diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..89ffa1f 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -3,9 +3,7 @@ { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [ "# Automatically reload changes to external code\n", @@ -28,17 +26,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -103,21 +95,26 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/andrew/miniconda2/envs/cedl/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + "/usr/local/lib/python3.4/dist-packages/tensorflow/python/ops/gradients_impl.py:91: 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" ] } ], "source": [ "tf.reset_default_graph()\n", - "sess = tf.Session()\n", + "\n", + "config = tf.ConfigProto()\n", + "config.gpu_options.allow_growth = True\n", + "sess = tf.Session(config=config)\n", + "# sess = tf.Session()\n", + "\n", "# Construct a neural network to represent policy which maps observed state to action. \n", "in_dim = util.flatten_space(env.observation_space)\n", "out_dim = util.flatten_space(env.action_space)\n", @@ -152,10 +149,8 @@ }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": null, + "metadata": {}, "outputs": [], "source": [ "class PolicyOptimizer(object):\n", @@ -214,6 +209,7 @@ " Sample solution should be only 1 line.\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>\n", + " a = r - b\n", " # <<<<<<<<\n", "\n", " p[\"returns\"] = r\n", @@ -258,98 +254,43 @@ }, { "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 = 38.33\n", + "Iteration 2: Average Return = 36.67\n", + "Iteration 3: Average Return = 41.39\n", + "Iteration 4: Average Return = 41.78\n", + "Iteration 5: Average Return = 46.85\n", + "Iteration 6: Average Return = 41.06\n", + "Iteration 7: Average Return = 49.29\n", + "Iteration 8: Average Return = 51.41\n", + "Iteration 9: Average Return = 49.02\n", + "Iteration 10: Average Return = 49.54\n", + "Iteration 11: Average Return = 51.24\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;31m# Train the policy optimizer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m 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\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.4/dist-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1002\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1003\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1004\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1005\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1006\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -360,7 +301,9 @@ "n_episode = 100\n", "path_length = 200\n", "discount_rate = 0.99\n", + "\n", "baseline = LinearFeatureBaseline(env.spec)\n", + "# baseline = None\n", "\n", "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", " discount_rate)\n", @@ -371,30 +314,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\nUoMToPBwi9sO6OKjOJ94AEw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jg+DgYGbPno23tzdaa9566y1SUlJo0qQJ0dHRhIfLus11SZ/4Af3ZWtTQURIU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\nyo892QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "util.plot_curve(loss_list, \"loss\")\n", "util.plot_curve(avg_return_list, \"average