = 130.2\n", - "Iteration 125: Average Return = 121.71\n", - "Iteration 126: Average Return = 114.16\n", - "Iteration 127: Average Return = 112.36\n", - "Iteration 128: Average Return = 116.12\n", - "Iteration 129: Average Return = 114.3\n", - "Iteration 130: Average Return = 117.15\n", - "Iteration 131: Average Return = 128.88\n", - "Iteration 132: Average Return = 123.62\n", - "Iteration 133: Average Return = 129.99\n", - "Iteration 134: Average Return = 125.04\n", - "Iteration 135: Average Return = 131.42\n", - "Iteration 136: Average Return = 127.78\n", - "Iteration 137: Average Return = 128.03\n", - "Iteration 138: Average Return = 123.06\n", - "Iteration 139: Average Return = 118.73\n", - "Iteration 140: Average Return = 121.81\n", - "Iteration 141: Average Return = 131.12\n", - "Iteration 142: Average Return = 136.83\n", - "Iteration 143: Average Return = 136.78\n", - "Iteration 144: Average Return = 134.48\n", - "Iteration 145: Average Return = 126.52\n", - "Iteration 146: Average Return = 136.42\n", - "Iteration 147: Average Return = 135.87\n", - "Iteration 148: Average Return = 135.61\n", - "Iteration 149: Average Return = 141.36\n", - "Iteration 150: Average Return = 150.1\n", - "Iteration 151: Average Return = 146.61\n", - "Iteration 152: Average Return = 147.94\n", - "Iteration 153: Average Return = 150.84\n", - "Iteration 154: Average Return = 146.08\n", - "Iteration 155: Average Return = 143.8\n", - "Iteration 156: Average Return = 150.74\n", - "Iteration 157: Average Return = 144.83\n", - "Iteration 158: Average Return = 148.24\n", - "Iteration 159: Average Return = 146.53\n", - "Iteration 160: Average Return = 141.88\n", - "Iteration 161: Average Return = 144.06\n", - "Iteration 162: Average Return = 140.69\n", - "Iteration 163: Average Return = 147.89\n", - "Iteration 164: Average Return = 146.76\n", - "Iteration 165: Average Return = 144.28\n", - "Iteration 166: Average Return = 140.49\n", - "Iteration 167: Average Return = 138.74\n", - "Iteration 168: Average Return = 134.06\n", - "Iteration 169: Average Return = 129.78\n", - "Iteration 170: Average Return = 130.54\n", - "Iteration 171: Average Return = 122.87\n", - "Iteration 172: Average Return = 114.02\n", - "Iteration 173: Average Return = 114.84\n", - "Iteration 174: Average Return = 104.79\n", - "Iteration 175: Average Return = 111.36\n", - "Iteration 176: Average Return = 103.88\n", - "Iteration 177: Average Return = 107.02\n", - "Iteration 178: Average Return = 105.65\n", - "Iteration 179: Average Return = 103.24\n", - "Iteration 180: Average Return = 99.1\n", - "Iteration 181: Average Return = 114.61\n", - "Iteration 182: Average Return = 116.86\n", - "Iteration 183: Average Return = 110.43\n", - "Iteration 184: Average Return = 115.65\n", - "Iteration 185: Average Return = 111.54\n", - "Iteration 186: Average Return = 121.67\n", - "Iteration 187: Average Return = 119.26\n", - "Iteration 188: Average Return = 122.05\n", - "Iteration 189: Average Return = 120.66\n", - "Iteration 190: Average Return = 125.58\n", - "Iteration 191: Average Return = 126.43\n", - "Iteration 192: Average Return = 130.43\n", - "Iteration 193: Average Return = 126.92\n", - "Iteration 194: Average Return = 132.38\n", - "Iteration 195: Average Return = 135.63\n", - "Iteration 196: Average Return = 132.69\n", - "Iteration 197: Average Return = 140.79\n", - "Iteration 198: Average Return = 137.78\n", - "Iteration 199: Average Return = 136.23\n", - "Iteration 200: Average Return = 139.68\n" + "Iteration 1: Average Return = 10.08\n", + "Iteration 2: Average Return = 10.65\n", + "Iteration 3: Average Return = 10.43\n", + "Iteration 