diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..63bc78b 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -28,17 +28,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -77,7 +71,7 @@ "source": [ "## Problem 2: compute the surrogate loss\n", "\n", - "If there are $N$ episodes in an iteration, then for $i$ th episode we define $R_t^i = \\sum_{{t^′}=t}^T \\gamma^{{t^′}-t}r(s_{t^′}, a_{t^′})$ as the accumulated discounted rewards from timestep $t$ to the end of that episode, where $\\gamma$ is the discount rate.\n", + "If there are $N$ episodes in an iteration, then for $i$-th episode we define $R_t^i = \\sum_{{t^′}=t}^T \\gamma^{{t^′}-t}r(s_{t^′}, a_{t^′})$ as the accumulated discounted rewards from timestep $t$ to the end of that episode, where $\\gamma$ is the discount rate.\n", "\n", "The pseudocode for the REINFORCE algorithm is as below:\n", "\n", @@ -103,14 +97,16 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 3, + "metadata": { + "collapsed": false + }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/andrew/miniconda2/envs/cedl/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + "C:\\Users\\User\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py:93: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] } @@ -152,7 +148,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -214,6 +210,7 @@ " 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 +255,97 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": {}, + "execution_count": 5, + "metadata": { + "collapsed": false, + "scrolled": false + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 14.85\n", - "Iteration 2: Average Return = 15.59\n", - "Iteration 3: Average Return = 16.61\n", - "Iteration 4: Average Return = 17.43\n", - "Iteration 5: Average Return = 17.08\n", - "Iteration 6: Average Return = 17.24\n", - "Iteration 7: Average Return = 21.3\n", - "Iteration 8: Average Return = 21.42\n", - "Iteration 9: Average Return = 20.62\n", - "Iteration 10: Average Return = 26.82\n", - "Iteration 11: Average Return = 28.0\n", - "Iteration 12: Average Return = 28.41\n", - "Iteration 13: Average Return = 28.96\n", - "Iteration 14: Average Return = 28.15\n", - "Iteration 15: Average Return = 30.64\n", - "Iteration 16: Average Return = 36.2\n", - "Iteration 17: Average Return = 38.13\n", - "Iteration 18: Average Return = 34.5\n", - "Iteration 19: Average Return = 40.37\n", - "Iteration 20: Average Return = 35.78\n", - "Iteration 21: Average Return = 47.81\n", - "Iteration 22: Average Return = 47.21\n", - "Iteration 23: Average Return = 43.34\n", - "Iteration 24: Average Return = 46.1\n", - "Iteration 25: Average Return = 50.25\n", - "Iteration 26: Average Return = 51.02\n", - "Iteration 27: Average Return = 59.81\n", - "Iteration 28: Average Return = 57.49\n", - "Iteration 29: Average Return = 61.39\n", - "Iteration 30: Average Return = 62.26\n", - "Iteration 31: Average Return = 61.98\n", - "Iteration 32: Average Return = 62.16\n", - "Iteration 33: Average Return = 59.89\n", - "Iteration 34: Average Return = 73.46\n", - "Iteration 35: Average Return = 78.51\n", - "Iteration 36: Average Return = 72.79\n", - "Iteration 37: Average Return = 78.74\n", - "Iteration 38: Average Return = 86.95\n", - "Iteration 39: Average Return = 94.08\n", - "Iteration 40: Average Return = 97.58\n", - "Iteration 41: Average Return = 103.42\n", - "Iteration 42: Average Return = 101.17\n", - "Iteration 43: Average Return = 112.39\n", - "Iteration 44: Average Return = 115.09\n", - "Iteration 45: Average Return = 134.65\n", - "Iteration 46: Average Return = 138.92\n", - "Iteration 47: Average Return = 147.15\n", - "Iteration 48: Average Return = 152.35\n", - "Iteration 49: Average Return = 149.66\n", - "Iteration 50: Average Return = 148.15\n", - "Iteration 51: Average Return = 144.82\n", - "Iteration 52: Average Return = 144.43\n", - "Iteration 53: Average Return = 153.21\n", - "Iteration 54: Average Return = 163.66\n", - "Iteration 55: Average Return = 154.28\n", - "Iteration 56: Average Return = 155.07\n", - "Iteration 57: Average Return = 161.53\n", - "Iteration 58: Average Return = 166.28\n", - "Iteration 59: Average Return = 174.05\n", - "Iteration 60: Average Return = 172.8\n", - "Iteration 61: Average Return = 170.78\n", - "Iteration 62: Average Return = 179.58\n", - "Iteration 63: Average Return = 174.84\n", - "Iteration 64: Average Return = 175.74\n", - "Iteration 65: Average Return = 174.99\n", - "Iteration 66: Average Return = 187.7\n", - "Iteration 67: Average Return = 178.94\n", - "Iteration 68: Average Return = 182.74\n", - "Iteration 69: Average Return = 181.42\n", - "Iteration 70: Average Return = 182.19\n", - "Iteration 71: Average Return = 184.58\n", - "Iteration 72: Average Return = 181.9\n", - "Iteration 73: Average Return = 184.29\n", - "Iteration 74: Average Return = 188.8\n", - "Iteration 75: Average Return = 190.46\n", - "Iteration 76: Average Return = 188.89\n", - "Iteration 77: Average Return = 187.9\n", - "Iteration 78: Average Return = 190.19\n", - "Iteration 79: Average Return = 186.28\n", - "Iteration 80: Average Return = 189.1\n", - "Iteration 81: Average Return = 188.16\n", - "Iteration 82: Average Return = 191.32\n", - "Iteration 83: Average Return = 192.03\n", - "Iteration 84: Average Return = 195.45\n", - "Solve at 84 iterations, which equals 8400 episodes.\n" + "Iteration 1: Average Return = 19.2\n", + "Iteration 2: Average Return = 18.62\n", + "Iteration 3: Average Return = 20.08\n", + "Iteration 4: Average Return = 21.41\n", + "Iteration 5: Average Return = 24.01\n", + "Iteration 6: Average Return = 25.1\n", + "Iteration 7: Average Return = 24.68\n", + "Iteration 8: Average Return = 29.77\n", + "Iteration 9: Average Return = 31.82\n", + "Iteration 10: Average Return = 33.22\n", + "Iteration 11: Average Return = 36.52\n", + "Iteration 12: Average Return = 42.57\n", + "Iteration 13: Average Return = 38.15\n", + "Iteration 14: Average Return = 41.95\n", + "Iteration 15: Average Return = 43.51\n", + "Iteration 16: Average Return = 50.87\n", + "Iteration 17: Average Return = 46.3\n", + "Iteration 18: Average Return = 46.38\n", + "Iteration 19: Average Return = 47.59\n", + "Iteration 20: Average Return = 54.97\n", + "Iteration 21: Average Return = 51.19\n", + "Iteration 22: Average Return = 55.24\n", + "Iteration 23: Average Return = 57.17\n", + "Iteration 24: Average Return = 54.51\n", + "Iteration 25: Average Return = 60.59\n", + "Iteration 26: Average Return = 57.52\n", + "Iteration 27: Average Return = 57.49\n", + "Iteration 28: Average Return = 58.99\n", + "Iteration 29: Average Return = 58.89\n", + "Iteration 30: Average Return = 64.4\n", + "Iteration 31: Average Return = 63.81\n", + "Iteration 32: Average Return = 68.63\n", + "Iteration 33: Average Return = 62.66\n", + "Iteration 34: Average Return = 73.32\n", + "Iteration 35: Average Return = 69.84\n", + "Iteration 36: Average Return = 72.61\n", + "Iteration 37: Average Return = 70.97\n", + "Iteration 38: Average Return = 77.4\n", + "Iteration 39: Average Return = 74.35\n", + "Iteration 40: Average Return = 74.03\n", + "Iteration 41: Average Return = 81.34\n", + "Iteration 42: Average Return = 75.02\n", + "Iteration 43: Average Return = 82.62\n", + "Iteration 44: Average Return = 88.25\n", + "Iteration 45: Average Return = 90.06\n", + "Iteration 46: Average Return = 94.94\n", + "Iteration 47: Average Return = 92.22\n", + "Iteration 48: Average Return = 99.55\n", + "Iteration 49: Average Return = 100.59\n", + "Iteration 50: Average Return = 125.51\n", + "Iteration 51: Average Return = 123.23\n", + "Iteration 52: Average Return = 130.61\n", + "Iteration 53: Average Return = 143.77\n", + "Iteration 54: Average Return = 143.55\n", + "Iteration 55: Average Return = 151.62\n", + "Iteration 56: Average Return = 148.13\n", + "Iteration 57: Average Return = 147.5\n", + "Iteration 58: Average Return = 154.59\n", + "Iteration 59: Average Return = 156.22\n", + "Iteration 60: Average Return = 147.31\n", + "Iteration 61: Average Return = 150.37\n", + "Iteration 62: Average Return = 159.35\n", + "Iteration 63: Average Return = 163.6\n", + "Iteration 64: Average Return = 160.0\n", + "Iteration 65: Average Return = 168.16\n", + "Iteration 66: Average Return = 165.82\n", + "Iteration 67: Average Return = 171.68\n", + "Iteration 68: Average Return = 174.81\n", + "Iteration 69: Average Return = 177.5\n", + "Iteration 70: Average Return = 179.13\n", + "Iteration 71: Average Return = 183.7\n", + "Iteration 72: Average Return = 178.92\n", + "Iteration 73: Average Return = 183.77\n", + "Iteration 74: Average Return = 190.83\n", + "Iteration 75: Average Return = 193.7\n", + "Iteration 76: Average Return = 194.52\n", + "Iteration 77: Average Return = 191.22\n", + "Iteration 78: Average Return = 193.04\n", + "Iteration 79: Average Return = 193.97\n", + "Iteration 80: Average Return = 195.11\n", + "Solve at 80 iterations, which equals 8000 episodes.