return\")" @@ -477,10 +399,8 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "# set the hyperparameter for generalized advantage estimation (GAE)\n", @@ -523,6 +443,7 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " a = util.discount(r, LAMBDA * self.discount_rate)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -543,7 +464,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -552,90 +473,105 @@ "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 1: Average Return = 22.13\n", + "Iteration 2: Average Return = 28.24\n", + "Iteration 3: Average Return = 27.37\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 5: Average Return = 32.29\n", + "Iteration 6: Average Return = 32.24\n", + "Iteration 7: Average Return = 40.07\n", + "Iteration 8: Average Return = 35.99\n", + "Iteration 9: Average Return = 37.54\n", + "Iteration 10: Average Return = 39.2\n", + "Iteration 11: Average Return = 40.52\n", + "Iteration 12: Average Return = 42.35\n", + "Iteration 13: Average Return = 44.43\n", + "Iteration 14: Average Return = 45.16\n", + "Iteration 15: Average Return = 41.27\n", + "Iteration 16: Average Return = 46.91\n", + "Iteration 17: Average Return = 48.0\n", + "Iteration 18: Average Return = 47.66\n", + "Iteration 19: Average Return = 47.73\n", + "Iteration 20: Average Return = 50.43\n", + "Iteration 21: Average Return = 47.1\n", + "Iteration 22: Average Return = 54.46\n", + "Iteration 23: Average Return = 47.67\n", + "Iteration 24: Average Return = 50.93\n", + "Iteration 25: Average Return = 55.73\n", + "Iteration 26: Average Return = 53.74\n", + "Iteration 27: Average Return = 56.44\n", + "Iteration 28: Average Return = 53.66\n", + "Iteration 29: Average Return = 54.02\n", + "Iteration 30: Average Return = 56.2\n", + "Iteration 31: Average Return = 59.24\n", + "Iteration 32: Average Return = 60.41\n", + "Iteration 33: Average Return = 54.51\n", + "Iteration 34: Average Return = 58.46\n", + "Iteration 35: Average Return = 59.67\n", + "Iteration 36: Average Return = 59.1\n", + "Iteration 37: Average Return = 60.24\n", + "Iteration 38: Average Return = 60.95\n", + "Iteration 39: Average Return = 61.45\n", + "Iteration 40: Average Return = 59.78\n", + "Iteration 41: Average Return = 61.5\n", + "Iteration 42: Average Return = 60.6\n", + "Iteration 43: Average Return = 64.26\n", + "Iteration 44: Average Return = 65.64\n", + "Iteration 45: Average Return = 67.18\n", + "Iteration 46: Average Return = 67.38\n", + "Iteration 47: Average Return = 73.31\n", + "Iteration 48: Average Return = 72.05\n", + "Iteration 49: Average Return = 68.66\n", + "Iteration 50: Average Return = 72.51\n", + "Iteration 51: Average Return = 71.16\n", + "Iteration 52: Average Return = 74.34\n", + "Iteration 53: Average Return = 85.4\n", + "Iteration 54: Average Return = 76.47\n", + "Iteration 55: Average Return = 84.77\n", + "Iteration 56: Average Return = 89.04\n", + "Iteration 57: Average Return = 90.94\n", + "Iteration 58: Average Return = 93.36\n", + "Iteration 59: Average Return = 94.17\n", + "Iteration 60: Average