4: Average Return = 10.11\n", + "Iteration 5: Average Return = 10.5\n", + "Iteration 6: Average Return = 10.89\n", + "Iteration 7: Average Return = 10.72\n", + "Iteration 8: Average Return = 11.55\n", + "Iteration 9: Average Return = 11.82\n", + "Iteration 10: Average Return = 12.94\n", + "Iteration 11: Average Return = 13.39\n", + "Iteration 12: Average Return = 14.73\n", + "Iteration 13: Average Return = 15.36\n", + "Iteration 14: Average Return = 16.54\n", + "Iteration 15: Average Return = 20.07\n", + "Iteration 16: Average Return = 21.16\n", + "Iteration 17: Average Return = 27.55\n", + "Iteration 18: Average Return = 29.23\n", + "Iteration 19: Average Return = 34.55\n", + "Iteration 20: Average Return = 44.27\n", + "Iteration 21: Average Return = 48.02\n", + "Iteration 22: Average Return = 52.26\n", + "Iteration 23: Average Return = 51.66\n", + "Iteration 24: Average Return = 51.36\n", + "Iteration 25: Average Return = 47.61\n", + "Iteration 26: Average Return = 44.38\n", + "Iteration 27: Average Return = 43.62\n", + "Iteration 28: Average Return = 42.3\n", + "Iteration 29: Average Return = 43.74\n", + "Iteration 30: Average Return = 41.57\n", + "Iteration 31: Average Return = 42.85\n", + "Iteration 32: Average Return = 47.78\n", + "Iteration 33: Average Return = 44.22\n", + "Iteration 34: Average Return = 46.47\n", + "Iteration 35: Average Return = 49.31\n", + "Iteration 36: Average Return = 50.2\n", + "Iteration 37: Average Return = 52.12\n", + "Iteration 38: Average Return = 54.35\n", + "Iteration 39: Average Return = 58.58\n", + "Iteration 40: Average Return = 61.6\n", + "Iteration 41: Average Return = 65.18\n", + "Iteration 42: Average Return = 62.52\n", + "Iteration 43: Average Return = 68.12\n", + "Iteration 44: Average Return = 64.27\n", + "Iteration 45: Average Return = 74.31\n", + "Iteration 46: Average Return = 71.63\n", + "Iteration 47: Average Return = 73.75\n", + "Iteration 48: Average Return = 73.88\n", + "Iteration 49: Average Return = 71.13\n", + "Iteration 50: Average Return = 74.95\n", + "Iteration 51: Average Return = 82.13\n", + "Iteration 52: Average Return = 80.74\n", + "Iteration 53: Average Return = 77.35\n", + "Iteration 54: Average Return = 85.08\n", + "Iteration 55: Average Return = 86.27\n", + "Iteration 56: Average Return = 86.01\n", + "Iteration 57: Average Return = 86.46\n", + "Iteration 58: Average Return = 90.21\n", + "Iteration 59: Average Return = 87.98\n", + "Iteration 60: Average Return = 97.56\n", + "Iteration 61: Average Return = 88.72\n", + "Iteration 62: Average Return = 92.56\n", + "Iteration 63: Average Return = 99.54\n", + "Iteration 64: Average Return = 106.79\n", + "Iteration 65: Average Return = 107.64\n", + "Iteration 66: Average Return = 107.36\n", + "Iteration 67: Average Return = 99.81\n", + "Iteration 68: Average Return = 106.64\n", + "Iteration 69: Average Return = 105.75\n", + "Iteration 70: Average Return = 102.49\n", + "Iteration 71: Average Return = 107.82\n", + "Iteration 72: Average Return = 112.37\n", + "Iteration 73: Average Return = 99.02\n", + "Iteration 74: Average Return = 113.06\n", + "Iteration 75: Average Return = 104.2\n", + "Iteration 76: Average Return = 115.43\n", + "Iteration 77: Average Return = 108.97\n", + "Iteration 78: Average Return = 116.72\n", + "Iteration 79: Average Return = 118.43\n", + "Iteration 80: Average Return = 109.3\n", + "Iteration 81: Average Return = 118.82\n", + "Iteration 82: Average Return = 131.41\n", + "Iteration 83: Average Return = 124.27\n", + "Iteration 84: Average Return = 130.17\n", + "Iteration 85: Average Return = 140.59\n", + "Iteration 86: Average Return = 146.39\n", + "Iteration 87: Average Return = 155.83\n", + "Iteration 88: Average Return = 159.49\n", + "Iteration 89: Average Return = 168.55\n", + "Iteration 90: Average Return = 172.3\n", + "Iteration 91: Average Return = 177.41\n", + "Iteration 92: Average Return = 176.14\n", + "Iteration 93: Average Return = 179.13\n", + "Iteration 94: Average Return = 182.21\n", + "Iteration 95: Average Return = 179.61\n", + "Iteration 96: Average Return = 181.13\n", + "Iteration 97: Average Return = 170.67\n", + "Iteration 98: Average Return = 183.08\n", + "Iteration 99: Average Return = 185.39\n", + "Iteration 100: Average Return = 181.76\n", + "Iteration 101: Average Return = 187.9\n", + "Iteration 102: Average Return = 184.79\n", + "Iteration 103: Average Return = 187.32\n", + "Iteration 104: Average Return = 192.09\n", + "Iteration 105: Average Return = 186.76\n", + "Iteration 106: Average Return = 187.4\n", + "Iteration 107: Average Return = 198.25\n", + "Solve at 107 iterations, which equals 10700 episodes.\n" ] } ], @@ -1411,6 +1157,13 @@ "np.random.seed(seed)\n", "tf.set_random_seed(seed)\n", "prng.seed(seed)\n", + "env.seed(seed)\n", + "\n", + "tf.reset_default_graph()\n", + "sess = tf.Session()\n", + "with tf.variable_scope(\"policy\"):\n", + " opt_p = tf.train.AdamOptimizer(learning_rate=0.01)\n", + " policy = CategoricalPolicy(in_dim, out_dim, hidden_dim, opt_p, sess)\n", "\n", "sess.run(tf.global_variables_initializer())\n", "\n", @@ -1425,36 +1178,8 @@ " discount_rate)\n", "\n", "# Train the policy optimizer\n", - "loss_list, avg_return_list = po.train()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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8BSooRCys6SR0mg7Az6E0oUPPn4Hyr9+uS8sSKXSsgCp0nOZCR9SzAlShQww+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\nAFRUVNDc3BxxTEtLC06nk8LCQkwmE+Xl5aHjZsyYwfTpkVe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3tEDhKdQg7YJyO6NMuc78G/qPv+m4p3JxIWh9+g9TdJz5Q+fJt+k//hrXs93X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\n+vyHoKAg9u3bR3R0tMdj+tWxmy856+7uRq1Ws3T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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "loss_list, avg_return_list = po.train()\n", + "\n", "util.plot_curve(loss_list, \"loss\")\n", "util.plot_curve(avg_return_list, \"average return\")" ] diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 759ad66..f91c21c 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -31,21 +31,18 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): """ # YOUR CODE HERE >>>>>> with tf.variable_scope("fc1"): - weights = tf.get_variable(name="weights", - initializer=tf.truncated_normal(shape=[in_dim, hidden_dim])) - biases = tf.get_variable(name="biases", - initializer=tf.constant(0., shape=[hidden_dim])) + weights = tf.Variable(tf.truncated_normal(shape=[in_dim, hidden_dim], seed=0)) + biases = tf.Variable(tf.truncated_normal(shape=[hidden_dim], seed=0)) logit = tf.nn.xw_plus_b(self._observations, weights, biases) act = tf.tanh(logit) - + self.weights = [weights, biases] with tf.variable_scope("fc2"): - weights = tf.get_variable(name="weights", - initializer=tf.truncated_normal(shape=[hidden_dim, out_dim])) - biases = tf.get_variable(name="biases", - initializer=tf.constant(0., shape=[out_dim])) + weights = tf.Variable(tf.truncated_normal(shape=[hidden_dim, out_dim], seed=0)) + biases = tf.Variable(tf.truncated_normal(shape=[out_dim], seed=0)) logit = tf.nn.xw_plus_b(act, weights, biases) softmax = tf.nn.softmax(logit) - + self.weights.append(weights) + self.weights.append(biases) probs = softmax # <<<<<<<< @@ -109,7 +106,7 @@ def