\n" ] } ], @@ -366,19 +362,21 @@ " discount_rate)\n", "\n", "# Train the policy optimizer\n", - "loss_list, avg_return_list = po.train()" + "loss_list_prob3, avg_return_list_prob3 = po.train()" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, + "execution_count": 6, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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KzJkzSUhICEiSUj5uIiVz/0ZixSmi+g3wu79AiomJISkpKdRhtDs9Z2TRc0aWzvKcEPhn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\n15du3boxcOBAxx+Z8OBZVfCIiIiI4dWfc14iIiIiVaSCR0R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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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hYViB/+2L0r0P/PgNdu+u0t1v/z5YvgTT7YLj7rsiVUdAjrCsXbuWKVOmsGXLFjIyMrjn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\nICCABx54gCFDhrB+/Xqg6bJKw1KPFi1aYDKZ+Om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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -396,8 +394,8 @@ } ], "source": [ - "util.plot_curve(loss_list, \"loss\")\n", - "util.plot_curve(avg_return_list, \"average return\")" + "util.plot_curve(loss_list_prob3, \"loss\")\n", + "util.plot_curve(avg_return_list_prob3, \"average return\")" ] }, { @@ -425,7 +423,123 @@ "\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": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 48.75\n", + "Iteration 2: Average Return = 49.12\n", + "Iteration 3: Average Return = 50.51\n", + "Iteration 4: Average Return = 51.15\n", + "Iteration 5: Average Return = 56.8\n", + "Iteration 6: Average Return = 56.0\n", + "Iteration 7: Average Return = 56.01\n", + "Iteration 8: Average Return = 65.28\n", + "Iteration 9: Average Return = 75.01\n", + "Iteration 10: Average Return = 81.49\n", + "Iteration 11: Average Return = 82.75\n", + "Iteration 12: Average Return = 87.08\n", + "Iteration 13: Average Return = 91.57\n", + "Iteration 14: Average Return = 97.05\n", + "Iteration 15: Average Return = 96.13\n", + "Iteration 16: Average Return = 102.2\n", + "Iteration 17: Average Return = 111.42\n", + "Iteration 18: Average Return = 114.7\n", + "Iteration 19: Average Return = 110.71\n", + "Iteration 20: Average Return = 113.3\n", + "Iteration 21: Average Return = 122.86\n", + "Iteration 22: Average Return = 124.25\n", + "Iteration 23: Average Return = 135.56\n", + "Iteration 24: Average Return = 143.98\n", + "Iteration 25: Average Return = 151.74\n", + "Iteration 26: Average Return = 145.77\n", + "Iteration 27: Average Return = 156.4\n", + "Iteration 28: Average Return = 166.11\n", + "Iteration 29: Average Return = 165.49\n", + "Iteration 30: Average Return = 164.04\n", + "Iteration 31: Average Return = 175.37\n", + "Iteration 32: Average Return = 170.62\n", + "Iteration 33: Average Return = 168.75\n", + "Iteration 34: Average Return = 173.3\n", + "Iteration 35: Average Return = 172.57\n", + "Iteration 36: Average Return = 171.75\n", + "Iteration 37: Average Return = 172.97\n", + "Iteration 38: Average Return = 177.62\n", + "Iteration 39: Average Return = 178.55\n", + "Iteration 40: Average Return = 173.01\n", + "Iteration 41: Average Return = 183.66\n", + "Iteration 42: Average Return = 177.99\n", + "Iteration 43: Average Return = 179.38\n", + "Iteration 44: Average Return = 183.55\n", + "Iteration 45: Average Return = 188.34\n", + "Iteration 46: Average Return = 186.56\n", + "Iteration 47: Average Return = 188.92\n", + "Iteration 48: Average Return = 193.57\n", + "Iteration 49: Average Return = 193.7\n", + "Iteration 50: Average Return = 192.98\n", + "Iteration 51: Average Return = 192.25\n", + "Iteration 52: Average Return = 195.97\n", + "Solve at 52 iterations, which equals 5200 episodes.\n" + ] + } + ], + "source": [ + "sess.run(tf.global_variables_initializer())\n", + "\n", + "n_iter = 200\n", + "n_episode = 100\n", + "path_length = 200\n", + "discount_rate = 0.99\n", + "baseline = None # 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_prob4, avg_return_list_prob4 = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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/JD+RmaK8PGhdGT4osrbDIhVaA1TVBt8+Oz3t5BmYYnHtA8lYJEdv98ytMwAr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\nEfjzrbfeCsAXv/hFNm3aZPu1c1pZORx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h+1Rm1e90nkI1L8Tx2S9C2y70q79L94rC0p5h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\nzYCePXv69wuUoGNE9cAET6ZANUWhz8jqh3Hz7I7fn2THPH6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T0Ri/1+rayTMs/tAgG5aGSA2Ld/QP2zeqm/caW82s42Cfm4X9aA0k9sB0743p3gu+1wvT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\nUMMjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list_prob4, \"loss\")\n", + "util.plot_curve(avg_return_list_prob4, \"average return\")" ] }, { @@ -446,13 +560,329 @@ "\n", "We use the one-step bootstrap for the advantage function, which change the $A_t^i=(R_t^i-V_t^i)$ in problem 3 into:\n", "\n", - "$$A_t^i = r_t^i + \\gamma*V_{t+1}^i - V_t^i$$\n", + "$$A_t^i = R_t^i + \\gamma*V_{t+1}^i - V_t^i$$\n", "\n", "Open **policy_gradient/util.py ** to implement the function `discount_bootstrap`\n", "\n", "If you answer is right, your will solve CartPole with roughly ~ 80 iterations." ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# set the hyperparameter for generalized advantage estimation (GAE)\n", + "LAMBDA = 0.98 # \\lambda\n", + "class PolicyOptimizer_actor_critic(PolicyOptimizer):\n", + " def __init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate=.99):\n", + " PolicyOptimizer.__init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate=.99)\n", + " \n", + " def process_paths(self, paths):\n", + " for p in paths:\n", + " if self.baseline != None:\n", + " b = self.baseline.predict(p)\n", + " b[-1] = 0 # terminal state\n", + " else:\n", + " b = 0\n", + " \n", + " \"\"\"\n", + " 1. Variable `b` is the reward predicted by our baseline\n", + " 2. Calculate the advantage function via one-step bootstrap\n", + " A(s, a) = [r(s,a,s') + \\gamma*v(s')] - v(s)\n", + " 3. `target_v` specifies the target of the baseline function\n", + " \"\"\"\n", + " r = util.discount_bootstrap(p[\"rewards\"], self.discount_rate, b)\n", + " target_v = util.discount_cumsum(p[\"rewards\"], self.discount_rate)\n", + " a = r - b\n", + " \n", + " p[\"returns\"] = target_v\n", + " p[\"baselines\"] = b\n", + " p[\"advantages\"] = (a - a.mean()) / (a.std() + 1e-8) # normalize\n", + "\n", + " obs = np.concatenate([ p[\"observations\"] for p in paths ])\n", + " actions = np.concatenate([ p[\"actions\"] for p in paths ])\n", + " rewards = np.concatenate([ p[\"rewards\"] for p in paths ])\n", + " advantages = np.concatenate([ p[\"advantages\"] for p in paths ])\n", + "\n", + " return dict(\n", + " observations=obs,\n", + " actions=actions,\n", + " rewards=rewards,\n", + " advantages=advantages,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 