Return = 108.22\n", + "Iteration 61: Average Return = 107.92\n", + "Iteration 62: Average Return = 115.48\n", + "Iteration 63: Average Return = 128.48\n", + "Iteration 64: Average Return = 146.68\n", + "Iteration 65: Average Return = 156.94\n", + "Iteration 66: Average Return = 175.66\n", + "Iteration 67: Average Return = 172.83\n", + "Iteration 68: Average Return = 179.89\n", + "Iteration 69: Average Return = 183.69\n", + "Iteration 70: Average Return = 184.61\n", + "Iteration 71: Average Return = 180.12\n", + "Iteration 72: Average Return = 183.11\n", + "Iteration 73: Average Return = 178.04\n", + "Iteration 74: Average Return = 186.24\n", + "Iteration 75: Average Return = 180.67\n", + "Iteration 76: Average Return = 175.88\n", + "Iteration 77: Average Return = 178.37\n", + "Iteration 78: Average Return = 186.55\n", + "Iteration 79: Average Return = 183.73\n", + "Iteration 80: Average Return = 180.25\n", + "Iteration 81: Average Return = 188.68\n", + "Iteration 82: Average Return = 181.71\n", + "Iteration 83: Average Return = 189.05\n", + "Iteration 84: Average Return = 192.01\n", + "Iteration 85: Average Return = 192.68\n", + "Iteration 86: Average Return = 191.92\n", + "Iteration 87: Average Return = 191.8\n", + "Iteration 88: Average Return = 191.74\n", + "Iteration 89: Average Return = 189.48\n", + "Iteration 90: Average Return = 189.22\n", + "Iteration 91: Average Return = 187.72\n", + "Iteration 92: Average Return = 189.28\n", + "Iteration 93: Average Return = 191.68\n", + "Iteration 94: Average Return = 193.41\n", + "Iteration 95: Average Return = 192.42\n", + "Iteration 96: Average Return = 190.9\n", + "Iteration 97: Average Return = 194.95\n", + "Iteration 98: Average Return = 195.55\n", + "Solve at 98 iterations, which equals 9800 episodes.\n" ] } ], @@ -659,13 +595,15 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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n4eY8HA7LRqO9vR3hcLhgTTAYhM/nkx97vV4Eg8V19uptWlpa4HQ6MTk5idbW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\neoWavzsseA2ctDTo7NDXzAkUEgR9oZvSUL4ZC9S+/0G9+l+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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds\nNhv3338/3t6Vr+IlRE1ow0CvfBUOfYe67zFU526O51TrMNTd0RZWJ4T1ZFnVn8npsDkNrZ10brZ9\ncaD/fQ5n01E334lt3B01ft+G1k7OIu1kTqPvhhLCKlpr9Kq/2xcFKi+HLj2xTZwGfQdbXZoQLknC\nQjROPx5Fb/kMNWg4auwkVIhMxyHE5UhYiEZJ79oKNhvq9vtQ3jIdhxCVkTW4RaOjtUbv2gZdekpQ\nCGGShIVofH46AeknUTI+IYRpEhai0dG7toGyofoMtLoUIeoNCQvR6OhdW6FzN5SPv9WlCFFvSFiI\nRkWf/BFOpaL6SReUEFUhYSEaFb17GyiF6nPh5H9CiEuTsBCNit71FXTogvILrPzFQggHCQvRYOmS\nEvTB79AF+fbHZ05C2jHpghKiGuSmPNEg6cICjEXPwLFDoGzQLhyaNgNA9ZGwEKKqJCxEg6ML8zEW\n/Q1+PIqaPB3y89DffwNH9kPn7qjAYKtLFKLekbAQDYouzMeIewZSj2Gb+RSq98/3UoyfjC4uAjc3\nawsUop6SsBANhi4t/TUoHpiD6nVNhefVz91QQoiqkwFu0XB8vweOH0ZNnXVBUAghakbCQjQYeu9O\naNIU1W+o1aUI0eBIWIgGQWuN/nYnXN0L5eFhdTlCNDgSFqJhOPkjZGWgevS3uhIhGiQJC9Eg6L07\nAVDd+1lciRANU51cDRUfH8/u3bvx9fUlNjYWgDVr1rBhwwZ8fOyLz0yePJm+ffsCsHbtWjZu3IjN\nZmPatGn07t27LsoU9ZjeuxPatkcFBFldihANUp2ExfDhw7nhhhtYunRphe1jx45l/PjxFbalpaWx\nbds2Xn75ZbKzs5k3bx6vvPIKNpucBImL0wX5cOR71A1/sLoUIRqsOvkJ3LVrV7y9vU