act(self, observation): # expect observation to be of shape [1, observation_space] assert observation.shape[0] == 1 action_probs = self._sess.run(self._act_op, feed_dict={self._observations: observation}) - + #print(observation) # `action_probs` is an array that has shape [1, action_space], it contains the probability of each action # sample an action according to `action_probs` """ @@ -123,6 +120,8 @@ def act(self, observation): action_probs = [0.25, 0.25, 0.25, 0.25] idx = randint(3) """ + #print('weight:', self._sess.run(self.weights)) + #print('action prob:', action_probs) cs = np.cumsum(action_probs) idx = sum(cs < np.random.rand()) return idx diff --git a/policy_gradient/util.py b/policy_gradient/util.py index e67b439..bb818d5 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,8 +32,8 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> - #b = np.concatenate([b,[0]]) - y = x + discount_rate*b + b = np.concatenate([b,[0]]) + y = x + discount_rate*b[1:] return y # <<<<<<<<<<<<<<<<<<<<<<<<<<<< From 0b102b553cf02a5336295e52bf4af5aa81c6fbec Mon Sep 17 00:00:00 2001 From: wengbrian Date: Thu, 9 Nov 2017 22:13:25 +0800 Subject: [PATCH 3/4] upload report --- .DS_Store | Bin 6148 -> 6148 bytes Lab3-policy-gradient.ipynb | 380 +++++++++++++++++++++++++------------ fig/p3_loss.png | Bin 0 -> 20155 bytes fig/p3_return.png | Bin 0 -> 15244 bytes fig/p4_loss.png | Bin 0 -> 21653 bytes fig/p4_return.png | Bin 0 -> 15089 bytes fig/p5_loss.png | Bin 0 -> 19284 bytes fig/p5_return.png | Bin 0 -> 17477 bytes fig/p6_loss.png | Bin 0 -> 15415 bytes fig/p6_return.png | Bin 0 -> 16986 bytes report.md | 57 +++++- 11 files changed, 310 insertions(+), 127 deletions(-) create mode 100644 fig/p3_loss.png create mode 100644 fig/p3_return.png create mode 100644 fig/p4_loss.png create mode 100644 fig/p4_return.png create mode 100644 fig/p5_loss.png create mode 100644 fig/p5_return.png create mode 100644 fig/p6_loss.png create mode 100644 fig/p6_return.png diff --git a/.DS_Store b/.DS_Store index 3fc982675d181dc19018ab8fb09c70849f6dbded..b61f157a31f555b25af54f9b74f28d0c721c5de6 100644 GIT binary patch delta 235 zcmZoMXfc=|#>B!ku~2NHo}wrR0|Nsi1A_oVaY0f}eiD#(GO1v*A@g!(Zjcl+LmERS 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qWURGRhIZGenqWISb06kH4aIuKCFEzeFUsrjzzjtdHYdwc7qoCNJSUR172B2K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\nIUS5/j/o8Jmp02d9ugAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ @@ -428,7 +442,7 @@ "cell_type": "code", "execution_count": 7, "metadata": { - "scrolled": true + "scrolled": false }, "outputs": [ { @@ -607,7 +621,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -659,7 +673,9 @@ { "cell_type": "code", "execution_count": 16, - "metadata": {}, + "metadata": { + "scrolled": false + }, "outputs": [ { "name": "stderr", @@ -951,7 +967,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": { "collapsed": true }, @@ -1025,7 +1041,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": { "scrolled": false }, @@ -1045,112 +1061,224 @@ "Iteration 1: Average Return = 10.08\n", "Iteration 2: Average Return = 10.65\n", "Iteration 3: Average Return = 10.43\n", - "Iteration 4: Average Return = 10.11\n", - "Iteration 5: Average Return = 10.5\n", - "Iteration 6: Average Return = 10.89\n", - "Iteration 7: Average Return = 10.72\n", - "Iteration 8: Average Return = 11.55\n", - "Iteration 9: Average Return = 11.82\n", - "Iteration 10: Average Return = 12.94\n", - "Iteration 11: Average Return = 13.39\n", - "Iteration 12: Average Return = 14.73\n", - "Iteration 13: Average Return = 15.36\n", - "Iteration 14: Average Return = 16.54\n", - "Iteration 15: Average