17.4\n", + "Iteration 2: Average Return = 16.72\n", + "Iteration 3: Average Return = 17.63\n", + "Iteration 4: Average Return = 20.45\n", + "Iteration 5: Average Return = 21.51\n", + "Iteration 6: Average Return = 21.28\n", + "Iteration 7: Average Return = 24.3\n", + "Iteration 8: Average Return = 26.07\n", + "Iteration 9: Average Return = 26.29\n", + "Iteration 10: Average Return = 28.86\n", + "Iteration 11: Average Return = 34.23\n", + "Iteration 12: Average Return = 32.87\n", + "Iteration 13: Average Return = 36.42\n", + "Iteration 14: Average Return = 32.58\n", + "Iteration 15: Average Return = 36.42\n", + "Iteration 16: Average Return = 37.85\n", + "Iteration 17: Average Return = 39.25\n", + "Iteration 18: Average Return = 43.24\n", + "Iteration 19: Average Return = 44.48\n", + "Iteration 20: Average Return = 44.31\n", + "Iteration 21: Average Return = 47.44\n", + "Iteration 22: Average Return = 47.87\n", + "Iteration 23: Average Return = 43.54\n", + "Iteration 24: Average Return = 46.81\n", + "Iteration 25: Average Return = 48.73\n", + "Iteration 26: Average Return = 46.36\n", + "Iteration 27: Average Return = 49.62\n", + "Iteration 28: Average Return = 49.19\n", + "Iteration 29: Average Return = 50.57\n", + "Iteration 30: Average Return = 45.5\n", + "Iteration 31: Average Return = 52.32\n", + "Iteration 32: Average Return = 52.75\n", + "Iteration 33: Average Return = 52.41\n", + "Iteration 34: Average Return = 52.12\n", + "Iteration 35: Average Return = 52.78\n", + "Iteration 36: Average Return = 49.62\n", + "Iteration 37: Average Return = 54.36\n", + "Iteration 38: Average Return = 50.55\n", + "Iteration 39: Average Return = 48.8\n", + "Iteration 40: Average Return = 54.0\n", + "Iteration 41: Average Return = 53.77\n", + "Iteration 42: Average Return = 52.03\n", + "Iteration 43: Average Return = 56.8\n", + "Iteration 44: Average Return = 54.62\n", + "Iteration 45: Average Return = 50.51\n", + "Iteration 46: Average Return = 51.9\n", + "Iteration 47: Average Return = 54.78\n", + "Iteration 48: Average Return = 54.79\n", + "Iteration 49: Average Return = 55.82\n", + "Iteration 50: Average Return = 54.26\n", + "Iteration 51: Average Return = 56.82\n", + "Iteration 52: Average Return = 58.9\n", + "Iteration 53: Average Return = 55.24\n", + "Iteration 54: Average Return = 60.87\n", + "Iteration 55: Average Return = 58.89\n", + "Iteration 56: Average Return = 58.17\n", + "Iteration 57: Average Return = 62.33\n", + "Iteration 58: Average Return = 61.14\n", + "Iteration 59: Average Return = 62.89\n", + "Iteration 60: Average Return = 57.02\n", + "Iteration 61: Average Return = 62.08\n", + "Iteration 62: Average Return = 63.31\n", + "Iteration 63: Average Return = 62.27\n", + "Iteration 64: Average Return = 60.78\n", + "Iteration 65: Average Return = 63.51\n", + "Iteration 66: Average Return = 67.65\n", + "Iteration 67: Average Return = 63.19\n", + "Iteration 68: Average Return = 62.74\n", + "Iteration 69: Average Return = 65.91\n", + "Iteration 70: Average Return = 61.86\n", + "Iteration 71: Average Return = 70.65\n", + "Iteration 72: Average Return = 73.31\n", + "Iteration 73: Average Return = 71.31\n", + "Iteration 74: Average Return = 71.21\n", + "Iteration 75: Average Return = 78.19\n", + "Iteration 76: Average Return = 79.41\n", + "Iteration 77: Average Return = 76.82\n", + "Iteration 78: Average Return = 83.41\n", + "Iteration 79: Average Return = 82.68\n", + "Iteration 80: Average Return = 75.77\n", + "Iteration 81: Average Return = 81.88\n", + "Iteration 82: Average Return = 87.18\n", + "Iteration 83: Average Return = 92.64\n", + "Iteration 84: Average Return = 91.55\n", + "Iteration 85: Average Return = 96.9\n", + "Iteration 86: Average Return = 100.59\n", + "Iteration 87: Average Return = 110.11\n", + "Iteration 88: Average Return = 111.52\n", + "Iteration 89: Average Return = 98.33\n", + "Iteration 90: Average Return = 103.59\n", + "Iteration 91: Average Return = 106.67\n", + "Iteration 92: Average Return = 106.3\n", + "Iteration 93: Average Return = 115.27\n", + "Iteration 94: Average Return = 111.88\n", + "Iteration 95: Average Return = 113.12\n", + "Iteration 96: Average Return = 108.56\n", + "Iteration 97: Average Return = 114.47\n", + "Iteration 98: Average Return = 108.1\n", + "Iteration 99: Average Return = 112.82\n", + "Iteration 100: Average Return = 110.29\n", + "Iteration 101: Average Return = 112.22\n", + "Iteration 102: Average Return = 112.48\n", + "Iteration 103: Average Return = 118.69\n", + "Iteration 104: Average Return = 118.88\n", + "Iteration 105: Average Return = 122.68\n", + "Iteration 106: Average Return = 119.02\n", + "Iteration 107: Average Return = 119.92\n", + "Iteration 108: Average Return = 120.28\n", + "Iteration 109: Average Return = 121.82\n", + "Iteration 110: Average Return = 117.23\n", + "Iteration 111: Average Return = 125.83\n", + "Iteration 112: Average Return = 123.92\n", + "Iteration 113: Average Return = 134.18\n", + "Iteration 114: Average Return = 129.51\n", + "Iteration 115: Average Return = 131.16\n", + "Iteration 116: Average Return = 134.89\n", + "Iteration 117: Average Return = 132.72\n", + "Iteration 118: Average Return = 126.77\n", + "Iteration 119: Average Return = 130.98\n", + "Iteration 120: Average Return = 120.7\n", + "Iteration 121: Average Return = 128.64\n", + "Iteration 122: Average Return = 136.51\n", + "Iteration 123: Average Return = 139.06\n", + "Iteration 124: Average Return = 139.46\n", + "Iteration 125: Average Return = 145.84\n", + "Iteration 126: Average Return = 136.6\n", + "Iteration 127: Average Return = 142.07\n", + "Iteration 128: Average Return = 142.62\n", + "Iteration 129: Average Return = 133.42\n", + "Iteration 130: Average Return = 143.88\n", + "Iteration 131: Average Return = 140.7\n", + "Iteration 132: Average Return = 141.02\n", + "Iteration 133: Average Return = 139.44\n", + "Iteration 134: Average Return = 143.04\n", + "Iteration 135: Average Return = 145.96\n", + "Iteration 136: Average Return = 150.18\n", + "Iteration 137: Average Return = 147.6\n", + "Iteration 138: Average Return = 149.82\n", + "Iteration 139: Average Return = 148.78\n", + "Iteration 140: Average Return = 146.21\n", + "Iteration 141: Average Return = 145.4\n", + "Iteration 142: Average Return = 152.83\n", + "Iteration 143: Average Return = 143.08\n", + "Iteration 144: Average Return = 150.78\n", + "Iteration 145: Average Return = 150.79\n", + "Iteration 146: Average Return = 150.56\n", + "Iteration 147: Average Return = 152.92\n", + "Iteration 148: Average Return = 159.57\n", + "Iteration 149: Average Return = 152.22\n", + "Iteration 150: Average Return = 154.09\n", + "Iteration 151: Average Return = 153.32\n", + "Iteration 152: Average Return = 150.93\n", + "Iteration 153: Average Return = 154.16\n", + "Iteration 154: Average Return = 152.32\n", + "Iteration 155: Average Return = 141.32\n", + "Iteration 156: Average Return = 151.45\n", + "Iteration 157: Average Return = 148.25\n", + "Iteration 158: Average Return = 151.34\n", + "Iteration 159: Average Return = 154.2\n", + "Iteration 160: Average Return = 159.37\n", + "Iteration 161: Average Return = 153.73\n", + "Iteration 162: Average Return = 156.98\n", + "Iteration 163: Average Return = 155.68\n", + "Iteration 164: Average Return = 151.25\n", + "Iteration 165: Average Return = 155.33\n", + "Iteration 166: Average Return = 155.38\n", + "Iteration 167: Average Return = 158.18\n", + "Iteration 168: Average Return = 150.1\n", + "Iteration 169: Average Return = 160.55\n", + "Iteration 170: Average Return = 162.56\n", + "Iteration 171: Average Return = 165.5\n", + "Iteration 172: Average Return = 161.18\n", + "Iteration 173: Average Return = 161.49\n", + "Iteration 174: Average Return = 157.17\n", + "Iteration 175: Average Return = 161.49\n", + "Iteration 176: Average Return = 153.74\n", + "Iteration 177: Average