29Njk5mcGD\nB+Ph4UHLli0JCQnhyJEjTq5Q1Gd6/x4wDFQP6YISwlksvSlv/fr1bNmyhfDwcKZMmYK3tzdZWVl0\n6tTJ8ZqAgACysrIsrFK4vL3J0LwFhF9ldSVCNFiWhcXo0aO57bbbAFi9ejUrV64kOjq6Su+RmJhI\nYmIiADExMQQFVb+/2t3dvUb7Nxau1k7aMMjYl0LTfhH4tmxldTkOrtZOrkrayRxXaCfLwsLPz8/x\n9ciRI1mwYAFgP5M4e/as47msrCwCAgIu+h5RUVFERUU5HmdmZla7nqCgoBrt31i4WjvpowfR53Io\n6dTdpepytXZyVdJO5jiznUJDQ029zrJR4+zsbMfXO3bsICwsDID+/fuzbds2SktLSU9P59SpU3Ts\n2NGqMoWL03t3gbKhuve1uhQhGjRTZxb5+fl89NFHnDhxguLi4grPPfvss5Xuv2jRIvbv309eXh4z\nZ85k0qRJ7Nu3j+PHj6OUIjg4mOnTpwMQFhZGREQEjz76KDabjXvvvVeuhBIO+ttk9Feb0Hm5kJcL\nmachvDPK28fq0oRo0JTWWlf2ovnz51NWVkZERASenp4Vnhs+fLizaquykydPVntfOR02x6p20uXl\n6LUr0evXgl8ABIVACx+Utw9q8AhUx651XtPlyOfJHGknc1yhG8rUmcWhQ4d4/fXX8ZA5d4QFdE4W\nxvKFcGgfaviNqEn3yfxPQtQxU2HRrl07zp49S0hIiLPrEaICffonjIV/geIi1L2PYhs03OqShGiU\nTIVF9+7def755xk+fHiFq5gARowY4ZTChNBGOcaKV6CsDNvTL6HaXGF1SUI0WqbC4sCBAwQGBrJ3\n794LnpOwEM6iN30CPxxA/Wm2BIUQFqs0LLTWzJw5k6CgINxk/WJRR3TGafSHK6F7P5R0PQlhuUqv\nSVVK8fjjj6OUqot6hEBrjbHyVbDZsN0TLZ89IVyAqRsYrrzySk6dOuXsWoQAQP/vczjwLWriNFRA\nsNXlCCEwOWbRrVs3nn/+eSIjIy+Yn0TGLERt0kWF6PdXwFU9UMOut7ocIcTPTIXFwYMHadmyJd9/\n//0Fz0lYiNqkt22EogJsf5gq3U9CuBBTYfHMM884uw4h0IaB3vRfaN8Z1b5T5TsIIeqMqTELwzAu\n+UeIWrN/D5z5CTVinNWVCCF+x9SZxeTJky/53OrVq2utGNG4GRv/Az5+qP5DrC5FCPE7psLi1Vdf\nrfA4OzubhIQE+vfv75SiROOj00/Bd7tQY29Hucu8T0K4GlPdUMHBwRX+dO7cmYceeoh169Y5uz7R\nSOjNn4DNhoqUK6CEcEXVXiiisLCQc+fO1WYtopHSJcXorYmovoNRfoFWlyOEuAhT3VBLliypcBlj\nSUkJ33//PcOGDXNaYaLx0Ns3Q2EBasRYq0sRQlyCqbD4/dTkTZo0YdSoUfTs2dMpRYnGQxcWoP+7\nBq7oCB2utrocIcQlmAqL3r1706nThde9HzlyRNbHFjWi338LcrKwPfAXuQlPCBdmasziueeeu+j2\n+fPn12oxonHR+1LQ//scdf0tchOeEC7usmcWv9x0p7V2/PnFmTNnZMpyUW26qBBj5RIIaYsaf+n7\neIQQruGyYfHbm/HuuOOOCs/ZbDZuueUWUweJj49n9+7d+Pr6EhsbC8Dbb7/Nrl27cHd3p1WrVkRH\nR9O8eXPS09OZPXu2YxHxTp06MX369Cp9U8L16fdXQHYWtqdiUB6eVpcjhKjEZcPi1VdfRWvN3/72\nN5599lm01iilUErh4+ODp6e5/+TDhw/nhhtuYOnSpY5tPXv25M4778TNzY1Vq1axdu1a7r77bsA+\noL5w4cIafFvClelD+9BbPkONvgXVoYvV5QghTLhsWAQH29cSiI+PB+zdUrm5ufj7+1fpIF27diU9\nPb3Ctl69ejm+7ty5M9u3b6/Se4r6S2/fBM28UDffaXUpQgiTTF0NVVBQwOuvv8727dtxd3fn7bff\nZufOnRw5cuSC7qnq2LhxI4MHD3Y8Tk9P58knn6RZs2bccccdXH21XFLZUGit0d/uRHXtg/JsYnU5\nQgiTTIXF8uXLad68OfHx8Tz66KOA/Wxg5cqVNQ6LDz/8EDc3N8cNfv7+/sTHx9OiRQuOHj3KwoUL\niY2NxcvL64J9ExMTSUxMBCAmJuaChZmqwt3dvUb7NxY1bafSHw6SlZtFi8HX0awBt7d8nsyRdjLH\nFdrJVFjs3buX1157DXf3X1/u4+NDbm5ujQ6+efNmdu3axdy5cx3X2Ht4eODhYZ9ILjw8nFatWnHq\n1Ck6dOhwwf5RUVFERUU5HmdmZla7lqCgoBrt31jUtJ2M/30BSpF/ZWcKGnB7y+fJHGknc5zZTr9c\nTFQZU/dZeHl5kZeXV2FbZmZmlccufmvPnj2sW7eOp556iiZNfu2OOHfunOOS3TNnznDq1ClatWpV\n7eMI16K/3QlXdkL5+FldihCiCkydWYwcOZLY2FjuuOMOtNYcOnSId955h1GjRpk6yKJFi9i/fz95\neXnMnDmTSZMmsXbtWsrKypg3bx7w6yWy+/fvZ82aNbi5uWGz2bj//vvx9vau/ncoXIY+lwPHD8t9\nFULUQ6bC4uabb8bT05M33niD8vJy/v73vxMVFcWYMWNMHeSRRx65YNul1u4eNGgQgwYNMvW+on7R\n3+0CrVE9BlhdihCiiioNC8Mw2Lx5M6NGjTIdDkLo4iLw8ET99i7/b3eCXwC0C7euMCFEtVQ6ZmGz\n2Vi5cqVj0FmIyuiSEoz/92eM5x9DFxbYt5WVofenoHr0lwkDhaiHTA1w9+vXj507dzq7FtFA6PUf\nQsZp+OkExqvz0CUlcGQ/FBWieshSvELUR6bGLEpLS3n55Zfp3LkzgYGBFX4zfOihh5xWnKh/9NkM\n9PoPUP2HQt8I9PKXMF5bgGrZGtzd4epelb+JEMLlmAqLsLAwwsLCnF2LaAD0+28BoG6bhgoMxigs\nQK+KRwN07YNq2szS+oQQ1WMqLCZOnOjsOkQDoA9+h975JeqmyahA+7xitsgbMAry0GvfRvUeaHGF\nQojqMhUWQlRGG+UY7y6HgGDU9bdWeE7deBuqe19o296i6oQQNWVqgFuIyuikzyDtGLaJ01BNKk4Q\nqJRCteuAssnHTYj6Sv73ihrT6Sftixl17Q39hlhdjhDCCSQsRI3o8nKMN+LA3R3b1D/LPRRCNFCm\nxiy01mzYsIGtW7eSl5fHSy+9xP79+8nJyamwDoVofPRnH8DRg6j7H0f5B1pdjhDCSUydWaxevZpN\nmzYRFRXlmCY3MDCQdevWObU44dr0iSPoj99BDRiG7ZprrS5HCOFEpsIiKSmJp556iiFDhji6GVq2\nbHnBUqmi8dDFhRivvwwt/FB3zbS6HCGEk5kKC8MwaNq0aYVtxcXFF2wTjYM+lYbx/BNw5iS2aQ+j\nmrewuiQhhJOZCos+ffqwcuVKSktLAfsYxurVq+nXr59TixOup/irTRjzH4O8XGyzn0V17WN1SUKI\nOmAqLKZMmUJ2djZTp06lsLCQKVOmkJGRwV133eXs+oSL0FpjfPBPcl/8PwgNw/bXOJTM8yREo2Hq\naigvLy+eeOIJcnJyyMzMJCgoCD8/WRazsdBao/+9DL35U5qNvpmSCVNQMmW9EI2KqbD4ZU1sHx8f\nfHx8HNtsckdug/fboFDX30qLGY9x/uxZq8sSQtQxU2ExefLF10x2c3PD39+fgQMHMmnSJBnwbmB+\nHxTqD3+Um+6EaKRMhcW0adNITk5mwoQJBAYGkpmZyUcffUTfvn0JDQ3lvffeY8WKFcycKZdQNiR6\n7UoJCiEEYDIs/vvf/7JgwQK8vLwACA0NpUOHDsyZM4clS5bQrl07nnrqqcu+R3x8PLt378bX15fY\n2FgA8vPziYuLIyMjg+DgYGbPno23tzdaa9566y1SUlJo0qQJ0dHRhIfLus11SZ/4Af3ZWtTQURIU\nQghzV0MVFhZSUlJSYVtJSQmFhYUA+Pn5cf78+cu+x/Dhw3n66acrbEtISKBHjx4sXryYHj16kJCQ\nAEBKSgqnT59m8eLFTJ8+nddff930NyRqThvlGKvioYUPauI0CQohhLmwiIyM5LnnniMxMZE9e/aw\nYcMG5s+fT2RkJADffPMNoaGhl32Prl274u3tXWFbcnKy4z0iIyNJTk4GYOfOnVx77bUopejcuTMF\nBQVkZ2dX+ZsT1aO3rIfjh1ET/4Ty8q58ByFEg2eqG+ruu+8mJCSEbdu2kZ2djZ+fH9dffz1RUVEA\ndOvWjWeffbbKB8/NzcXf3x+wn53k5uYCkJWVRVBQkON1gYGBZGVlOV4rnEefy0Z/+DZ06YkaGGl1\nOUIIF2EqLGw2G6NHj2b06NEXfd7T07PGhSilqtzdkZiYSGJiIgAxMTEVAqaq3N3da7R/Q5G7ainF\npSUEPjgH9+DgC56XdjJH2skcaSdzXKGdTC+rmpOTw5EjR8jLy0Nr7dg+YsSIah/c19eX7Oxs/P39\nyc7OdtzDERAQ4JjdFuDs2bMEBARcsH9UVJTj7AaosE9VBQUF1Wj/hkAf3o+RtB41ZhI5Tb3hIu0h\n7WSOtJM50k7mOLOdKhtC+IWpsNixYwdLliyhdevWpKamEhYWRmpqKl26dKlRWPTv35+kpCQmTJhA\nUlISAwYMcGz/7LPPGDJkCIcPH8bLy0u6oOqA3vwpeHmjxky0uhQhhIsxFRarV68mOjqaiIgIpk2b\nxosvvsimTZtITU01faBFixaxf/9+8vLymDlzJpMmTWLChAnExcWxceNGx6WzYJ+4cPfu3Tz88MN4\nenoSHR1dve9OmKaLC9F7vkINGnHBGtpCCGEqLDIzM4mIiKiwLTIykunTpzNlyhRTB3rkkUcuun3u\n3LkXbFNKcd9995l6X1E12igHQNncKm7fvR3On0dFDLegKiGEqzN16ayPjw85OTkABAcHc+jQIc6c\nOeOYM0rUH8aiv2G8tvCC7Xr7JghqBR2utqAqIYSrM3VmMXLkSA4cOMCgQYMYO3Yszz77LEopxo0b\n5+z6RC3SZaVwaB+Ul6H3pzjWotA5Z+HAt6ixk+QGPCHERZkKi/HjxztmmI2MjKRbt24UFxfTtm1b\npxYnatmpNCgvA2XDWPMmtr8uQrm5ob/eAlqjBl1ndYVCCBdVaTeUYRjcc889jlXywH4ZlwRF/aN/\nPAqAmnAkt/EjAAAWbElEQVQX/HQC/eUX9u3bN0H7zqhW5i6hE0I0PpWGhc1mIzQ0lLy8vLqoRzhT\n6lFo0hR1w63QuRt63b/Qh/dD2nHUoOFWVyeEcGGmuqGGDh3KggULuPHGGwkMDKzQr929e3enFSdq\nl049Cm2vRNncsE26F2P+Yxjxz4ObG2rAtVaXJ4RwYabC4vPPPwfgvffeq7BdKcWrr75a+1WJWqcN\nA1KPoQYOB0Bd0REVMQK9bQP0ugbVwsfaAoUQLs1UWCxdutTZdQhnO5sORYUQ1t6xSd1yN/roQWwj\nxlpYmBCiPjA9N1RZWRmHDx8mOzubwYMHU1xcDCBLqdYXvwxut/t1ESnlF4jbvHirKhJC1COmwuLH\nH39kwYIFeHh4cPbsWQYPHsz+/ftJSkpyTNEhXJv+8SjYbNDmCqtLEULUQ6bu4F6+fDm33347ixYt\nwt3dni9du3blwIEDTi1O1B6dehRah6E8aj6dvBCi8TEVFmlpaQwbNqzCtqZNm1a6lKpwIalHUWGy\njrkQonpMhUVwcDBHjx6tsO3IkSOEhIQ4pShRu/S5HMjJqjC4LYQQVWFqzOL2228nJiaGUaNGUVZW\nxtq1a/niiy+YMWOGs+sTtSH1GFBxcFsIIarC1JlFv379ePrppzl37hxdu3YlIyODxx9/nF69ejm7\nPlELfpnmQ84shBDVZerM4ty5c7Rv317WmKivUo9CYEtU8xZWVyKEqKdMhUV0dDTdunVj6NChDBgw\nQO6tqGd06lGQwW0hRA2Y6oaKj4+nb9++fP7550yfPp1Fixaxc+dOysvLnV2fqCFdXARnTqKkC0oI\nUQOmzix8fHy4/vrruf7668nIyGDr1q28++67/P3vf+eNN95wdo