Return = 20.07\n", - "Iteration 16: Average Return = 21.16\n", - "Iteration 17: Average Return = 27.55\n", - "Iteration 18: Average Return = 29.23\n", - "Iteration 19: Average Return = 34.55\n", - "Iteration 20: Average Return = 44.27\n", - "Iteration 21: Average Return = 48.02\n", - "Iteration 22: Average Return = 52.26\n", - "Iteration 23: Average Return = 51.66\n", - "Iteration 24: Average Return = 51.36\n", - "Iteration 25: Average Return = 47.61\n", - "Iteration 26: Average Return = 44.38\n", - "Iteration 27: Average Return = 43.62\n", - "Iteration 28: Average Return = 42.3\n", - "Iteration 29: Average Return = 43.74\n", - "Iteration 30: Average Return = 41.57\n", - "Iteration 31: Average Return = 42.85\n", - "Iteration 32: Average Return = 47.78\n", - "Iteration 33: Average Return = 44.22\n", - "Iteration 34: Average Return = 46.47\n", - "Iteration 35: Average Return = 49.31\n", - "Iteration 36: Average Return = 50.2\n", - "Iteration 37: Average Return = 52.12\n", - "Iteration 38: Average Return = 54.35\n", - "Iteration 39: Average Return = 58.58\n", - "Iteration 40: Average Return = 61.6\n", - "Iteration 41: Average Return = 65.18\n", - "Iteration 42: Average Return = 62.52\n", - "Iteration 43: Average Return = 68.12\n", - "Iteration 44: Average Return = 64.27\n", - "Iteration 45: Average Return = 74.31\n", - "Iteration 46: Average Return = 71.63\n", - "Iteration 47: Average Return = 73.75\n", - "Iteration 48: Average Return = 73.88\n", - "Iteration 49: Average Return = 71.13\n", - "Iteration 50: Average Return = 74.95\n", - "Iteration 51: Average Return = 82.13\n", - "Iteration 52: Average Return = 80.74\n", - "Iteration 53: Average Return = 77.35\n", - "Iteration 54: Average Return = 85.08\n", - "Iteration 55: Average Return = 86.27\n", - "Iteration 56: Average Return = 86.01\n", - "Iteration 57: Average Return = 86.46\n", - "Iteration 58: Average Return = 90.21\n", - "Iteration 59: Average Return = 87.98\n", - "Iteration 60: Average Return = 97.56\n", - "Iteration 61: Average Return = 88.72\n", - "Iteration 62: Average Return = 92.56\n", - "Iteration 63: Average Return = 99.54\n", - "Iteration 64: Average Return = 106.79\n", - "Iteration 65: Average Return = 107.64\n", - "Iteration 66: Average Return = 107.36\n", - "Iteration 67: Average Return = 99.81\n", - "Iteration 68: Average Return = 106.64\n", - "Iteration 69: Average Return = 105.75\n", - "Iteration 70: Average Return = 102.49\n", - "Iteration 71: Average Return = 107.82\n", - "Iteration 72: Average Return = 112.37\n", - "Iteration 73: Average Return = 99.02\n", - "Iteration 74: Average Return = 113.06\n", - "Iteration 75: Average Return = 104.2\n", - "Iteration 76: Average Return = 115.43\n", - "Iteration 77: Average Return = 108.97\n", - "Iteration 78: Average Return = 116.72\n", - "Iteration 79: Average Return = 118.43\n", - "Iteration 80: Average Return = 109.3\n", - "Iteration 81: Average Return = 118.82\n", - "Iteration 82: Average Return = 131.41\n", - "Iteration 83: Average Return = 124.27\n", - "Iteration 84: Average Return = 130.17\n", - "Iteration 85: Average Return = 140.59\n", - "Iteration 86: Average Return = 146.39\n", - "Iteration 87: Average Return = 155.83\n", - "Iteration 88: Average Return = 159.49\n", - "Iteration 89: Average Return = 168.55\n", - "Iteration 90: Average Return = 172.3\n", - "Iteration 91: Average Return = 177.41\n", - "Iteration 92: Average Return = 176.14\n", - "Iteration 93: Average Return = 179.13\n", - "Iteration 94: Average Return = 182.21\n", - "Iteration 95: Average Return = 179.61\n", - "Iteration 96: Average Return = 181.13\n", - "Iteration 97: Average Return = 170.67\n", - "Iteration 98: Average Return = 183.08\n", - "Iteration 99: Average Return = 185.39\n", - "Iteration 100: Average Return = 181.76\n", - "Iteration 101: Average Return = 187.9\n", - "Iteration 102: Average Return = 184.79\n", - "Iteration 103: Average Return = 187.32\n", - "Iteration 104: Average Return = 192.09\n", - "Iteration 105: Average Return = 186.76\n", - "Iteration 106: Average Return = 187.4\n", - "Iteration 107: Average Return = 198.25\n", - "Solve at 107 iterations, which equals 10700 episodes.