2iJtKO29eqkMFtIUQNmJ7u4xe5\nubnk5OSQl5dH8+bNnVGTqCFdVAgZpyDjtH1tbZBuKCFEjZgKi7S0NL788ku2bt3K+fPniYiI4Ikn\nnqBjx441OvjJkyeJi4tzPE5PT2fSpEkUFBSwYcMGfHzsM6FOnjyZvn371uhYDZ3WGn44gPHFOkjZ\nDvo366Nf0RECgqwrTghR75kKi7/+9a8MHDiQ6dOn061bN8cSqzUVGhrKwoULAfuKfDNmzOCaa65h\n06ZNjB07lvHjx9fKcRo6vXcXxsfvwLFD4OWNGn0zqv1VEBwCQa1QXnIGKISoGVNhsXz5csecUM6y\nd+9eQkJCCA4OdupxGhqdcRrj1Xn2S2PvnIkaPALVRK5WE0LULlMJ4O7uTk5ODkeOHCEvL8/e5fGz\nESNG1EohW7duZciQIY7H69evZ8uWLYSHhzNlyhS8vb1r5TgNjf7sQ7DZsD35Asov0OpyhBANlNK/\n/cl/CTt27GDJkiW0bt2a1NRUwsLCSE1NpUuXLjzzzDM1LqKsrIwZM2YQGxuLn58fOTk5jvGK1atX\nk52dTXR09AX7JSYmkpiYCEBMTEyNJjZ0d3enrKys2vtbofxsBpkzb6PZyHH4zHyiTo5ZH9vJCtJO\n5kg7mePMdvL0NDcTtakzi9WrVxMdHU1ERATTpk3jxRdfZNOmTaSmptaoyF+kpKTQvn17/Pz8ABx/\nA4wcOZIFCxZcdL+oqCiioqIcjzMzM6tdQ1BQUI32t4Kx+g0wyikZPqbOaq+P7WQFaSdzpJ3McWY7\nhYaGmnqdqZHqzMxMIiIiKmyLjIxky5YtVa/sIn7fBZWdne34eseOHYSFhdXKcRoSfS4HveUz1KDr\nUEGtrC5HCNHAmb4pLycnBz8/P4KDgzl06BAtWrTAMIzKd65EcXEx3377LdOnT3dsW7VqFcePH0cp\nRXBwcIXnhJ1OXAelpagb/2B1KUKIRsBUWIwcOZIDBw4waNAgxo4dy7PPPotSinHjxtW4gKZNm/Lm\nm29W2DZr1qwav29Dpgvy0Zs+QfUfigppa3U5QohGwFRYTJgwwfF1ZGQk3bp1o7i4mLZt5QeVFfQX\nCVBchBpzm9WlCCEaiWrdPBEUJHcDW0V//w36k/dR10Si2srkgEKIulE7t2KLOqGzMjD+sRBC2qDu\necDqcoQQjYiERT2hS0sxli2AslJsD/wF1dTL6pKEEI2IhEU9od/9Bxw7hG3aI6jWMlYkhKhbEhb1\ngN79FXrLetSNf0D1jah8ByGEqGUSFi5Oa43xn3ft4xQT7ra6HCFEIyVh4er2pUDqMdT1t6JsblZX\nI4RopCQsXJzx2QfgF4gaNNzqUoQQjZiEhQvTPxyAg3tRoyeg3D2sLkcI0YhJWLgw47MPoHkL1LDR\nVpcihGjkJCxclD75I+z5GjViLKppM6vLEUI0cs5dK1WYprWG8+ehvAzKy9GfvAeeTVDX1XyyRiGE\nqCkJCxdhvPI3+5VPv6FG3oRq4WNNQUII8RsSFi5Apx2HfSmoAcPgyk7g5g6enqh+QyrdVwgh6oKE\nhQvQW9aDuzvqzhkobzmTEEK4HhngtpguKUFv34zqO0SCQgjhsiQsLKZ3/g+KClCR11tdihBCXJKE\nhcX0lvXQOgw6dbO6FCGEuCQJCwvptGNw9CDq2tEopawuRwghLsklBrgffPBBmjZtis1mw83NjZiY\nGPLz84mLiyMjI4Pg4GBmz56Nt7e31aXWKp20Htw9UBEjrC5FCCEuyyXCAuCZZ57Bx+fXAd6EhAR6\n9OjBhAkTSEhIICEhgbvvbjhTdOuSYvTXm1H9h6Cat7C6HCGEuCyX7YZKTk4mMjISgMjISJKTky2u\nqHbpr5OgqBB17Q1WlyKEEJVymTOL+fPnAzBq1CiioqLIzc3F398fAD8/P3Jzc60sr1bp8yXo/6yG\nKzpCx6utLkcIISrlEmExb948AgICyM3N5bnnniM0NLTC80qpiw4AJyYmkpiYCEBMTAxBQUHVrsHd\n3b1G+1dFwQcryc/OxP/Rv+EZHFwnx6wtddlO9Zm0kznSTua4Qju5RFgEBAQA4Ovry4ABAzhy5Ai+\nvr5kZ2fj7+9PdnZ2hfGMX0RFRREVFeV4nJmZWe0agoKCarS/WfpcDsb7/4Re13AupB3UwTFrU121\nU30n7WSOtJM5zmyn3/9yfimWj1kUFxdTVFTk+Prbb7+lXbt29O/fn6SkJACSkpIYMGCAlWXWGv3x\nO3C+BNttU60uRQghTLP8zCI3N5eXXnoJgPLycoYOHUrv3r3p0KEDcXFxbNy40XHpbH2nT6Wit6xH\nRd6ACmlrdTlCCGGa5WHRqlUrFi5ceMH2Fi1aMHfuXAsqch7j/RXQpCnqpslWlyKEEFVieVg0Bjr1\nGMbH78C3yahb/4hq4Wt1SUIIUSUSFk6kf/oR46N/we6voJkX6qY7UKNutrosIYSoMgkLJ9GFBRgL\nngK0PSRGjkc1b1jTlQghGg8JCyfRWxOhqADb/8WiruxkdTlCCFEjll862xBpoxy94WPo1BUJCiFE\nQyBh4Qx7dsDZdGwjx1tdiRBC1AoJCycwNnwEgS2h90CrSxFCiFohYVHL9I8/wKF9qBFjUW5uVpcj\nhBC1QsKilunEj+033g0dZXUpQghRa+RqqBrQJSXob3eggkIgNAyKi9DJW1DDrkd5yWWyQoiGQ8Ki\nBvTq5ej/fY7+ZUPzFlBWhhoxzsqyhBCi1klYVJPel4L+3+eo68aguvRCnzwBaSegdRgqpI3V5Qkh\nRK2SsKgGXViAsXKJPRgm/gnl4YnqG2F1WUII4TQywF0N+v23IDsL29SHUR6eVpcjhBBOJ2FRRY7u\np9ETUOFXWV2OEELUCQmLKtCHvsNYsdje/XTznVaXI4QQdUbGLH5H79qK8dmHqF4DUAOuRbUKRedk\noT9Ygd6+GQKCsd33qHQ/CSEaFQmL39DnSzDeXQ4lJeh1/0av+ze0C4f0U1BWiho7CXXjRFSTJlaX\nKoQQdUrC4jf0pk8gJwvb489DcAh655fo3dugSy9st01