\n" + "Iteration 4: Average Return = 10.24\n", + "Iteration 5: Average Return = 10.9\n", + "Iteration 6: Average Return = 11.8\n", + "Iteration 7: Average Return = 11.62\n", + "Iteration 8: Average Return = 12.78\n", + "Iteration 9: Average Return = 14.62\n", + "Iteration 10: Average Return = 17.71\n", + "Iteration 11: Average Return = 21.66\n", + "Iteration 12: Average Return = 24.08\n", + "Iteration 13: Average Return = 29.15\n", + "Iteration 14: Average Return = 35.26\n", + "Iteration 15: Average Return = 38.5\n", + "Iteration 16: Average Return = 36.95\n", + "Iteration 17: Average Return = 37.7\n", + "Iteration 18: Average Return = 38.53\n", + "Iteration 19: Average Return = 32.56\n", + "Iteration 20: Average Return = 34.28\n", + "Iteration 21: Average Return = 36.93\n", + "Iteration 22: Average Return = 34.85\n", + "Iteration 23: Average Return = 39.25\n", + "Iteration 24: Average Return = 40.84\n", + "Iteration 25: Average Return = 45.73\n", + "Iteration 26: Average Return = 42.62\n", + "Iteration 27: Average Return = 49.18\n", + "Iteration 28: Average Return = 48.32\n", + "Iteration 29: Average Return = 49.92\n", + "Iteration 30: Average Return = 55.02\n", + "Iteration 31: Average Return = 54.99\n", + "Iteration 32: Average Return = 59.32\n", + "Iteration 33: Average Return = 58.2\n", + "Iteration 34: Average Return = 53.46\n", + "Iteration 35: Average Return = 60.33\n", + "Iteration 36: Average Return = 58.18\n", + "Iteration 37: Average Return = 60.39\n", + "Iteration 38: Average Return = 54.86\n", + "Iteration 39: Average Return = 53.26\n", + "Iteration 40: Average Return = 53.77\n", + "Iteration 41: Average Return = 57.74\n", + "Iteration 42: Average Return = 62.99\n", + "Iteration 43: Average Return = 57.09\n", + "Iteration 44: Average Return = 62.4\n", + "Iteration 45: Average Return = 64.03\n", + "Iteration 46: Average Return = 63.78\n", + "Iteration 47: Average Return = 67.13\n", + "Iteration 48: Average Return = 74.32\n", + "Iteration 49: Average Return = 65.57\n", + "Iteration 50: Average Return = 70.44\n", + "Iteration 51: Average Return = 77.52\n", + "Iteration 52: Average Return = 69.0\n", + "Iteration 53: Average Return = 67.96\n", + "Iteration 54: Average Return = 68.89\n", + "Iteration 55: Average Return = 74.8\n", + "Iteration 56: Average Return = 72.6\n", + "Iteration 57: Average Return = 71.29\n", + "Iteration 58: Average Return = 76.88\n", + "Iteration 59: Average Return = 76.73\n", + "Iteration 60: Average Return = 84.05\n", + "Iteration 61: Average Return = 83.74\n", + "Iteration 62: Average Return = 81.81\n", + "Iteration 63: Average Return = 83.34\n", + "Iteration 64: Average Return = 88.84\n", + "Iteration 65: Average Return = 92.73\n", + "Iteration 66: Average Return = 89.73\n", + "Iteration 67: Average Return = 100.84\n", + "Iteration 68: Average Return = 99.5\n", + "Iteration 69: Average Return = 100.02\n", + "Iteration 70: Average Return = 105.98\n", + "Iteration 71: Average Return = 108.62\n", + "Iteration 72: Average Return = 108.95\n", + "Iteration 73: Average Return = 121.67\n", + "Iteration 74: Average