FtQq1ukQhhLCEhMXPjIJ89KfvQ7c+qKu6\nA6BGT4DREyyuTAghrGdpWGRmZrJ06VJycnJQShEVFcWYMWNYs2YNGzZswMfHB4DJkyfTt29fp9ZS\nuO4dKMjDdssUpx5HCCHqI0vDws3NjXvuuYfw8HCKioqYM2cOPXv2BGDs2LGMH18360HoczkUfvwu\nqt8Q1BUd6uSYQghRn1gaFv7+/vj7+wPQrFkz2rRpQ1ZWVp3XoT95D33+PLYJd9X5sYUQoj5wmfss\n0tPTOXbsGB07dgRg/fr1PP7448THx5Ofn++04+qzGeikT2k6YgwqpK3TjiOEEPWZ0lrryl/mXMXF\nxTzzzDPceuutDBw4kJycHMd4xerVq8nOziY6OvqC/RITE0lMTAQgJiaG8+fPV/nYZT+dIO+NRfjP\n+v/AP7Bm30gj4O7uTllZmdVluDxpJ3OkncxxZjt5epq7Z8zysCgrK2PBggX06tWLceMunNo7PT2d\nBQsWEBsbW+l7nTx5stp1BAUFkZmZWe39GwtpJ3OkncyRdjLHme0UGmrulgBLu6G01ixbtow2bdpU\nCIrs7GzH1zt27CAsLMyK8oQQQvzM0gHugwcPsmXLFtq1a8cTTzwB2C+T3bp1K8ePH0cpRXBwMNOn\nT7eyTCGEaPQsDYsuXbqwZs2aC7Y7+54KIYQQVeMyV0MJIYRwXRIWQgghKiVhIYQQolISFkIIISol\nYSGEEKJSlt+UJ4QQwvXJmcXP5syZY3UJ9YK0kznSTuZIO5njCu0kYSGEEKJSEhZCCCEqJWHxs6io\nKKtLqBekncyRdjJH2skcV2gnGeAWQghRKTmzEEIIUSlLJxJ0BXv27OGtt97CMAxGjhzJhAkTrC7J\nJWRmZrJ06VJycnJQShEVFcWYMWPIz88nLi6OjIwMgoODmT17Nt7e3laXaznDMJgzZw4BAQHMmTOH\n9PR0Fi1aRF5eHuHh4cyaNQt390b/342CggKWLVtGamoqSikeeOABQkND5TP1O//5z3/YuHEjSinC\nwsKIjo4mJyfH0s9Uoz6zMAyDN954g6effpq4uDi2bt1KWlqa1WW5BDc3N+655x7i4uKYP38+69ev\nJy0tjYSEBHr06MHixYvp0aMHCQkJVpfqEj755BPatGnjeLxq1SrGjh3LkiVLaN68ORs3brSwOtfx\n1ltv0bt3bxYtWsTChQtp06aNfKZ+Jysri08//ZSYmBhiY2MxDINt27ZZ/plq1GFx5MgRQkJCaNWq\nFe7u7gwePJjk5GSry3IJ/v7+hIeHA9CsWTPatGlDVlYWycnJREZGAhAZGSntBZw9e5bdu3czcuRI\nwL6o1759+xg0aBAAw4cPl3YCCgsL+f777xkxYgRgXyq0efPm8pm6CMMwOH/+POXl5Zw/fx4/Pz/L\nP1ON+rw4KyuLwMBf190ODAzk8OHDFlbkmtLT0zl27BgdO3YkNzcXf39/APz8/MjNzbW4OuutWLGC\nu+++m6KiIgDy8vLw8vLCzc0NgICAALKysqws0SWkp6fj4+NDfHw8J06cIDw8nKlTp8pn6ncCAgK4\n6aabeOCBB/D09KRXr16Eh4db/plq1GcWonLFxcXExsYydepUvLy8KjynlEIpZVFlrmHXrl34+vo6\nzsLEpZWXl3Ps2DFGjx7Niy++SJMmTS7ocpLPFOTn55OcnMzSpUt57bXXKC4uZs+ePVaX1bjPLAIC\nAjh79qzj8dmzZwkICLCwItdSVlZGbGwsw4YNY+DAgQD4+vqSnZ2Nv78/2dnZ+Pj4WFyltQ4ePMjO\nnTtJSUnh/PnzFBUVsWLFCgoLCykvL8fNzY2srCz5XGE/cw8MDKRTp04ADBo0iISEBPlM/c7evXtp\n2bKlox0GDhzIwYMHLf9MNeoziw4dOnDq1CnS09MpKytj27Zt9O/f3+qyXILWmmXLltGmTRvGjRvn\n2N6/f3+SkpIASEpKYsCAAVaV6BLuvPNOli1bxtKlS3nkkUfo3r07Dz/8MN26dWP79u0AbN68WT5X\n2LuYAgMDOXnyJGD/odi2bVv5TP1OUFAQhw8fpqSkBK21o52s/kw1+pvydu/ezT//+U8Mw+C6667j\n1ltvtbokl3DgwAHmzp1Lu3btHN0CkydPplOnTsTFxZGZmSmXOf7Ovn37+Pjjj5kzZw5nzpxh0aJF\n5Ofn0759e2bNmoWHh4fVJVru+PHjLFu2jLKyMlq2bEl0dDRaa/lM/c6aNWvYtm0bbm5uXHnllcyc\nOZOsrCxLP1ONPiyEEEJUrlF3QwkhhDBHwkIIIUSlJCyEEEJUSsJCCCFEpSQshBBCVErCQjRKjz76\nKPv27bPk2JmZmdxzzz0YhmHJ8YWoDrl0VjRqa9as4fTp0zz88MNOO8aDDz7IjBkz6Nmzp9OOIYSz\nyZmFEDVQXl5udQlC1Ak5sxCN0oMPPsif/vQnXnrpJcA+XXZISAgLFy6ksLCQf/7zn6SkpKCU4rrr\nrmPSpEnYbDY2b97Mhg0b6NChA1u2bGH06NEMHz6c1157jRMnTqCUolevXtx77700b96cJUuW8OWX\nX+Lu7o7NZuO2224jIiKChx56iHfeeccxz8/y5cs5cOAA3t7e3HzzzY41l9esWUNaWhqenp7s2LGD\noKAgHnzwQTp06ABAQkICn376KUVFRfj7+3PffffRo0cPy9pVNFyNeiJB0bh5eHhwyy23XNANtXTp\nUnx9fVm8eDElJSXExMQQGBjIqFGjADh8+DCDBw9m+fLllJeXk5WVxS233MLVV19NUVERsbGxvPfe\ne0ydOpVZs2Zx4MCBCt1Q6enpFep45ZVXCAsL47XXXuPkyZPMmzePkJAQunfvDthntn3ssceIjo7m\n3Xff5c0332T+/PmcPHmS9evX88ILLxAQEEB6erqMgwinkW4oIX4jJyeHlJQUpk6dStOmTfH19WXs\n2LFs27bN8Rp/f39uvPFG3Nzc8PT0JCQkhJ49e+Lh4YGPjw9jx45l//79po6XmZnJgQMHuOuuu/D0\n9OTKK69k5MiRjon1ALp06ULfvn2x2Wxce+21HD9+HACbzUZpaSlpaWmOuZZCQkJqtT2E+IWcWQjx\nG5mZmZSXlzN9+nTHNq11hUWygoKCKuyTk5PDihUr+P777ykuLsYwDNMT4WVnZ+Pt7U2zZs0qvP8P\nP/zgeOzr6+v42tPTk9LSUsrLywkJCWHq1Km89957pKWl0atXL6ZMmSLToQunkLAQjdrvF9oJDAzE\n3d2dN954w7EqWWXeeecdAGJjY/H29mbHjh28+eabpvb19/cnPz+foqIiR2BkZmaa/oE/dOhQhg4d\nSmFhIf/4xz/417/+xaxZs0ztK0RVSDeUaNR8fX3JyMhw9PX7+/vTq1cvVq5cSWFhIYZhcPr06ct2\nKxUVFdG0aVO8vLzIysri448/rvC8n5/fBeMUvwgKCuKqq67i3//+N+fPn+fEiRNs2rSJYcOGVVr7\nyZMn+e677ygtLcXT0xNPT89Gv8qccB4JC9GoRUREAHDvvffy1FNPAfDQQw9RVlbGo48+yrRp03j5\n5ZfJzs6+5HtMnDiRY8eO8cc//pEXXniBa665psLzEyZM4IMPPmDq1Kl89NFHF+z/5z//mYyMDGbM\nmMFLL73ExIkTTd2TUVpayr/+9S/uvfde7r//fs6dO8edd95ZlW9fCNPk0lkhhBCVkjMLIYQQlZKw\nEEIIUSkJCyGEEJWSsBBCCFEpCQshhBCVkrAQQghRKQkLIYQQlZKwEEIIUSkJCyGEEJX6/wG3Vkil\nyo892QAAAABJRU5ErkJggg==\n", 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NEoWoNuW2LAzDIDQ0lOzs7PO+RS2EqFm6sBD9xYfor1dCUDDGw8+iOnR2dlii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\nSElJqfC1tblpqrWGosJSy2+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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -713,7 +651,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.4.3" } }, "nbformat": 4, diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 99fecf3..8cc88b3 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -30,8 +30,10 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> + hid1 = tf.contrib.layers.fully_connected(self._observations, hidden_dim, activation_fn=tf.nn.tanh) + probs = tf.contrib.layers.fully_connected(hid1, out_dim, activation_fn=tf.nn.softmax) # <<<<<<<< - + # -------------------------------------------------- # This operation (variable) is used when choosing action during data sampling phase # Shape of probs: [1, n_actions] @@ -55,11 +57,13 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): # 3. Gather the probability of action at each timestep # e.g., tf.reshape(probs, [-1]) == [0.1, 0.9, 0.8, 0.2] # since action_idxs_flattened == [1, 2], we'll get [0.9, 0.8], which is the probability when we choose each action + probs_vec = tf.gather(tf.reshape(probs, [-1]), action_idxs_flattened) + # probs_vec = tf.dynamic_partition(tf.reshape(probs, [-1]), action_idxs_flattened, 1) # Add 1e-8 to `probs_vec` so as to prevent log(0) error log_prob = tf.log(probs_vec + 1e-8) - + """ Problem 2: @@ -72,6 +76,8 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 1~3 lines. """ # YOUR CODE HERE >>>>>> + surr_loss = tf.reduce_mean(tf.multiply(log_prob, self._advantages)) + surr_loss = -1.0 * surr_loss # <<<<<<<< grads_and_vars = self._opt.compute_gradients(surr_loss) diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..f85678c 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -31,7 +31,15 @@ def discount_bootstrap(x, discount_rate, b): (the shape of it should be the same as the `x` and `b`) Sample code should be about 3 lines """ - # YOUR CODE >>>>>>>>>>>>>>>>>>> + # YOUR CODE >>>>>>>>>>>>>>>>>>> + y = x - b + # left shift + tmp = np.copy(b) + tmp[:-1] = b[1:] + tmp[-1] = 0.0 + + y += discount_rate * tmp + return y # <<<<<<<<<<<<<<<<<<<<<<<<<<<< def plot_curve(data, key, filename=None):