Return = 117.65\n", + "Iteration 75: Average Return = 119.5\n", + "Iteration 76: Average Return = 133.06\n", + "Iteration 77: Average Return = 121.35\n", + "Iteration 78: Average Return = 129.91\n", + "Iteration 79: Average Return = 130.6\n", + "Iteration 80: Average Return = 141.91\n", + "Iteration 81: Average Return = 144.43\n", + "Iteration 82: Average Return = 148.28\n", + "Iteration 83: Average Return = 155.76\n", + "Iteration 84: Average Return = 160.7\n", + "Iteration 85: Average Return = 175.64\n", + "Iteration 86: Average Return = 176.35\n", + "Iteration 87: Average Return = 173.2\n", + "Iteration 88: Average Return = 177.82\n", + "Iteration 89: Average Return = 171.12\n", + "Iteration 90: Average Return = 185.48\n", + "Iteration 91: Average Return = 176.56\n", + "Iteration 92: Average Return = 180.5\n", + "Iteration 93: Average Return = 175.53\n", + "Iteration 94: Average Return = 178.84\n", + "Iteration 95: Average Return = 177.17\n", + "Iteration 96: Average Return = 179.41\n", + "Iteration 97: Average Return = 173.87\n", + "Iteration 98: Average Return = 181.22\n", + "Iteration 99: Average Return = 178.76\n", + "Iteration 100: Average Return = 182.88\n", + "Iteration 101: Average Return = 182.3\n", + "Iteration 102: Average Return = 183.5\n", + "Iteration 103: Average Return = 190.48\n", + "Iteration 104: Average Return = 180.84\n", + "Iteration 105: Average Return = 189.44\n", + "Iteration 106: Average Return = 193.44\n", + "Iteration 107: Average Return = 182.5\n", + "Iteration 108: Average Return = 187.52\n", + "Iteration 109: Average Return = 190.58\n", + "Iteration 110: Average Return = 186.15\n", + "Iteration 111: Average Return = 185.75\n", + "Iteration 112: Average Return = 192.02\n", + "Iteration 113: Average Return = 182.12\n", + "Iteration 114: Average Return = 186.09\n", + "Iteration 115: Average Return = 180.29\n", + "Iteration 116: Average Return = 188.47\n", + "Iteration 117: Average Return = 182.65\n", + "Iteration 118: Average Return = 187.64\n", + "Iteration 119: Average Return = 189.63\n", + "Iteration 120: Average Return = 183.23\n", + "Iteration 121: Average Return = 188.25\n", + "Iteration 122: Average Return = 183.75\n", + "Iteration 123: Average Return = 183.57\n", + "Iteration 124: Average Return = 179.75\n", + "Iteration 125: Average Return = 181.73\n", + "Iteration 126: Average Return = 181.07\n", + "Iteration 127: Average Return = 188.71\n", + "Iteration 128: Average Return = 190.01\n", + "Iteration 129: Average Return = 182.47\n", + "Iteration 130: Average Return = 187.76\n", + "Iteration 131: Average Return = 190.18\n", + "Iteration 132: Average Return = 186.1\n", + "Iteration 133: Average Return = 184.43\n", + "Iteration 134: Average Return = 187.42\n", + "Iteration 135: Average Return = 183.89\n", + "Iteration 136: Average Return = 191.08\n", + "Iteration 137: Average Return = 184.78\n", + "Iteration 138: Average Return = 186.74\n", + "Iteration 139: Average Return = 185.42\n", + "Iteration 140: Average Return = 184.8\n", + "Iteration 141: Average Return = 186.89\n", + "Iteration 142: Average Return = 184.19\n", + "Iteration 143: Average Return = 186.96\n", + "Iteration 144: Average Return = 184.43\n", + "Iteration 145: Average Return = 181.07\n", + "Iteration 146: Average Return = 186.9\n", + "Iteration 147: Average Return = 184.73\n", + "Iteration 148: Average Return = 185.4\n", + "Iteration 149: Average Return = 185.32\n", + "Iteration 150: Average Return = 181.36\n", + "Iteration 151: Average Return = 185.77\n", + "Iteration 152: Average Return = 185.87\n", + "Iteration 153: Average Return = 190.28\n", + "Iteration 154: Average Return = 185.9\n", + "Iteration 155: Average Return = 186.17\n", + "Iteration 156: Average Return = 183.36\n", + "Iteration 157: Average Return = 182.99\n", + "Iteration 158: Average Return = 178.57\n", + "Iteration 159: Average Return = 183.16\n", + "Iteration 160: Average Return = 183.93\n", + "Iteration 161: Average Return = 181.09\n", + "Iteration 162: Average Return = 181.36\n", + "Iteration 163: Average Return = 180.34\n", + "Iteration 164: Average Return = 178.66\n", + "Iteration 165: Average Return = 183.6\n", + "Iteration 166: Average Return = 184.41\n", + "Iteration 167: Average Return = 168.21\n", + "Iteration 168: Average Return = 181.55\n", + "Iteration 169: Average Return = 175.31\n", + "Iteration 170: Average Return = 179.87\n", + "Iteration 171: Average Return = 186.2\n", + "Iteration 172: Average Return = 179.06\n", + "Iteration 173: Average Return = 184.86\n", + "Iteration 174: Average Return = 182.04\n", + "Iteration 175: Average Return = 182.17\n", + "Iteration 176: Average Return = 188.19\n", + "Iteration 177: Average Return = 186.86\n", + "Iteration 178: Average Return = 181.47\n", + "Iteration 179: Average Return = 184.95\n", + "Iteration 180: Average Return = 181.73\n", + "Iteration 181: Average Return = 188.52\n", + "Iteration 182: Average Return = 182.97\n", + "Iteration 183: Average Return = 185.27\n", + "Iteration 184: Average Return = 182.44\n", + "Iteration 185: Average Return = 183.3\n", + "Iteration 186: Average Return = 177.99\n", + "Iteration 187: Average Return = 185.23\n", + "Iteration 188: Average Return = 180.72\n", + "Iteration 189: Average Return = 183.36\n", + "Iteration 190: Average Return = 181.31\n", + "Iteration 191: Average Return = 184.01\n", + "Iteration 192: Average Return = 181.04\n", + "Iteration 193: Average Return = 181.97\n", + "Iteration 194: Average Return = 183.04\n", + "Iteration 195: Average Return = 179.3\n", + "Iteration 196: Average Return = 180.55\n", + "Iteration 197: Average Return = 184.47\n", + "Iteration 198: Average Return = 182.27\n", + "Iteration 199: Average Return = 184.47\n", + "Iteration 200: Average Return = 184.83\n" ] + }, + { + "data": { + "image/png": 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Wvg1rPoIBg1EX/sJ8ix8wGJXr++3Vfmk+ZPfGuuom9LuvQ1wCNDdhP/C/qH55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\nTjim72En/vnIzjVLmtdWm7W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FRKE8PeH6m+rdVvkHQt/r0f/5N5SVgKc3lJXgGdPrQrHo0gM145l636ux1Mhb0F98jOpZ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\nslBpR6GyEnVdVzxbeXNu5/f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+49,10 @@ a = util.discount(a, discount_rate*LAMBDA) ## Experiments all experiment are under the same random seed for environment and network initial weights. -|problem3|problem4|problem5|problem6| -|---|---|---|---| -|![](https://i.imgur.com/F1hzpO2.png)|![](https://i.imgur.com/DDDN8qn.png)|![](https://i.imgur.com/Qw1bNwv.png)|![](https://i.imgur.com/TcYEveS.png)| -|![](https://i.imgur.com/CIgBDGx.png)|![](https://i.imgur.com/aJuHqco.png)|![](https://i.imgur.com/SY5MnRO.png)|![](https://i.imgur.com/v02OZ37.png)|| +| |problem3|problem4|problem5|problem6| +|---|---|---|---|---| +|loss|![](https://i.imgur.com/F1hzpO2.png)|![](https://i.imgur.com/DDDN8qn.png)|![](https://i.imgur.com/Qw1bNwv.png)|![](https://i.imgur.com/TcYEveS.png)| +|average return|![](https://i.imgur.com/CIgBDGx.png)|![](https://i.imgur.com/aJuHqco.png)|![](https://i.imgur.com/SY5MnRO.png)|![](https://i.imgur.com/v02OZ37.png)|| ## Conclusion setting for problem4 converges the fastest in this task. maybe I implement something wrong...