Return = 150.64\n", + "Iteration 178: Average Return = 148.93\n", + "Iteration 179: Average Return = 157.15\n", + "Iteration 180: Average Return = 147.42\n", + "Iteration 181: Average Return = 156.23\n", + "Iteration 182: Average Return = 159.33\n", + "Iteration 183: Average Return = 164.4\n", + "Iteration 184: Average Return = 160.96\n", + "Iteration 185: Average Return = 162.56\n", + "Iteration 186: Average Return = 166.75\n", + "Iteration 187: Average Return = 167.53\n", + "Iteration 188: Average Return = 165.14\n", + "Iteration 189: Average Return = 168.25\n", + "Iteration 190: Average Return = 162.55\n", + "Iteration 191: Average Return = 156.87\n", + "Iteration 192: Average Return = 164.96\n", + "Iteration 193: Average Return = 169.11\n", + "Iteration 194: Average Return = 161.3\n", + "Iteration 195: Average Return = 158.28\n", + "Iteration 196: Average Return = 158.12\n", + "Iteration 197: Average Return = 162.84\n", + "Iteration 198: Average Return = 160.8\n", + "Iteration 199: Average Return = 158.64\n", + "Iteration 200: Average Return = 158.91\n" + ] + } + ], + "source": [ + "sess.run(tf.global_variables_initializer())\n", + "\n", + "n_iter = 200\n", + "n_episode = 100\n", + "path_length = 200\n", + "discount_rate = 0.99\n", + "# 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_prob5, avg_return_list_prob5 = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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HOQ4NLU34PKoVd5Ox3MBDRXB064qmpxGclhAgE1MhU1TrVnQubQgUh8CxzZdq\nKSZjwBA4LXukBxCPCVMmDnj7cBp1IJMFS6XFwM9G3Zo1RAwn0vsRqcnYWUUVLILDOQcaDbB0AP9G\nWFoty9RqiHn+4nPgR59E7Oq3R/96YWk0xGdW3KTAKZesSJW80OllJZUyGyr2nt+1RskEJR9iHpUU\nphTBGWHSGXECG/UIjoxiOKI0XPHghDJa6y1TIPTSoM21phA4k9PheuE4TcaA3SvQbLTPl5ImYwCI\nJ909OImAwzbV1/IyYcooknx+P0MyMRz0qA8Os5mMc8EiOI89BP2DN4MHnZ1mwJcXof/w3g5r0sT+\nn0qZ2y1/+D7wf/xSqNfqGc0GMLtDNPvbxAUYr5SsSLBMyfcy0lqvmp4Yds55YNu2h3o4G5sIbjI2\np9STyXhkYYyN/rgGTbOiGFFVUbWUKiq9l9PEhehgE9PgASM4XNNEtEqmH+UVii1tVG8fv9AWwdHs\nkR71vp2oV62rbq8DolGqbn7+lKIafvpiMg4YwVldFukK2RMqIPzH94P/3afA/fZro2eUGINirGV9\nBajXoo1edQHnXHxm23eK48e6+8Ur1zTwnz7m/2S2CE4fUlSbnQ2VHwsewZHfG0VwRpzxSWCExzXw\nlmUka6sA6LKKiustICHLxHvcByeREENRg5qMjYo488rL02QsIzgJS+BIg3FC9MbhaqQHMIdtcs79\n11CrirQa4G3ClCbjNAmcUYH3ymScdDT607TOkRmZLjv+bLjXk9FMOeTRDeNigGWyVgRHRlhXN9k9\neLMYFyBs+07xu4fRmP/4Aeif+CNwv4vbimIyNlNUvdlPLdPvJiIq+XFgI2QEh6qoRpzxSf+NfNjR\nFJ+NSwSHJZKiaV+oPji6OOgy1vMqKiZTVEH74MiSf0eKyjYYsFG3Qs4y7aRWm8UTZqM/5kxRGevy\ng9dqYs2AvwcnlbYM4FuxnwgRjl40+ms2LeENo4oK6BzFkQLohZACR0Yo/S4opCE/o0ZwjP1zs+MR\nNov8DrYbpdZeRuPTJ8TPqo+Qq6xbEZxkjyM40vS7GcEx1o0Hh1JUIw0b8QiOmLHkEW3RmsaAyXj4\nFFUsBsRCPi4sMqoyMQ1UK9boBD+kn0qajJMpIcTUxzo8ONxs9GcImHhCVJypqSzAupLulKaqWxEc\n7uPBMTsZy9+J4aYXAqflMBnLqppOr7GZCA7gL1QM8z9TB9kaqSAecjBu5BjrZ9MzYt/2iuCcEbOe\nvIQib9Q36X//AAAgAElEQVSFmBlzpqh6tJ9GYfrNTwCNuhVJ9IE3yGR8djA+4h6clgaWTIq0S1uK\nShkwGbJMnMmmeL1u9JdIgMloSICrQy6vYIwmjiwWM8YhGNUeum4IHNWDY1RRmREctYrKiuAwKXA6\npQdqVZEiY8w7RaU2+pO/E8ON0fkajUZ05nutKZpRSsJGcE69AB7GwC5LrH0jOE0jRZUTvhtdty4S\nVwcscMz5cSnRQ8srgiOndXt9jsZxhOXtJuMg4sEPXqui9X/eDO6MrEVg+mVhxjXI9x2kS3IEkMAZ\nFKM+cFNWIsmohIrZ3K6bCE7cSuX0Crk+KXCCVFKV18Rj1P4R6jwqeeA3B2gmxEFR1y2TseHBsaWy\nAOtKOoDAQTbn2aVYlPDaBU6okxCxNWk2LO9VVFGcZtOKLAJWK4FOPp9mQ+wHug689Hzw15PRSb+U\nsOLB4fWaOKFKU/KgU1Ry/ckkMD3j6sHheguYPyV+qXoIHHXQJhBdimp9FVhZAn/+GfvtUZh+ZTot\nSJrK7LnTH+lBAmdQjE2K9EeQ6phhRBp13USM9J3IqqGg6C0gHhP/eunBkeubCB7BkSXijCmdjdJK\nKN0wCTJ1FpUUIdLrICNaTpNxIkSKKpM1IkcuwkUeJFNpIbDiCYrgjAKqwInKaKxW9wFWBKfeIYKj\nNYHd5wKJRLg0VRAPjqxulPuVvPBIJIGBp6iMfSuZApveBr6y2H6f5UUrUuUlRKWXzxANTB5DN7uf\nyuNQccH99k15cIxoU5AITh8HbQIkcAYGM8c1jEaaiq8ugx9+xLpBnZLtViZupqhCmozNCE4Pq6jk\n+vJjAIuBB4m0ldesqy6JWs6qhrABcaUnw7VuERxnoz8gWATHb1K4sQazCRvNoxoNeiFwtKa9iipr\nmOaDRHAyOWDPecALz/jf1/Z6xv7sdzGhaUA8aZiMFYGze2/gdg49Q+6biZRnBMdMTwHeKaoNw3ys\nHkuSqc1XUcnXW3YIryhMv2NhIjhVEjhnBePGAWlE0lT8h/8K/a//zLrBHCLpIkZsKaoQQkU2xeuL\nyTip+GgCVBopc6hMMkqKSomeABBixhA/zKWKKmwEh3MuDmKZrDiAuAkXU2QZz00TxUeDZgOs1ykq\nWXXXwYPDmw0gmQTbd36oCA43PTgdUlTxuOXBMe7Lzt3f8xQVXzjlX2wgIzjSg7O63NbTh595Sfzd\np6cQL5dEIUVW6SacSm8+RWW8Hl+2R3B4FCmqbA5gLFizv8323AkJCZxBMWoDN5t1e1pEpqhcTcYy\nRRU2giM9OP0QOIboCCgCuDqHSpLOWNVM8jmktyaetMSPkqLirWabyThQikprigiXFDg+ERzzuYOK\nN2Jr02hYF0wRRHA452IfUEzGLBYzTsydIjhNEXHYez5w5iV7mwQ/gkZwErKKqirum84Aszt7mqLi\negv6rb8H/pXPed+paXlwWGFWrHXpjP0+Z06KWU/ZvPfnWBYl4rZUdzIVelSD/pXPQf/mV6wb6h4R\nHLn/b8L0y2JxMeWdUlSEiTKPaiQwKoLMKg4jCuKZotrKERx1knIqHWxysTqHSpLOWI9tOiI4yYR1\ncDEFTlzcxnV7J2N5ovE7yBknEiY9OK4RHMfBjFJUQ4/ZQVe2B4giRdVqAZzby8SBYN2Mmw2wZAps\n3/niOV58LthrNoN4cByN/tZXxfuenAY2ArZz6IYTx4GNMvgD3wWXJmEnMoWUTAEXvRLIj4N/7y7b\nXfiZl8B27PH/HCsla96WpIv9lB9/xhZBM4XmWtHu+zRSRps2/ebHLf+Q37rq1b71wAFI4AwMJr0S\noyJwpFCRO6KfkVj154T24MTEc/Zj2CZgiJQAV0/qHCoDplZROT04iaT1uchoUSJhXoHbhhXKfkJ+\nERx5AEtn7ZEjlbozReWRyiKGByl682NiO4pC4MjtTGn0B0B4a7yqf8z1GCMeZuYAILg3RmuK9gbl\nknfhRcvR6M8QOGyqIP7eozQVf+aIeE8T0+D/dIf7fRQPDktnwH7+reD33W1P25x5Cdix21/gqGMa\nJEYLgFA0G/ZtQb4e53Z/UFQpo4kpz/EUNur1vnUxBkjgDJbxESoV1xwCp+WXomp1V0XVUlNUvTQZ\nNx0RHH8RwDk3IjgOD47NZOz04ChXx8b/WTwBbFTEbcmQjf7k6xiTwv09OBlrLSRwhhtD4LBUKvhA\nzE4Y+xZLukRwOlVRNY2ePImQ5c1aU2nL0H5MFJEqQ+Cks2JsxOqyEcGRAqc3aSp+9Elg/wVgb/tV\n8B/dB/7S8fY7mVVU4rjBfv46gHPwf/2WeI6NshBkc4bA8RCivFJqu1BCKh3eZNxs2rcF9YKnuGi/\nPQKBwyangzVbrFfFhV+fIIEzSEZS4Bg7ki1K4+xkbJiM3cSPH3pL8e70cNimOj4hiE+luiHeo4sH\nRz7WDJ+rZeISlwiOmwfHdw5QzRI4SPtXUUGpoupZWJ/oD0p5MrK5aCI4cjuLOwROkOc3qq9YIiGi\nrUG9I1oTkFOs3SIx8jiRSIAZFV1YPCPM1VMh2jmEhHMOPPNTsJe9HOzyq4GZObu3RWKMtmAxsS+z\niSmw1/9H8Hu+IYzXRgUV27kHyOYsc6+TSgnMGcFJpcLvp416ewTHiHTZjMZRpYwmp4P1C6vXKEV1\n1jA+OToeHNnMz5aiSoqTt+LBMQ2MXhVWfkiTcSzWswgO13WRCotbEZyOBxfnoE1Jpr0Pjq2KSqJO\nE3cTOEE6GdeUaoiUu8mYO0zGaqRHf+jfwE++6P38xNakoQicTEQCx2xa50hRZfOdK2VkBEeuSQso\ncJpNYc4F3H04UuDIFBUgDLMTU8LgmkiC92Lg5pmXgPI62MFXgCUSYK//j8BTh9sH36pdyg3YNb8E\nlNfBv/Bp8ONGyfzcbhGB8vqeyqX2dhPdpqjUNFi9KnqvjU/ajca1WjQpo4mpYAKTqqjOHtjEZDDV\nOwxIEVNXBI5bBEeWTnbVyVg3Gv0leufBMX0xRqg5SATHOYdKks5aHpy1VSCbE1e2gCOCIwVO0koB\nqAdL+XffFJVs2OVXJl4TPgf52sZ7462WOAj/4B7/90kMDF5cQusjvw8uU5gSs3onBeQiSlHJfdlh\nMmbT2zynZNvWIwV5MhkuRTU9I/pOuUZwjFlPCUXgcF14cBgzBuNGn6LizzwpLqj2Xyhe/5zzhBHY\n+Tk0G+2f19wusBs/AP6Th0QFVmFGpGeyOR+T8XqbB4el0qGrqNBstkdwMlkRJVMiODyqqqbJaSHm\nOo0KqUckqAJCAmeQeDWEGkYUDw7XW+LgYwgcrgocTTHWOv/WiX6UiZsHd9Vk3CmCY59DZZIWFVic\nc2DhlDVpGHBPUcmfgH0WlRQlfn1wbCZjHw9OKmOVoMoIzqkXxc8hTFfxShn6D+8d9DJ6z+kTwLGn\n7f4JwF69k8lFU0XlZTIuzIh2/87ohW09agQn3bk5pfmamtgexyc6RHDithSH2f9nqtCbeVRHnwTO\nPWCJqnPOEz9POMZQqO9bIXbZGxH70CeBCy8Bu/g14kYPkzHXdaBSaY/gpFLh981mHdCaZlqb16pC\nWGybBbd5cKJJUTHpg+p0wV6vWh7APkACZ5BMzxhlez00zPYJrlZRaUoUxOmzUSMk3cyi6mYKeQjk\nd8GUFJXr2AP1MWUjguPWyZhzMQRx4RTY9p3W35IuERw1baXOogKMXhgdUlTJlGga6JGiQt3RIdkQ\nb/z5p42/D19PHP74w+B/+4lNDyPc8jj7KUkaVoM5FnmKyuHBmZ4V63BGkVScEZyg5lhjkCYmpt3n\nUZnHlKQlNgBrnMrkdE+6GXPDf2NSmAVyeXDnnC3Z/8cFNrsD8d/9MGL/+TfFDRmPCE61AnDdGrQp\nSYZr9Md13fq8ZESvXgNLZ8EcERxxexQRHENo+pT5c84pgnM2wQqz4gQ46DkqUWDsUFwROOYcFVXA\nGcKEeRmQ/TA9OHHwXkVwWs4IToBKo/I6kM2DOUPU8sBRrwLzp4E5K4LD1KtjdRaVxNl4K5HoXCYu\nD/yGKGu70nY2EJTi7dhRAAAP0u9nqyE/kxEXOOZ349wWNcWD048UFQC4zVlS15NQPDiBIziGMJqc\ncp8oLr9n1WQMmP1/WA9SVHx5ASgugh18hXkbY0yMoXCN4DgEoRdGw8S2/bPsGLQpSYVs9Kd+5lLw\nyghOYTtQXLS6LEfmwZFGb58ITqMhzndkMj5LKMyIn87uksOIFAb1mi1Kw5wiRpqR43ERbei2k3GP\nPDjcGZ4PUkpdcplDBVi57fUVcfBVU1TqwVA1GUschkUk/VNU5qBN+bpcb7+/cwSE8d7480eN5xi+\nFJW5bY3q0FqJ/G68Iji9MBknXFJUgGdanbdaYn9WTcZBT8xG/xw2Me1fRRWPC5OuRKaoJgvRV1HN\nG7Ojdu+z3czOOQ/cKXA07whOG5mseD/ObdYxaNMkbJm4et+aKnByYNtmhICVqaSoqqgmJg3/lI/I\nlKNphtlkzDnHkSNH8JOf/ATVTk2hznaMAwZfWRrwQiJA7qxqisqtD45SDRE6gmOajHvYB0f1CAHB\nOhm79cABTMHBXxQHQ1uKys1kLE8oqhHYvH+Hk0XNOlCZTQLbToY1u8BJp8XjTp8wJhYPYQRHbgfN\nYNuD/uW/gv4NlzLfrY5HioqrJuOoysRNgePYBicL4iRW9DheOVNbyWQggWN2Y04kvcuN1YumtNXH\nyUxXTRUMk2v3QpfXqvbIsEzFOQXHOecBi6ftYyg8PDhumBEoZ5rKbdAmYFRRhRE4agTHeA1Znl0w\nSvGlDyeqPjixuLd/SiK34T4KnETnu3jz5S9/GUePHsWHPvQhAGJD/eM//mMcOXIEADAzM4M//MM/\nxI4dOza/0hGEZXJALt9uHBxG1EZ/bT4bNYKjiB9n+qoTRgSHxRNtg+yigjvD8wFMxtyt+6h8LACc\nOCZ+epmME44UVSptn0UDGBEcn89KhqABy8RXr9nX1ajbDy6ptIj0AMB5FwxnBEcLF8HhLzxrzW0a\nJoxtkDfqsG0ZqsnYEDic8/btJwxeKap4XAgJL4Ejmw4qJuNA3qiW8nqToty47T0oF03CZ5ayojcw\nGs0BQhzJcvOQ6H/ye2BvuAbs6l8EYDTnY8yKjMrXOuc8IcpOvgAcENVVPEyKSkZMqhu2CyP+2ENi\nf3UWK6RClol7RnAyZq8hvrwItv+CaD0xXv4piVrp2Sc2FcF56KGHcODAAfP3Bx98EEeOHMGv/dqv\n4YMf/CB0Xcc//MM/bHqRI830jPcBY5gwU1SOCI5zFpVaDRFiVAPXdZG/jfe2D45VQaJEcDTN3/NT\nrYDl8u23G2KCn3heCFn1ykwN/6vTxAH3K8FE0tfPwOs1e4oKaBMs3JGiYvL/mSzY3gNDaTI2t5+g\nV+7NZuduvFsRLw9OowHEYsLTlsmJ/WKz6TqvFBVgVFJ5XJA1HZGfoGXiauRnYlq8R+d35GjfgFTG\n6nwMKN2Mu0tT8WZDRDKXFAPuRkW0dnDOadp5LhCP29NUjeARHGRz4qcSweGnT4Df9y9gb7vBaiUh\nMcrEfavXVJTPnFeNKJRMYefy4mdxQRxTo2y85+WfkgwggrMpgVMsFm3RmYceegh79uzBO97xDrz6\n1a/Gm970Jvz0pz/d9CJHmsLsiKSovCI4jjSU5hPd8UN6bmJdTCEPAXeajGU0xC+KU92wDloqagRn\n+y77FalrHxzF9+OkU7i/VrW8CfLxzpST04Mj17fvZeKgN4wpqpARHDQbQ1kO7+nB0awTK5Pb4CbT\nVNwrRQWATW0D92ptYXZVNkaPpNLBvpemLEpICrMw0B4JcGvfoEbicsaAykq58+u5ISuL1EaGGxUx\n+dsBSyaBHXvsRmOtaUWuOiEvRBRDuP61L4g+OVe+tf3+ybS4uAsj4iXVqvg+NQ1IZ8UxaNt2IeTM\nzubRCA7WyQdVV1pZ9IlNCZx4PA5NVs8Y3ptXvvKV5t+npqawvt55wujZDCvM2EzG/OnDIjQ6bKgC\nRw1xO8VIm/gJKFTkaAY5bLPXfXDURn9AlwLH2JE3KmBqegqwTh7xuCV8TFHlEcHpWEVlHKikB8cZ\nkanXrPcDWB2Nz3uZMVZiCE/80rQetFqnUR/OSJVfmbg8sUYkcNrEhErBJ+Ks+oEAI+oYIoJjDLQE\nAKw6RJQzgpMfA5suWH83Iqhd9wFamhePVwVStdI+2duAnXOevVTcpdGfJxl7BIc/81PgsYfA3vGf\n2+d/wZgzBgRPUzlTVMbrMOP4wM7dLxoYGtsUizCC4ytwZNPTYSkTP+ecc3DfffehXC7j3nvvRalU\nwqtf/Wrz74uLi5iYmPB5BgKFWcCI4PDaBvQ//0PwYWxcJg9S9Zo9xJ1wVlGpJuPwERwW73UVleNA\n6iUWVGob1kFLRfqMAGBuZ/vfAHvllJoWa3uupL+Bsl6z1uAVdXIrEwfAzrtAPGYYIxthU1RaczgF\nTsMjgtN0ETibLRXXmiLtFYu3/82v2V+byTigd0Q9XszuAMYmwJ/4kf0+DoETu/m/gr31Buvv6Yzw\ny1R9evT4wA2BA/XislJ2jeAAEEbjky+IpqaAbx+cNmTxgSHG+L13Abv3gr3259zvL583aCWV/Mzl\n6Ben9+XQpcI/dPqEcXuEHpz1Fc9UGpfrGJZGf9dffz2OHz+O97znPbjttttw4YUX4uKLLzb//uij\nj9o8OoQLhRmgUhJf/vPPiFEGQcbObzVaXikqh5HY0ck4cCSmTykqVw8O4Hny55wbERyXUDZjVvjX\nM4LjYjb2EDgdG/2l/T04oiRUee49+8Decj3w8p8xe23wXo3A6BVhU1SNxlAKHPPk4BQMqsCRAtev\nEV8QZEWTC2x6Vrxm2WUmlTr4Ewjev0WJ4LBEAux1V4I/+K/2BqiOqBLbvRdsyorgsFjMKJPv8r1L\ngaNEcHi1YkaGnLBde8V2JKNZLrOoPJFizEjZ8IXTYOcdbPf6SMxjUNCxF8b9xidtERyzyvLlPwPE\nYuA/fsBaTxRMTos1eo2hqFfFcdUtMtgjNvVKl1xyCT72sY/hiSeeQC6Xw2WXXWb+rVwu46KLLsJr\nX/vaTS9ylGHTs8L9X1wCP2Z0lK0MYVpPafTHnCZj1xRVyI7Eqjk5FvN9HNd1oNVyDfd2gqsVHYB1\ncPE6KTYaYi1uKSpAHFQ2KmBz3ikqEx+TMUsmwf2uzNVGf4aI4fWaveJmfdVeeZJMgf3yb4j7ppWo\nj1s0aqsStg9Os2HNQxsm/FJUKWcEZ5Mmak3zTreYvXCW2mevSTGjmowDeXDsnh92+dXg93wDePIn\nwCuN84ca+fUilwc2uoteuUZwNipgO6fdHyCrnyolYGYuVKM/Jiuz5Pe0sgS88me9HyCPBwEjrFwK\noYkpUSYuX8c4PrD8GHDgQvAf/0DcHlGKyqxkWyu6Hw+NkvRNVfiFZNNSas+ePdizZ0/b7WNjY7jx\nxhs3+/SjjzxgFBctgeN2dbTV8TIZJxKWTwKwDlSJ5CZNxt6P41+/A/zxhxD773/e1l048PuQwiPd\nwWRsiA7mKXAMgeSM4MhJzcrVDIsnxAHCK4IT1IMTTwgRqJiGeb0uruyVq14Vls6I164PmcCRwrqp\nodNhU7SwbwItbfOl1P1Gbn/OqJzaYM743nh1o+Nn4Yscm+CG2c14CTh3f/vjAPssqlBVVEZ05pzz\ngHPOg/6D7yJuCBzuV9klyeaAapf+xaUFgMWAStnaNjYqlnnZiYzsyIhPU+ngHIR0VhiAm83Ope3y\neBC4aaIVweHVCljdLnAAgB26VHh/gOgiOGo34x3tmkBMEu+fwRjYZIqqWq1iacluOCsWi7jzzjvx\npS99Cc8+++ymFndWML0NYEwMQDMEDq8MocCRIkYVONKDYovgKCkgZxNA3+eXU8hjQuT4XYWvLIr8\n+L98Pdx7ANpHNXQyGcuQuF8EZ2xcXDWpmBGc9pENzLOKyl3gcOOkbYagZWpMPRkaHUbNoXhOOkWq\ntiphPDjy8zPmgw0VZh8cl8o4WUWVTIrtamOTx4+mTwRnYkqUSLsZjZ0pqoCN/twaC7LL3gg8/iPw\nkhHNViO4Xmym0eHSPLBzj9iP5L6+UfZMUZnmYxnxCePBkWutVU3/JZMXum6ETVE1G6J4IT8uXqPm\nLnBMovLgGBVwnqXilZL359kjNiVwbrvtNnziE58wf9/Y2MAtt9yCr33ta/jmN7+JD33oQ3jyySc3\nvchRhsnKgacOi464szuGLoIjOpFq1tRbWyl4RCZjeT9zmrjP44wDAf/mneAhx2BYoxpkoz8j3eMp\ncOTBw0vgZNqjN4BSGq4csP2qqJIp75O4WSWhXB05TcNy0vLUNu91AluqVJzX6/5pOSBcikpTThDD\n1gvHs5OxIzVy/kXg9393c40wWz4enFhcbEMuvXCsrsqKybhbgfO6q8RzPvx9Y00O878bubGuqqh4\ndQOolEQvKMAqFd+oAFmPCE4mJy5MN5QITpiUuDGPymzy6hfBCZCishl7ZWWdMbqD11z6z+zeK3qw\nAdGZfjNZIcY8Kqn4wilxfusjmxI4Tz/9tK1q6r777sPKygo+/OEP4/Of/zzOPfdcfO1rX9v0Ikee\nwgz44w8DANjFr9n8FVi/kaIlOwbUa0o34PZGf21/68pk7O/d4c0GcMEhIJuH/tW/DvdeNMeV4iYj\nOOySS12rIxhj1mcgka/pnCQOiPt6mYzNKzTlQJVO26Ix5owYjxSVpzF5gPA7Pwf9b/7C/z5hBI56\nBTxskSovD06zadteYr/0LhG9lMKgGzTNPxXk1Zy0zYOTCtagTo7ZUAXO+ASw9wDw4nPWmuIJ37Qi\ny+bsHpqgSP/NuYbA2SiLY4jWBPIeJuNYTBQWVCri/YWN4BgeHC4FzrTHhQdgXfB4VFHp3/r/oP9f\n/5v1OUvjedYQUfWq6ACtfr6MiShOItHeWLBLGGMiiuNVKj5/qt2L2GM2JXDW19dRKFgHzEceeQQX\nXnghDh48iGw2iyuvvBLHjx/f7BpHn+kZsRHuPAfYPnwRHPPqKpd3T1HpurXz2ToZx00/ROfXkCmq\nAObkZgNsfBLsl/4T8OgPvWfnuMAdVVQsFhcHXq8ToowweAic2LW/jNjVb3d/rOwTZP7eoYpK87ga\nlj1D1PRTKmOPxqwWhYjxapO+BVNUfH0VOPWi/52kYA7SB0eNJgxbSbxXmXijbvVJAcAOXAi88mfB\n/+mO7ucyNb0jOIBIp7g2+2sK745ZDSRP+J3GsTjLyyWZHLiMtLU0//QU4Jui4n7Ga0PgsL3ni98r\nFdNbw7wiOIBIU22UxbGI66EFDq9tCKE4PumelpakZBS5ff/XH7gH/Gt/J96D3Daa9giOrQBBgV3z\nS2DX3xx8zUGYcO+Fw7Wm6PfmFs3uIZsSOPl8HqurouNko9HAU089hUsuucR68lgMjWHLdQ8AZoQn\n2f4LgPyEmPAc1FC2FZAHsPyYlaJiMUscAJbocTb6A4JVtagRnFiHPjiGL4FJo1uYsLXWbL9S9Jko\nbobEuzHmJpKOkQ1+nYxT3h6cReMKdGbOujGdtkdjVovAZMH7CriTmXoQNBtWS3kvwkRw1H1qs5VG\nXcJL62j9ye8J8Rb0MZyL7yWbc+lk3GwTBrFfehewNA9+/790t8iWj8kYENEGt+7rjoGTrEPkQeLZ\nOTmTsQR3q0NUCRARZJd9nT9yP/QP/LpIkbi9/tK82Od27BY3VEpWZNbPM5IzBI7m8B4FgGUMD05x\nsfPsLI8UFf/pT8C/+GlxYQxY27QsWc/mDQ+Ou7mXze1C7I1vC7zmQExOu3twFucBroPN7Y729Tqw\nKYFz8OBB3H333Xj44Ydx++23o9Fo2MrCT58+bYvwEB5Ig9mBCy0z6jBFcaRxOJs3ZlEpB0h51aUp\nAicWEyda+bcgPhwpaOKxzqktuYMH6ULsgLda7QfSVNo7dVOtAKlUd2FetREgoAgcr07GHp/T4mlg\nctqasmyuWY3gLHunp+T9ofRb2QrIFvN+823C9MHZChGc+ZeA548CZ04Gf0yjIYzRYxOuERxnSpPt\n2Qe88nXgD/5rd2v0MxkDwLRoTtomPJ3dfGVVUafomtdwz3TW6n5rpKh8yeXaegDx0yeg3/4pEWHx\nGgS5NC/GFxgVU3yjbD2Pr8DJi0hPQw4ZDenBqW6IFNW0j8EYRurHxc+kf/trwN7zEfu1/yJukNGu\nhpKiajZE25E+DbhkXimqeWN7dzY87TGbEjjvete7EI/H8Wd/9me455578La3vQ3nnCPUpK7rePDB\nB3HRRRdFstBRhhkTXtn+C63pz8PUC8c4QLH8mDiQ1KpWQy55UJKCRBUQzr/50VI9OB2GbcqOvd1E\nJbRmeyjcb6J4terd7bQTiaT7TKqwZeKL8/boDQCkMzZjNF8t2hqjtdGFGOw58sToZxQfNg+ONKWH\nMTnLVKObwPFoMMdm5rqey8R9TMYAwLbNGsLTOS/K4UORJ/xO25TWFBFf536XyVhRiVYAgZPNAbUN\nU3jx2gb0z3zEiq56RJL4kth/WDwunqOiChzvFBXLjQkx5BxREQRZRVVcEp9nJ9y6Qq+tgO17mbVG\nNYKTTIkoEcS+37cJ3mOTQKn93MUXTonjqFcVZ4/YlLtox44d+Iu/+Au89NJLyOVy2L59u/m3er2O\nm2++GXv37t30IkeeSy5F7DdvAdt9Lvi8EUYdpgiOvAKTJ/pqxToYSTEjozzKlRiLx0XvlVARHKMP\njuHrcU25NBviqtZr6KQP3O1KMZXyfo5qxbtEvBOyF5D5u880caPk1u0986UzYLP2KyOWyljGYkCk\nqPad77kUFouJ97mVIjjGiYMvzQtviRtS+Ib04LQ1QewTsiosVK8a+Z2MTwInj9v/1my691/ZTMl0\nJ5OxFNNLZ+xRQUeKyhRencSn1rT6Qqmks5YQ7LQmQKSoOBefVzYHfs83geIiYr/9P6D/6R94l1kv\nzU/j1HQAACAASURBVINdcEj830g7mdVRfhGc/JiI/jiGjAZCNvorl6wIvh+pdLtAk407zeGddoFj\nHpdWi54ztSInl3fvJj1/Cti+s++9pzYVwQGARCKBffv22cQNAGSzWbz2ta9tu51ohyWSYK96nfhl\nTEZwhkjgtBQPDiCufpxl0LI6STULhorgyGGbhgfH73GNetcpKtdQuN8gylq1+8Z4TpNxh1lUANzF\n4OIZYNYZwbFSVJxz0Qen09VTKrO1BI48McpJz373CZuiGlgExxAdYeZFGdseG58AGg17akhu605y\nmxhb0MFkLAWO2f1XfZx6kjdnKHXwE3qNhkirHpxWxwiO2WxTRl+W5kU59DnnifW6rINzLu4nRZs0\nDm9UxP7oZ/7dhAcHmaw4xterIuXXCdmCQ6671RKPn5iy/DVyeKeMYMvj0upy/yI4RqGJ0+DO50+1\nDxzuA5uuD9N1Hd///vfx6KOPmk3/ZmZm8JrXvAY/93M/h5jXfA3CnWweYDHwSmkgV5hd4YzgbFRc\n0lDSg9Oyesx048GR08TN21w2YXkFY44sqAf+LLmbmdHHZCx6ZXQpcJLuVVTMtQ+O8Zk5Tga8Xhc5\n7xlHfwn15FCriv/7pajkY7ZUiso4cfgJnBARHPMEx9jgBI4UNmFMzvI7GTPGAzSbVodsr/4rmZxx\notHC+8Namm8HcJbJimhSm8BxRHBMc2wHgeMlqDLZkCZjOU29AmAWfG1FCAA/oVVaE5VoUuDkxowU\nlRi06RtxkPftJkWlXBT5NvmTOFNUpTWAczAlgmNGJTVD4GQNUbO+avfn9RCWzYuofHXDGmcBAAun\ngQP9t6tsSuBsbGzg1ltvxbPPPotsNou5ObGRHD58GA899BDuvvtu3HLLLcjlhqj1+4BhsZjovTCM\nKSojnMvVFJVT4GjdRnAc5eV+j5O+BHNkQVgPjlPgZDwb/fHaRvcCJ56w+w4mpsHe8k7g4MVtd2XJ\npDhwNJuAeqxaOiP+vt0hcFRRZjT58/XgyMdsqQiOMYbBN4LThck4NzZ4D061Cw+OPGE06orAabaZ\njAHlRFOvAonxcGv0G9UgmZlzETi9ieCYIzY6moyNCLKMkq2vgu09IAReLOYutJYcFYj5MfBKGazq\nM6ZBkjfSMXI/C1VFlYXZHKNTFRVgpKiU9Uv/08SU5TWsWSZjlh+3Ljg5728EBxCfi7G98npdVN31\n2WAMbFLgfPnLX8axY8dw8803441vfCMSxk6haRq+973v4fOf/zy+8pWv4OabI661H3XyE0OWohIn\nF5YbEzttpdxeRdVyS1F1E8GJW94dl1JxrmnitZJpcfWVSofrzuuS62epNPiaR7i/utFZOHjAXv4q\nKyUJIW7ZL7/b/c7yBOCMVCwKgdMWwZneBqwui6oo2SdnGCM4jHWI4BgiKKjJOBYzwuiDEjjGdhQm\ngmN6cIzhlnJsg94S798t4qema/IhBU6nFBWEidlsT2A+zjGPSYqdbgWOPCk3akbkN0AfHMBKUa2v\nWvORkmnXdXC5/xgXCCw3Br60YMyh8i8eYLkxcM7B19eM1wjpwQHEe5qc8r8v0B5FVgQOi8XEvmsr\nE0/bU+f9EjhqFF+yKHyl/S4RBzbpwXn44YdxzTXX4NprrzXFDSB8Oddccw3e9KY34aGHHtr0Is86\n8mPDFcGRJ11VvcvtQR64NCVF5ZzDFCSC4zQZA+7CSB7E5EHfL73kgqvJOO0T2ahudO3Bif3CryH2\n89cFu3NCSVEp8KUz4r1O2qces4MXi8/1uacss3FHD45POfwg0JqifHd50bsZpBoZ7IRqPq/1VuB4\njvaohffgcNODo0RwgLYp3DZMw38XRuOW1vlk7RLB4c6ePEklyuSH5v565uiRWi2gydiKIHNdt0y4\nQJuHxWTxtGi0J/dh1WTcaW6SjPCsGRcQ3aSopraJfmGdSKZsjf64GsEBjGiXInASSfGepUWkX0Mu\ncy4CRxbODMCDsymBUy6XsWuX96J3796NcrnL6a5nM/nx4Rq42bKnqFxNxmqjv24iOK4mY5cGcEal\nAZMHm7BRCbcycT+RVN3ovkw8DF4RnIUzwMyOdq/AznPENOGnj4gUVTbfOQ+fzrQPcxwkWlM0X2s2\ngJJHD5OwVVTJpLFN9O598uIi9N/+dXCXLszcTFGFEB5qmTigdDU2+q+4GWFNP4r36/BKCfo//0O7\neGwGSAfNzAEry9boFUB0EHcpE+/YtNQrYqSmXoKUicu0dHXD6DCsgcnoiNdcrIUz9hYL+XFx/Nqo\ngHXar2VRxarR9yVMBEf6Y4L4bwDx3tQqqtIqkM1Zn7esygKMNhkpcUyQQqrfERzF4M7nT4lzw1jI\nSGIEbErg7NixA4888ojn3x955BHTl0MEh42Ni8GbWxj+7L9b1RymB0fmwN1MxmqKqv1vXG9Bv+9u\nEXZ3ez3dzYPjIowabhGc4F2hPU3GfhGcbj04YTDb3jsjOPOuA+wYY8DBV4AfNQROgDQa20IeHN5q\nAbrS+XTJI00V1oOTSvlXxUXB0rxY1+kT7X+TZeJh+uDUa2KblCcrM4IjvR9uERzVcOsOP/Io+P/6\nYntjNq+UkQKbmRM9r4pKjyKn4TkRJkXlUSYOCB9OAJMxY0y8742y1dRPpqhS7gKHLzpaLOTz4vGV\ncufSajOCY3x+buX6XhiCgwXx38DYN50pqnEltaUKHK1hRc+y/RY4huHZGcGZ2933EnFgkwLnmmuu\nwRNPPIGPfOQjePzxx7GwsICFhQU89thj+MhHPoInnngCb37zm6Naaxvlchmf+tSn8O53vxs33XQT\nPvvZz6LWIfT88MMP49Zbb8V73vMe/Oqv/ipeeOGFnq2va7a4B4evLEP/2AeBo0fEDVJoyJ1JTUPJ\ng5I8AXlFcF54DvwLnwZePOb+om4mYzcxZPakMHbwsCdtrzJxlwgO11siLNwPgeORosLiaasCxAE7\neDHw/FHwhdOBBM6W8uDI92m0z/c0Gocd1ZAQAqenHZuNRmeu85pkRCVMBKdeFyX8zrYHZvWOdwTH\ndxq7PMY4P4uW5t6XRkXthSNxDJxksZjYbjdjMgbEPhakkzFg9f+R3a9l6tatUZ5cv2LQZ/lxYcot\nLnSOzMrOx2tF0Z09TLWaFKsBBU7b+tX0G2D34DQUoWlsB32roooZzRLVCM7CKbDt/TcYA5s0GV97\n7bVYW1vD17/+dTz22GP2J04kcP311+Oaa67Z1AL9+NSnPoW1tTX80R/9ETRNw2c+8xn81V/9FT7w\ngQ94PqZWq+HCCy/E61//etx22209W9umGBvf2h4cGV0yVLo1ITxlpXPaREyHTsZmh1ePE6xzmrj6\nnCpmBEcROGE9OK5l4i4HR+OAwvoSwTHWpDar03URLZh1P3iwgxeLK99/fxzs0is6v4af16jfGO+T\nTUyBZ/PeEZyWJozIQU3GqZRogrjqIj4igsv9Y7XY/kd5EgpVJl4TJ7A2gWP8dG0roKRrvJDHGGc0\nqdm0Wjl4UZgV7SyW5q0WDM4ycbmODgKnzbsjURvYtbRgPpKcmEfFZVTF9OC0N8rjjbr4jtQIaE4Z\nldPJg5PNiW1vtRjOfwNYg2+DGm8dVVTcKXAyOUu0q92t+52iAoQwVCM4SwvAhZd437+HbLoPzg03\n3IA3v/nNOHz4MBYXRbhydnYWhw4dwsTExKYX6MXJkyfx+OOP46Mf/SjOO080crrpppvw0Y9+FL/x\nG7+BqSl3Z/ob3vAGADDXuiXJjwOVMriuW5N5txLGQZM3jP4ypsBJKALH2cnYECPqlZgSwTE9R16D\n+VpKHxy/Rn/Og34q7W34dMOtHDUtKrHaughLUdZto78wZBwVIoA4sGoamLPJn2TXuZZYDhLB2UqN\n/swJ0ylhNC62CxzOudgGMtkQHpyUfYhjLygZVTVuIqraZR+ctNWZ29zvzAiOi0HXTNf4NPuT3Xqd\nUW+vzsLq8ycSwj+iGo3dvDQeqSEbXp6fjIg68LowGbN80AhORURwMlkrcuEWwTGqwGwpKrU0vFMV\nVSwmXm9tJZz/BuL7if2PTwfbL4H2z3F91dY4j2Wy4HK7aw4wRSVfU178cg6U1+w9cfpIKIEjG/m5\nccEFF+CCCy4wf280GrbGf1Fz9OhR5PN5U9wAwCWXXALGGJ555hnb0M9hg42Ng3Nd5Os79WIYBDLs\nLXe4VlNcycRi1lVmW5TGaLvv1clYzs1RTlT89EvAwimwV/6smDhuDOnkftVX8iBmXFGxVFr05QkI\ndzMzptIibO2ctSOftx8RnPFJIdbUK2ZZ4uoVwYnFgJe9AvjJg8OXolIrhGa2i9JdJzI9lc4GT1El\nU70XckYEh7tGcIwGaGHLxFOZ9tlODUc61kmncQ0y0uSWourgwQFgVFIp34vm0nQwkbTv03qrvWpI\na4K5HedSIU3GgBjRUK2AqSXigKhCcgqtxdPipxrBUX03QY69OWNcw9S2zvd1EGgGlSTp4sGZcHhw\nFs+ICLSum9+D2W+nX1VUgDGuQUnFaprdL9RHQgmc3/zN3+zqRe68886uHufH6uoqJiftqjAWi2Fs\nbAyrqx4VF8OC7FtRLm1JgcPlxit3OCMqwxgz8+bM0a2Yt1rixKy2XFc9OEYEx7w6BcC//x3wxx9C\n/JU/K1JU8sAYN6Jafh4c1WQcJh2haWBZx3TmVNpomlZzCBzjc+iDwGGMiQPxouV54NL/MOM9DoVd\ncAj8Jw8G69WzhUzGpmBJJMG2bQf/6WMu9zEETiYbSJhxGbrPdF9FxY89Df6TBxF7p0e/IsAaNujw\n4HDdGES7fSdw8gXvWWpO6iKCY84LC2IyBjoKHG5cVPBa1drndD3QWAQAYDPbwU8qlWJuERwlcsJ/\n/AD0v/8sYv/9z+3mWo8UldnfpV61p7b91pTLg59ZFSZjtb9MKtXWXJEvurRYUI63rFOKyrz/fOgI\nTmhSyueot8Q25ubBcXoQBxHByY1ZF5VlEVVi473L5vgRSuC8733v69U6TO644w58/etf973PJz7x\niZ6vY6Dk1XlUgzFn+eImcOTBpy2C4+iDo2lKkyslEiPD5epVVqOuTBNuuUR+2quouPOqNmRUwr2K\nyppKzpcXwb/3DbB33mhFsvpRJg4AszssUQMA8yeBwoy9NNcBe/mrwGOxYLn+tLhKDHzi7SVq+mXb\ndmB5oX1dMoKXzljbT6fnTBgRnC774PDDPwb/3jcBH4HDjYM61or2NUvxOD0jzPSyIVun15QeHMDu\nKfMzGQNiuwxkMlZO/Jp32quNmR3A4z+yfnfz4KRS5qwmfuYkUFqD/rd/gdjvfthKv2s+EaO08V0F\n6WQMmP4Pvr5iEwAsmbZ6x0gWT7e3WMjmRCRa14Pt1zLiE9aDE5akUiZeLgFcF2MaJHKshfSuye9v\nAB4cls2DLxupS9kEcRgiOFdddVWPlmHxC7/wCx1fZ25uDlNTU1hbW7Pdrus6yuWyp/9mM9x///14\n4IEH2tZx4403YmJiwrsRWRe09CaKAMYYR7rQ3/HyQdiIARUA2Xgc+UIBG6kkNlJpFAoFrObyaAJI\n5/MYLxTAdR1LAPKZNLKFAlZjDLFsFhOFAng2Y/wtg0azgTqAfDKBrPGe12NAvVFHoVDARiaNjXgC\nhUIBWq2CFQDj+TxSjs+nlkygBKAwtwMskUB5YhKNloZCwM9xtdVCOj+GceX+jZlZrAGYzGTQeOwh\nVO7+R0xd9yvQYky81q7dfTEal/fsQ+PHD5jvZW1pAfyc/Zjye2+FAvTPfxOxsc5XULXCNpR0HYWJ\ncV/R1A+aS6ewCmBy2wxatb1Yb9QxnUkhpnTl1WPAMoDk+Di0pTOu33EymTRvX+U6YmNjSE4XUDa2\nq7CUdQ3VRh3T4+PWScTBSnUDrbFx8HIJ05k0YsZJsLWsoQggM7cLNQDT2QxijgaNbqzqLbCxCUwW\nCljOZJGJxZAvFFBLJY1tfc5qiqewNjEFaE1MerzPYnUDLQC5eAw54z56pYxlAGOTU8h0+Hxq+w6g\nVF7HdDYDls1hUWsiPzVl7r8AsJLNIc4YJgoFlFtNVDNZ4OgRZH/wL8i9/dfFOriO5Jh9n5Ms5/JI\nM6ABIJXPY6xQsH2nTiqFbajVq2DlEpJ7D5jPWRobg7Z4GtPK49ZWi8Duc9s+n6X8OHhpDZM7dyPR\n4TNYm5pGA0Aim7M9d9RUpwsoaxqmJyfQKq1gBcDEnnORNF5zY3obNuo1TOVyKAIY3zaDVKGAyrZt\n2ABQ2LELLN1ZTEdBabqA5qkXUSgUUIeOdQDT5+513daluLz99tsxP29vHHn55ZfjiisCFEf4sGmT\ncdSMj49jfLxzQ6CDBw+iUqng+eefN304hw8fBuccL3vZyyJf1xVXXOH5Ya+vr6MZxOQYEN4U/WVK\nZ06hUnTJ4w8YfVmE3qtrq6gXi9BLJfBYDMViES0jylLXWmjKtbMYKuvrqBaLaNVqYGM6isUiuPGZ\nldfWzJLayuoqqsbjWqV1oF7D8tISeKkMHmPicUbzyNLKCpjj89FXRcnmyrpIE+g6B69WUQz4ObJm\nA3VNs9YOgNfFVdHa4gL4c0cBAKtHfiIiWSyG4kYVrNr71I4+MQW+cAbLS4tgsThaLzwHdsmlwd5b\ngPvwpoiIFM+cFuWyA4Qvi/WubVSBlrh4WDl1yuZb4EXh8WvGk0Cj4fo5FAoF8/ZWdQNschqNlpht\ntLywEHoQpV4U22nx1EtWZ2EHrdUisHsf8PRhrDz/LNiuc8V6T4uOrnUjDbJy+iRYq/OFUatcBsvk\nUSwWoSeSqK4b+92KeF/Fchlso93ToycS4CtLnttHy4hobBSXUTPuI6MclXodGx22GZ4REY7iM08B\nu84BNA2VRtPcfwGgxWJolUti7ctLwO69YAcuQuXvb0P1ZYfA5nahVatB13TbPme9hxRqqyvg9Tpq\nmoZGsWj7Ttvuz2Lg5RLQbEJPZ83n1LlIrauPa506AXaoff/h2RxQWsOaprUdX9zWBwAaiwU+xnQD\nz4n9sXjkcdPEvo6YuT5xnNvA6qLwRJVqdbBiETpLAImk2EYqXU6XD4kei4OX18V3fuolgDGsNN0/\ny2QyidnZWdx44409WcsWLNEJxu7du/GqV70Kt912G5599lk89dRT+Nu//VtcfvnltgjO7/zO7+BH\nP7LCqOVyGcePH8eJE6IJ18mTJ3H8+PEt5dth6bQISW7VUvGauwcHQHuKSv7fHNWgpIBUD46ZolLS\nSTLd1KjbPTgxnz44jYY9ZB/AV8IPP2KW9rqOalDKc/kZo3nbi8eEyTib7Vs6h83uEJ/VyrLwkyye\nER2Lo0Je4W0FH47iwbHKhR3pFiNFydIZQGt2jqI26mJGmXyfXfhwuNxOPaqTOOdAaQ3sHKP4QfV/\nydTutFF0EdRo3KhZ340zRRVPeLf6z3h7cLjeskzy6vetfu6dUHvhNI3926VMXF7I8IrwFLLrfgXQ\nNPAXn7Ne06tqK5M1PDha59J1QKSVGnVhoFZTOI5yda63hDnYOaQWsCwCgVJUxn177cHZfwGQSII/\n/UT7mAZA+Mq4bh1Hpcn4P1yF2H/9n/1NOefy1rZVWgPy48HGUfSALRfBCcMHPvAB/M3f/A0+/OEP\nIxaL4XWvex1uuukm231Onz6NjQ1rJ3/kkUfwl3/5l+bvn/zkJwEAv/Irv4Lrr7++PwsPglEqviWR\nG68UIy3LXMhSGWHIVQVOPO46qoHFYgCLGVVUskxciYTJA1LdGLYnd5KEX5m40gMCMH0lfuif+QjY\nO98NdvXbvcvE5TpOvwQA4CeeB9t3fn9KxCVyoObCaXHi4jrYrggFjvQabYV5VKZZMmEZJZ2CoKWY\njOXvfidmtZMxIN6ni4mfz58CP/wIYle/vf05pLDxKr+uG36RPULg8NWiVfVmiA02vU3sI0Enitfr\ndg9OXTEZ+51Ys3nvTsYbFVEZCNg/V3khEsTvMjEFJBLgxSUwzeH9kCSTloDaKINtmxPrisWs45tf\n5+SM0ZRRrb70gWVz4rPlHGzSXkVlKxNfWRajHFy6gCOXB5KpYGnaXH88OCyZAg5cCP70EbCXvQJI\nZ2zN+8xqKVkqblzksWwOOPiKnq6tjayoohJm6MGViANDLnDy+bxvUz+gvYLrqquu6ouXaNOMjQOV\nrTmuwZynIw8YbibjuCOCozb6izvFj1Im3nCYjAFxgNQVk7FfH5yGw+iYSoure7fyVBgjAbSm+frc\nrVpD9h8pLooddsce4MQxYG5nf0rEJTPbzeZqZolvpBEcaabeShGclCUindEIeTKWJbCdpmBLk7Ep\ncNzfJ3/4++D/dAf4q17X3iVaXiF7CQfjBMOmt4GPjdsrqWQEatooKQ4cwalb4jOVtuaFOToHt5HL\neYso9eLJLYITICLBYjFRHr2yrAhS+3pYMiVSRvI1zz0gogn5ceuixk/gpDNWqXGQdKIadVEFjnOW\n04JRIj7TLnBYfhw8SAUVIEY7AL2P4MBo3HnPN8TQygmHz9TYB8xeOG7NH/sEy+UtAV9aH6jAGdoU\n1ciT38LdjGtWoz8A9h4VbgInnjD74MBZpST/5tbor6FEcPSWNRnXeG7u1egv5UhRAd5RHHlglics\nt2Gb8oT4ggips9e9ASitiaqQPgoclkiK5mqLp8Wco4mpaL0yWyhFxdUqKjOC40xRGd+/jOB06oXT\nqDsiOB7v00gr8Z882P43GbnxEjhynx2bENPblV44ZnsFo2Tfd4yCSt2ZopKp2w5VWNkcUNuwZsbZ\n1mkI5MmCfS5WmBQVIMTa6rL3ZHM1NVQpWykdY2o3AJHe8ng9JquDAsyiEs+rCBNbHxx7N3K+eEZE\nj91aLIxPWoNNOyD79/TDlM8uOCQmnf/0sXaBI/cBmb7qg+DyRIrMjTJ4adXTq9YPSOBsVfLjVsv3\nrYZfmXjaxYMjozTyvqqAiMfFgU8ehNUIjpmiqorJ4c4IjlcfHOXqxQzjdhI4xsHW1YOTSAKMgb/w\nLMBi1tiD557qX4m4ZGZOdGA9dSLa6A1gnSy3QopKPdEa3yFvi+AY9zE63nbsZiz7rXSK4KxIgfPD\n9j/K7cQrRSVLxMcngelt9mZ/taq40k5nxck16DwqpUzcNnQxSIqKc/f3KcXFtllHBEfpSh4ANrVN\nfF4eERwpcDjn4iJGpnTyY/YUldf7SGdDN/ozUU+sqZQRyTWOM8sLwPQ2cdHgfE9vvR6x9/5+59cC\n+paiAgDsPyhe56Xn28uupcCR5wyv1gH9QIrM6gZFcAh32Nwu92nEWwFzqJsUOM32FJUzSmOajF1S\nVPKqg8Xa++AAog+GrdFfh2nizhQV4H3Sls2z5AnLJRTOGBPPc/K4aLY3t1vsxI16f+ZQqWvZvhN8\n8Qz46RNgUQucTmKwnxjbFGPMaPiW9fbgpINGcAwDeqpDBGdlWZy4nv130U/FgGtN67PxMhnLFMHY\nBNjUtnaTsTSlS/NsB0QKVbOlqCyB49J3RoGZqT2jbf5Lz4Mb+4FMG7HCrMODEzaCMwOsLCk9edwE\nTlN89lrT6huTGwM3xtH4eqdkA7vAwzaN5x8bt1fIyXXJdW6UhQ3ABTYxDbb73M6vBfSvDw6MKNF+\nMS2AeaSosAVSVJbAqQzcg0MCZ4vC9p4PrBbd270PGtNkrHhw2lJUygErnvj/2zvzMDfKK92/n1q7\n1FIv7sV2e2vvxsYOEAzYAw5LZiDB2VgeJsmAg814PGwJYe7kmQkOMJAMDCE3DHDD4oFwA8MSIDBc\nkkDAccDsEHZiO16Il267925treW7f3z11abS0q2tpT6/5/HjllQqlVRS1VvnvOccS5OxfEytCgg2\nGNupy3TVqMlknG3Yprl5mpqiyjAPR76GvDLPdKXodIn3ObVDnKBmdIr7y2kyBkQE5/BBoPugKM0t\nJk7d7J9KEx81nvQ8nvSIh5qiUk7+WQQOTyXFd0+fosrkNRroATvpNAAM/I+vaffrRU3GCM6QmIHk\ncIhU1IDJgyO/L1bvxwplXzCLKire15N9RIAU35EIeDKJ1A+vBv/Db8R9oWHxWdQHjE0Px5OiMkRw\nLEzG8ZiagpYpVebzA+Fh7biQS+CkUnmZjNX3HDD2XGHyhC9/75FwcX67agSnPCkhtmiZ+CNDikoV\n2Pnuv1Igo9qhkYrOoQJI4ExcZs8T/yu+j3LB//gakv+ao2N1NCxEgDzQ6juRypNH1hSVw/iYKnCa\nLCM4PGo0GTM598pC4PDRmHUEJ9PJTAqfsN6Dk0HgAGrURC0DLnMEBy1TxcE5mSh+BMduF5/rRDAZ\nmw20bm+6B0eJCrJ8PDh6j4giFqyEHB+NCR/NjDnAgqPA39alqfSiJqPJeEjzbzQ0A4MDmlcsGtG1\nzvfmZzKW+0J+j/VVgUe6rKuAJB7dlXTvYfFd7xJVgAgNA76AuPKPWVRR5ZuiapwifrODyoVYpgiO\nTInJiIesEs3k3ZG4dQND8zhpM7s9ffwCoKVsZMRWvy8KoZwpKgBsQQaBI4+7w4OiAqySnciVz5X3\nHQaSyYqNaQBI4ExcmloAfwB8386yviw/fBDoPiCueK0eTyTEQSLQoB1oc5mM7Q5TisoYwVEFTmOz\ndgDi3GQyTmkmY/k8yz44MaPhz5W/yVidTm11cJcHkKkd4v8KCRym79tRZIGjzhKbKB4cQwTHQhCY\nU1TZPDjKd4k5XcJ3UWe3TlEpEVPW2Ax2zInAJ+/ret8o/3uzlF+PaJ4D1tAsepMMK9/vSFjzSrg9\n2ccoSOS+MI1q4JyLPi5ZBY6u+uywaDLI5SyzkWEhNlzu8ffBAdSKMC6rktIiOEp5tvTb6E3GoRGt\nl1HGPjhKfxcgb9EFjz89haNGcOTvPWzZ/XnMeLxi0HC5IiZzFgCLl4PNXWS4m9XVifc4PFQ2sZUJ\nZneI72m38p2o0JgGgATOhIUxBsyaC753V3lf2Dyp2Iy82gs06MrE42q+m1l6cMx9cCw8ODabQ8fc\newAAIABJREFUOCjFtXVq2xRNF0a2uiwpKqsIjvQeDKkdcA3vNxzStjFrBEfk5tlMJUVV7giOLGv1\n+tOv4opBqSdt50vcZDx1W6WoZB8cZR8kLDxZ6vpMKRRXhgaQsqy7sRls2XHiNf78ibhPRhKmtGU0\nGfPhQS2C06i07lc6DvNo2BDB4RYl3Hyw33iH3EazB2ewH4iPZo/geJUr6UgIXJ5spMAJDQux4XYb\nhCPPFVExI5sWqgInwywqWSXp05mMw7kjOIZeL/mkqABRGdXeYbzPYUpRRSNFmc/EbDZgRidYex6z\n3ooAczhQ953rteOPHpdHjeBUHK8PvPuA+JsiOIQVbNZ8YN+uos65yokqcDJcxcsDe7DRGMExVVEx\ncyfjpC6Ck2ZAjosTtsNp/fpmkzFgFE2G7bfoZKxbH3/iAaS26Ia1SiEVjWiCzepAqkZwlANZ+wxg\n6gyw6bPSly0hzOcXn9W0GaUJQ7tcEyNFZRHBMZdVc/00cfmcTJinLLs8lpEq3q+I34ZmEUVlTKuq\nUiuPWrN6cJiaolIEjvThRMKa8dedbprm+/cidfU6LcoCaPvC7MGRy1j0cVFxukV0IRIWrQUAMbQ0\nkRBdhX314nMYjWkR2+TYUlQINIjeTFLg2C1SVMmk5g2RBlRfvTJMV9mnGVNUOhGSTydjALZvXw92\npqlpq7zokb/xaJE8OADqvn8r2LGrirKugnB7NJ9ZpfH4NNFLERzCCjZ7nohulNNorB+PYIVyUGbB\nRq3809JkbO51kxQHUW4yC8q/ffVKMy5djw9JLGKR2hpbBEf6LXjfEe1q0vw6SomlVekonC6gcYp6\ngmJ2O+quu130pig3M+aAzS7+vDUAxk65lcTUtI9ZjR1QU1S5TcbWERwLD8xAL+Dxgbk9ImJQHxTR\nEkCIGptNpJ50KarUL+5Eauuz4oa+LNYfFCZ6KXB0vg/msUhRde0Xvw/91OtRixRVMgkuvTTmRoQ6\nmM2mepd490Hx2qkU0HcYCI2A+es1AaGviATyq1iCElUJNuoiOBYtFgBxDPP41Gabsn+M+tlmNBnr\nBU5+ERzmcqVHe6Sw1aWoyh59LTXys5ogERz0HRGVsb70buHlggTORGaWNBqX0YeTK4IjTzKBBtFj\nIxE3dTJWDsSmNBRPJjRBYu5yDIgfgcOlEzi6148pV5gGD04GgWMyGQu/RZ22PmWAp4TrGwvKq0yL\nAylrbBb57wmA7dJ/BfvqhaVZudmTUSnMvVEsPTj5V1Gp3yspfjNEcNDfq3UaBsTJWxpowyPiwO3z\nGwQO/+Pr4Fv/n7gxoqWomM0mni+jQgYPTvr7UYWQ/vOX2+jU9cEBgIOfAsGm3BOiPV4gHBYCZPFy\ncd/hLiHmfX4tBSQrqZSme2OKDspmfxZzsdTtHewznuiUv9WUXMY+OFqKKu+okhUOcwSnOCmqCYV7\nAgkc2YPJXy9+BxWCBM5EprEZqA+W14eTS+BEdQJHLqfvYyGvivQHJtkHxyr8bY7gyAOQFB5Op9bo\nz2by4GRs9Gc66Ot7hwwPmsZB6P5WSyzTD6TsgktgW/+d9NerAEyWIZcC/SiASmIuE7fy4MjIobIc\nz8NkrKWorFNxfKDXWHodbNJOwuGQSA96fGqKiqeSwFA/cGCfiJKEQ0bPwbQZ4Pv3ib8NHhyLCE5/\nusBRK730KSoA/MA+oCVz9EbF4xUjX3q7RbSxzg5+5JAQa7KKCtDEVjI+diEhfThW30nlPj7QpxmM\nAU3s5IrguIskcHQmY55IiO9XuVs8lJoJJHCYTEVWsEQcIIEzoWGMAbPng5exVFwdv5AhTSG7yaqD\n7EZHDSkq1tIO23euB/Quf9kHR5b1mquooHhLHBYpKn9QHORT40xRAcbKk5FBo3jTTxhWIzgWAsfu\nKEs79orjcmc2mJcA/pc94Ls+Sr8/YSoT91iUiSeVsRry5DiWFJXTLdoPmOnvFdE6BRZs1KWoRoS4\n8fqAqOgtg+EhtQs33/478RzdQZ3Nng/s3Sm+e5GIrg+ORURKen30EZzRqPDRyM9CivcD+8Cy+W8k\nHh/4/r2iXLe9Q6S0Du0XYtHn106K8jVzzfOyQP28rH4fUlAO9FpGcDCUS+DoU1SFRHAUYRgfVVOT\nzFNbERw1GjchPDjK95wEDpENNmteeY3G+aSobDbtamw0lmYIZYuXG8KSTIoRqxSVFC3SZCzbqcvX\n9wd0s6hMAseyTNxkMgY0X0ksKh7Xv7fRUZEnBjTvQyEH0iqHlbmKij/7GFKP/zz9AX1vJUAIg0jE\n+DtQDOvMZhPfhzGYjJnLbW2m7u/RIhKA6M2kpqhCIq0j+8tEw5o/LtAAvv0F8bdujhGbPV9EBg/9\nRfhr5Anb5Ul7P9wigoOYmK2mpoykwBkayD+C85fd4u/WqUBLO/hekfJm/nrd2ApFbOU71FJPHhEc\nDPQZ56ZJD86AFDgZXtNVLIGjbMdoTIsEUgSndCgRnErOoQJI4Ex42IzZ4gA5PJBz2aJg5YHRI6sP\n9G39cw3Cs5tSVBYRHMgIDiCuIuV2BILKsL1keuTHFMExdKvVI1NUMkKTTGgVOPGY1rI9iwdn0pCp\nfLpE8EjYOloYHzWm4dweIRD00SW9ud3uyN7JOM1knC7keDIJDA5o5d0A0CAiODyVAg+PCHEjw+/h\nkNY3Z9XpWrWUPkWlmMH5x++K5eRzPV7l/ejeu6UHJ2rdmRsQTR9zwDxe8ZnZ7UDTFFFW/pc94kFv\nvZYCkq+ZHHsER/UsmSuoAO03PTxoiOAwZcYYl+Ix02s6nCKCBRSUomI2m3iN+KgWCaxRD86EiDTL\nkRkkcIisNLaI/8tVSSVn1WSM4CiVIPry61xXfaYUlWUEx1dvbKcuG7P5g9aN/qw6Gas+iwwCRz+8\nVJ3nExcHBo9XpBuAyrY5rzQuD9DfA97TXZ7Xi4atxbQ5KiivtvVpqqRJ4MTz6YMjTcYWQm6wH+Ap\n0Z1Xvm6wSXzPQsNqBEc/a4cP9ooBrKvP0Najj+AEG4HGKarAkVEDtcmckqbinFsLHN2gTQAGgcPy\njeAAQMtUYQBunaoJQX+9GiHhMl2WZbJ3Jpj0LFlGcHS/Ra9p9pPPr0VNM/XBsdmsCxfGg6zSlO+1\n1qqo1AjOBDh+kQeHyAvpdTE3ACsV+ZiM3R5dVUIs96Rf2QfHopEeM0RwtHbqXJ+iikaMs6jkOsx9\ncJTnsFwRHP37kwMLvX5wGSWbxBEcdtKpgN2O1A8uQ0pWBpWSaMT6u2Zu9Cf9EnqjsX4yfY4IDkZH\ngbo6LQro8qQLHH0PHIn6++vTPDgeUwQn2ADWOhWYPktsj3nC/Kx5wI4PjO9DHYSpvJ+RIe0CwJyi\nyiBwsvbAkchtaRXRHqaP+vjqNfOyPOknxmMyzubB0d3nM30u3not/ZdNVMkTdyEmY0AcX0ZHxUUa\nUHspKrVMvIKTxCXkwSHyItAgmo2NMYLD//wJkjdcpc3ByRfVZJwhTREJGSM48dE8IzhJ8Fe3igNe\nq+4gK+dLySoquc54TISm/UpDMCuTsdmDY27mJnG5wWNR8GF9BCeqvV+HU5wIslRRTRbYjDmw/eA/\nwY5bBf6L/6P1WykVkSwRHPMsKsAUwUlqJ0a7PbcHxzzCw/wdl78xfZm42qyvX3z3fX4LgSOWYcet\nBlra00qs2ex56b4P1dyrnGyluGLMuF3mcmb5u7Oat2SF7Lsjf3P6UR8+v4jqOHWfRbbJ3pmQgtCy\nf5Re4FhEcGTDxEyjGgBN4BV64eF0AvGY1jCyxkzG6vdkApiMGXlwiHxgdruIYgyOUeDs2wXs3WlM\ny+RDjggOl5UgY0pR1QH9veC/fQLsr78K1tBkfAzQTMaAOBlJ4eFya43+0joZ55eiYmoEx6KBmqy6\n8vq0FNUkNhkDwrfB/uZr4sbQYPaFCyWWJYKj/055TBEPwOj9cuSI4KQJnPQIDu/vESdp/YlYmUrN\nB/vEydjj04YJRkLiwkP5PrMzz4Hte/+R9tJsjq4po3wf5oiUMs4Bza3GMvFo2FrgTEkXUpbI15MC\nZ0qbEFEuj9bQUu9HGk8VlcMhrtSzmYyhVErqkbdttrT+OQakTyjPTsYZkXOx5GfudGdfvtqYSCkq\neRFQwTENAAmc6iDYNPYUlWwrPzRGc7KFyTj16lbwD94SN6JhYVxUrhJ4NKJ0J87yo6qzi+0JNGgn\nTv1jgNYHR27DqNLPRpYtJ+JAnd6DYyFw9L1z9KgCRyf2VK+RUnXl9YkScv02TWasIiZFRi2dliX8\nehLmPjhye3Sl1UlTiko/id6MSeCwtmlALCr6yUgGRJM/vXBgDkXwdB8UPjCfX1x0uNxC8Az2qYKd\n1dVpJmI9s3QCx2VKUUkPTn+P8JW1tBvLxKMRzX8E5YKnri77kE09agRnmvJ+nCJC5deJOP3YiPGk\nqABRSWWVorJn9uCoVVW5BFXRUlQ6D47bU9EGdKVA9XVNhBTVzLlgXziv4s1Ra2sP1yoNjeADYxQ4\ncnrvGKqvxATv9AgOf/4ppH77pLghW5zX2cUBWYaYc6WoALBz1qV3XtU3+pMHw9FRLbIif7SRUFoE\nhyeT4KFhpB78maiSMTdzk7h0Hhy1f48xgsM8Pk0wTeIUlYoapSidwEEirvmozOIkbk5RedK3J62K\nKofJWC98l6wQvqvXt2n39fcaS8QlwUbwQ38BAK1E3KNMFNdFcDLBfH4RQXE6tRlt8v1IATnQKy5k\n3B5j+brVSAGnCyzLiAbDawcaRcRGP3yyZaqxJ43Lo6bK+FD/uNIKbNmxaROuAUWQSSFhTlFJMZhL\n4EhRWIwU1ehoUedQTSgmUB8c5nDA9uVvVLyiiwROFcD0vTjyRRE4fCwRnGRCbVpmHJUQAf6yW7ni\nFgcHxpgQEkrLepZFFLDFy8FOO1t4FMzIE5TXp4vgxJQUlUv0ZQHE65qniaeS4G/8AfzFZ0Tpq7kd\nv0Tpg8NHhsQARf37i4+KH6H+ypsiOOIzs9nSuwcXE300xixwTH1wmMMhbmetosphMtZFE5jdAXbs\nSeCv/R6cc1EGvn8vWJOFwGloAg4KgaMKA5nSHB5UPTjZYLPnG0+qDqf4PkvDqzIigrk82tgEwHKk\nAFt1OthnTsz5mgCARUfDds3/NrwvdsyJYEd/VlvG7dZes6c7/+iQDtuXvwHbWedaPyhPcmkpKkXw\n5EipqA3sihDB4fGYVglaa6iRrsoLnIkCHcmrgWAT8PEfx/QUdfLxWDwUhgZ4ur8jEWBkWBgho2HN\nP+B0ahGcLKKALTgKbMFR1g/W1QEeL1hdHbiuikqkqJxa/j0Stqyi4h+8DQDgvUe0uTeZRjUMDYC1\nTgXfswN8NAYG6KqodAKHIjhCwFoNuCwm+nXHYsYr/Pho+onP402vohqLB8ckfNnKU8D/8Ftg95/E\nmIWDn4J9/R/SnsqCjeAfv6dsg9bHhncfADjXyqSzwFafAbRN024rPhg1RSVHRJhHSEQjadEG2/nr\nc76e+jo2G9Ax2/j8U79oXEia8BNxIbTyjA7ljcMhPD5mgSOb/eWM4LhFKX42n05e26FEcGI1OIcK\nmFAm44kCHcmrgYZGYHAAPJXKP28cGocHR6Z4GAPXN1+TV9qf7lYEjnKQd2oRnHGLgqYWLXwufT3S\nZCw9OBKDwLEB4SiwT5nT1dutHZiz9cFZdLTyXnWpOKc5gjN5y8QNWI1HKCb6CI4+JSqHuJpPfHqv\nCCAGuOo8OHwsJmMAmL8EaGgG/8NvwD94B+zYVdZCPNgkfGaALoLjF78HIGeKClCimHLYpcTr00YV\n9PeCLZkhhLshghMu/clYCq3eI0KwFV3guABnLC1dwXx+cCA/D04RfpPM6RLjWCJl+EwrwUTqZDxB\noBRVFSCajSVEs7F8GY/JWJ5kfH71b55Kafn5vTuFCJJXlE4XeB4RnGywz30Btn++SdzQV1HJoZmG\noZ368Q92YM8OcWXo8gC9R4RhGEi/gnG5jR4cu0NXNaL1wdFeh3Q/gPSISbHRiyd9xFB6acwnPrPg\nGkOZOLcQOMxWB/bZ1eAv/w4IDYOdc5H1k/Xl2Iq4Zx6fljbOQ+BYwZasAH/nVfEbUwzO+ggO51wI\njxKXMzO34sGRzR1LEcExN/kDtIhdrosjl6c4UVXFZMyjFr6mWsAnGjcyKx/ZJIUETjWgbzaWL4rA\n4cPjSFH5A5b9cPjODwHohtQ5XdoJcJzdfxljalRKtFO3i4OQvkxcYi4Tj4+Kk8vCpeC9h8VtZksX\nKE6XOPmNxkTZon66uFJFpVa/1NXlV347GSi5wLGO4KhCxXwlak6ZJeJaH6VcHhyrIawQaSoAYJ//\nSubIhfTYuNya10z9vtjTzbN5wlaeAvQeBj58W7yvhmZj+XosCnBeekOsUibOj3QJ35X0qRULhzM9\nPQXkn6JqnqKW6xeE06nOomI1aDJmLjds/3EfkMkOMAmhS9VqQOb4B/qBjjn5PSc8IqonxhPB0Qsc\neUKZPgvY/Sfxtzw4OJz5VVGNBZknH42Bef2mYXsmkzEAdtQxIpL0p/fVdFOaQNG3t/cHwfUCR574\nPHkebCcTbq9W5VMCDBVRFgKHmZu/uT3aSAFAmU8mPTjOHB6cOJhFFIHNmgfbt68F5i/N+FQWbBSp\nFH2HYilwgo3jLzeetwRoakHq178Ur9PYDB4JCbHBuRqtYqVOp7iVPjg93UBTi3HmWzFwONN9cYAm\nenKZjFefAXb8KcXZjvioKKSoxRQVyvBdqTIoglMNBBsAQBtMlwO1ZLqpxTieIBeyksUQwREnFLbg\nKC114NFSVKoHp1hpHYfoNqqlqHQHRrPJGKI8Fc2twj8goz4mmP7gGghaRHA0Dw6j9JQKM0Vw+J/e\nR+q5XxXvBWIZIjjye2iqBmFpEZwxlIlLr5UFbMlnjIM9zcgUlD4KIX8D40xPASJiyVaeAuz4UFmX\nEsFJpYRYk2Ku5BEc4cHhPV3jqqDKicNhTAFL8uyDw2x1xTlxy1ENFsZtojYhgVMFMLtDNObKd1yD\nNBi3TweGB9KbqGVCznLy1WsnnKgUOLorXCuBU/QIjtKfxu7QTmJ1JpOxzQYsXg42pVWcLAf6rE9i\neoHjDypt+pX3lxg1moypgkrDLHBe+z34I/eCf/JecdYfiaj7lFumqMweHE96mfiYOhmPMzonU1RW\nEZw8KqiywU5Yo91obNZKomNR3cykUkdwpAfncPENxgBsf/1V2E77YvoDbo/4DZcraiqHbUbCJfc1\nERMDEjjVwli6GSv+G9beIa5qpQjJhSFFpfgA5Alu5lxNKMiJyE5nwR6cNJwubVSDfD2rWTTzloCt\nOUuksZpaAUDMTbLq4qkXOPVaBIcnk8Ko6nBp5lFKUWm4jaZeOcsr9X/vBM/md8mXaFgbxmeI4Cjr\nTqui8mbpZGzP4cGJj7vDK3O5hNgzRHDE36yACA4AsGkzgZmdgL9eVBnpBY46M6kMHpzRUeDwoeIb\njAGw5ceDyepF/f2MichOuX5z8uKpHJVpxISABE61EGzKO0WlRXCU8us8fThqaXh9MC2CA68PmKH4\nf/QRHBkdKmYER1ZROUwCR5eish1/MmwXXCJuNCumyK791iWSMs3l9ohUhExRydEODoc44DFGERw9\nZpNxaEhMxu7pBn/2scLXH40IYWl3ZDAZ5+iDY6iiyhXByZyiyotgo9bFGNowwUJSVBLbF84HW/15\nccMgcMqYogLEhVAJBE5WfPXZ04PFxOkUkapUilJUkwQSOFUCCzaOPYIzVQqcPH048VERMvZ4gdFR\n0d1VXkW6PWAzOkWVklVDvWIJA6dmMlZPSKrAyfB1rQ+KZQf6jJ4ddZ1ubTllfTwWVfv+MKdTGEU9\n3qwdmScdZkExMgw2bzHYX38F/NlHx9Yl2wrZj0TviQLyj+CkeXByRHAKiBSwM88BO+lU7Q4p8vPo\nYpxz3cecCNvXLhQ3dAJHNWGXONrA3FqlYilSVFkJBMsXTdFF8FgtlokTaZDAqRYamvL24HBzBCff\neVTKeAT1IBtXjI52h9ranh23SqtS0kdLCp30K5ERHDlsE7BOUelgjKlpKssIjlyPInDU6eLSzCoP\nfF4/9cDR4/EBibiWjhoZEqmUVacLcbF/T2Hrjyot89MEjvVUeHg8ooWAFDL6FFUuD47++zQObCed\nZmzUJ79LzUUuqdZHcGIRwG4vfYRDX6lYCpNxFmx/dxnY2ReU58X0ETyK4EwKSOBUC8o8Ks45eGg4\n+9VzeFj8mAMNQJ0dPN8IjiyzlicCpWeEvMJii46G7ZKrteVLEcFxOEUVWFxXEWWRokpDnmjyEDjq\nCXXUdCL1+siDo0PtdxQNC6N6aFj4s5pbxfeq60BB6+fRDBGcDI3+mHmiuH5UQz4pqiIKBdbSDtv/\n+ndgQeby8nFhMBmXySsiIzgut9i/ZYS1TwdrLMyonfdrWQxvJWobEjhVAmtoFAf0wX6kbvoeUv/1\nvzMvHAoB3noR2agP5t8LRxp79QInlmUwnf6KqEiRD+ZQjMupVHqKKkt/Dtbclr5N6naK98Pkwdvs\nwZHP8fgAO41pUJGCIhIW/oxUCvAFRJ+U1qlA98HC1h+NCNGSKYJj1clYbg9gMWxTCKPUgz9D6okH\n1KfxZFJs+zhNxplg8xYXvymk8l3n0mRcjkiDjOBMaavtJpf6YwNVUU0KKB5fLSi5/tQDtwMHP81+\ntRoe0So+AsGxpagMAkcpVXVlOBjoIzjFag7mdKojKWQkibk94ED2YXtKBIdZnMSY3S5OhAHpwZEC\nR5pZZYrKV9rZS9WGNNXqfDjMr/QuaZtecARHnZTtcmnRNEBLQZkjLuYITtIUweEp8GQS/L03jKkW\nvZl8oiN/U9JkXBaBo1xAlNt/U270xwZKUU0KSOBUC3Jcw3tvCG9NT3fm4ZuhYa1PR6AhfzPoqKm5\n3mgs+ywceTC224t35edwaVVg8oAkX6cuS8CxWfHgZKqUaW4Fps7U1jca06IGynPYMSfBlUogar2G\nyYcuRYWkkjZSomCsfTr4G38obP3yBJ6WospgMpbbIwVXwlQmDggx33vY2LMmrHQErgJjKbMpJn7Z\nB6csKSolBV1m/03ZMURwJv53gSgcSlFVC7Icde4i2L7yDXESyNClmIdDaudQVh/Mv5uxrFxSryJj\nik8iw8FA5rSLZTAGxOuHh7W/Ae0gnyWCw5qzmIwB2K6/XWuqlmYyFs+xnbAG3i+eV8jW1xZqBCek\nDXqVab62aUDfEWODvrGiq6Li5ioqZkuPCqoRHJmiSqrfPdWIu1eZLj8ypD1P/u0Pjn9by4mcDRXN\nkh4uJk6XEIit00r/WpVEPV7V0UiWSQJFcKoE5nCC/d2lYEd9Rjtg9x0xTjqWhEfA2qaLvwMNwJ8/\nyes1uJUHJxoRIslqm1wuMaOnmKXVDoeWrjBXUWVNUWWP4BjSW/IKWQqcQvqj1DKKoOCRiNbvSElR\nsfbpwnh8+BDQMXvMq1ZnLXk8YE4XuF6QJOKAwyIq6JHbExbbY2j0J05YfM9OcXtkSOvgPaII/Pry\nGmjHjSJwEA1n/O0VE2azwfZPPxLz5moZKXDc3tr2GhEqFMGpImx/9XmwphbtZN53xHrB0LA2+yXQ\nUJjJOFslh0NLURUNfZ5cHpDUFFUWgRNsFD6bfIykTjeQSolIl/51CAPM4RD7NhoW3ymXW6tEaVNa\nEHSP04ejDj30Kh1mTSZjqytsp0t8B8IhEb0BjB4cAHzvDnFbTo+H1oG53BVC40YKnHJVUQFgcxYY\nZ7bVImondjIYTxZI4FQjXr9oVtd72Prx0AjgU9IL9Q1ikF4+qYT4qDjIKQcCLquoMh0QVOFRRIGj\nj6aYD0jZUlQ2G9hXvwm2YmXOl2DSYxQaVroXU7g6Ix6fEBQjQ9pwREBEcrz+8RuN9ZOyrTw4VkNT\nZWv/0LDmCaozChzs3aVFNWVUaGRIrM/lRlUgBU62CkZi7MjvFH2mkwYSOFWIaGzXAvT1pD3GORcn\nJOVkxAJiEnlePhwZwZEHAllFlcmD4yxFBEcvcMxl4tm/rrbPfwVMjpPIhtzukWHA4aBwdTbcYtI0\nRoYNERDGmBjmOt4IjqyEUhv9aVVUiCcyi05/wCBwmH7YJiDSswuXib+lwBkeBOoD1bOf1QhOlgpG\nYuyYPX1EzUMCp1ppbrGO4MSi4uAvU1Qyh59Psz9F4DDGtKvqaLYIjjTtlUrgjMGDMxacughOkXuj\n1BweHxAJgYeGVP+NhBVSKq6flJ0Wwcky+dtXL8RWIkMEBwDkYMdhXQSnWtJTgDZKJBqmaEMxqbML\n8zqViE8aSOBUKaypxdqDI+dQ6T04AHg+bfXN85+iOcLkpYjg6H0AivhgeTT6G9trKM3URobJf5ML\nj1eIkZFhMJ9R4KBtGtB9QDPzjgX9IEmrWVQZIzj14KFhzYNjIXDk5GquVH7x4aHqqaACwJxuIW5i\nUYo2FBFx4easinYBRHEggVOtNLUAvZkFjqHR37zF4D//TyRvvwH88KHM69TP63G6tLRWLg9OET0s\nhrk78m9Xbg/OmFAjOENUQZULj1e0CrCIgrD2Ds2fM1Z0Q1wtPTgZvlPM5xevp/bKsRv/rw+Kfi4O\np86DM6h1sa4G3G5gUCkMoGhDcXE4STROIkjgVCvNLUBoWPTK0COb5Mk+OLY62K7+IdjF3wE+3Y3U\nnT/MfMUth20C4qSjVF+xnH1wSlBF5XBqTQynzwSWH1+8TqtS1FAEJyfM7RV9cEaG01JUaFdaEYwj\nTcUNERwxYJWnUuK+eDxLiipgMhnrhm0CWsm6P6Dz4AxVT4k4ICKMg/0AdPPAiOLgJIEzmSCBU6Uw\nOT2732Q0DpkiOFD6XJywBrZvfRvYvxd4/03rlUqTMSCarw2Jg2xOD04xW+Cr69SEB/MHUHfpvxYv\ntCyrqEaGCpowPSnweES5csjCx9LSDjAG3rV/7OuNhIUfQt9YUvYlyhLBgb9efMfNKSp187jVAAAg\nAElEQVTl+8JUgaN4dQCxn8vQT6ZouN0iNQzQybjIsLPOA/vsyZXeDKJMkMCpVuT0bJPRmIeNERwD\nC44C5i5C6tnH0h7iiYS4KlYFjlMLk2e4imS2OpEaKIXJuJTCQ/HgIDxCEZxceHzAQJ8w9Zo8OMzp\nEh6v/t6xr1c2+WNMK9uXfWvio5n3i69e7Dd1IKfy3XO6xHd2zkJxW4ng8FQqrQJswuPUlbNTiqqo\n2E75G7A58yu9GUSZIIFTrQSbAGYDNxuNQyOA2yMmPptgjMF25jnAro/Bd3wIPtCH1IvPCI9F3NQ9\n2OkCZAQnW6mqbPNeLGQEp5TeGLnuZLI6BjBWErdXTfUwqzSPx6tFG8aCfpCk2lgydwSH+etFF2Pp\nD1PENXM4YbvuTrDjVinLBcBHhoTRmKest32i4tYLHIrgEMR4oVENVQqz24HGpnSjcXjEOnojWXYc\nMH0WUvfeIjw2iQTgdIMtO0asV19FJYcaZksNOV1gJfLglAoReXIoDeUoRZUV/b73WYgEl0eriBoL\n+vYDTmMEB/E4mNVr6baBKx4VffSQyagmIFJUh/6ClIxCVlEVlSGCQxU/BDFuKIJTzViViodHDP4b\nM8xmg+0r3wTq7GBr/1Z4E3q6gJicrK2UZutTRNmuIh3O0jT6K7U3Rik9Z1RFlR39CdYqzePxakJ4\nLOh7vJgFTiKLyVganaXAyfTd8weAkWHwYUXgVFEEh+kjONTojyDGDUVwqhjW1GKdosoWwQHAlh+P\nuuXHAwCS770JHOlKH3Cp63HDspWBFztFVS6B43Qpjf5I4GSDebxQa+7MVVQA4PakV/LlAdd36R2L\nwJHiXY3gZGgd4BMenNRQFUdwHE6tUzNBEGOGIjjVTLOxFw6PxcA/flerJMkD1tIG3tOtnVzMAieH\nyZEtOAqYVUTTnrOMAkf/eoQ1cv87nJb7hLm9Wk+bsRCNaOZ1ixQV7JlMxkqKaihXBKceSMSRPNwl\n5o3J2WzVgGxsSf4bgigIEjjVTFML0N+jDtLkLz8nZvGcdnb+65jSDhw+BMQzCZzsB1nb326E7XNn\njXXLM+NILxMvCWo5OnlwsiLTSL5661lOHo82dsFE6uf/Cf6nD6zXGw2LQZtAhgiOtXBhDocQANJb\nU5fBjKykpJIH9oltL1aTyHLgJoFDEMWABE4VwxYtB+rs4P99N3giAf7bJ8GO+yvRyTVfWtpFRYqs\nSjGniMpcpspsNpEWK3VkRV4lUwQnO1LgZCqzdnssq6g45+Dbfwf++jbr51lUUakT7+Oj2btj++qB\nwT7xd6YUlbK9yf37qqsHDqClqMhgTBAFQQneKoa1Twf7278Hv/82MW+n9zDYpf86tnW0tIMD4Ac+\nFXe4xhbBKQkOV/lSVOTByY4qcCz8N4AQKVYm49FRIJkE37fL+nnRsPbdUqfX6yM4WfaLv17rnpwp\nRaWkshIH9omZWdWEGsEhgUMQhUARnCrHtvoMsFWnAX98FVh23Jj8NwBEBAcAP7hP3FYneCv/V+Iq\n0uksQ4qKBE5eKCfZtEGb6uMeaw+OvG//XvB43OJxbYirHIKoeXASuSM4ctlMLQr80qszUL0RHEpR\nEURBUASnBmAXbATsDrDPfXHsTw40iJPLwb8IM6Y8schy8UocZP2Bkp+UmNMlqoNI4GSF1dWJ70Km\nMmu3F0gkwONx46DUSEj8n0wAB/cBs+apD3HOhW9HXwKtDNzknAOJ7Ckq5g+IfVdXZ+0LAkR3ZKcT\nGB0Fq6YKKkBrYUARHIIoCBI4NQBzucC+sWl8z2VMGI279ouyVOWEoQqACggc23euK314njw4+VMf\nBIKNlg+pZeTRiLG0W5e24vt2gekEDkZjAE8Zo4NyovjIEJBMggUaMm+PLBXP5L9RlwsAoz3VNaYB\niqi0OyiCQxAFUtUCZ2RkBFu2bMFbb70Fm82GlStX4qKLLoJb3yhLRzKZxEMPPYQ//vGP6O7uhtfr\nxbJly/D1r38djY3WB/BJQUs7cPBTo++lgh4cFijDvlBTVFRFlQvbFZvFaBAr5PcjGjZGeWQEx+sD\n9u4C9PMNlb45hgiFFDjSWyMnlVsh02UZKqhU/PViGG0VNflTcbszzoAjCCI/qtqD89Of/hQHDhzA\nNddcg3/+53/Gxx9/jLvuuivj8rFYDPv27cM555yDm266CVdffTUOHTqEm266qYxbPfFQq64sBU6N\nhsmVyA11Ms4NmzoDzJuhj4z8fpib/ckIzoJl6UZj+ZjbIkXVtV9MGW+dmnmDpOE5VwRHRm6qzYMD\ngC07Dmzu4kpvBkFUNVUrcA4cOIB3330XGzduxNy5c7Fw4UKsW7cO27dvx8DAgOVzvF4v/uVf/gUn\nnHACpk6dinnz5uFb3/oWdu/ejd7ecUxErhWmZBM4NXoVSSbj4iC/H6ZKKq7cZouOBg58KiaES2RZ\nucckcGJKBGdKK1i2/SLnVOXo8ssUgcOqLEUFALZvfRvsMydUejMIoqqpWoGzY8cO+Hw+zJkzR73v\n6KOPBmMMO3fuzHs9oVAIjDH4qqnTaZFhLW3iD73AqfVuqk7y4BQFKVLMvXAiYcDlButcIIzG+/cZ\nHwPSIzjxURHBacuSnoIyURzIXEElkctVY4qKIIiCqVqBMzAwgGDQGHq22Wzw+/0ZIzhm4vE4Hnzw\nQaxevTqjb2dS0KKkA/Qne2cFy8TLAXlwioOSouLmXjiRkPjudMwG6urA9+kuOmQ6y+TB4YoHh7V3\nZH9NX74CJ2D8nyCIScWEMxk/+OCD+NWvfpV1mVtvvbXg10kmk/jxj38MxhjWr19f8Pqqmimt4n99\nBKc+CMxZANYxx/o51Q7NoioOLrdoL2DuhRMJA26vSDVNmwns+7P6EI+mR3CY0wXe1yMm22czGANa\nZCbXIEoSOAQxqZlwAufss8/GmjVrsi7T1taGhoYGDA4OGu5PpVIYGRlBQ0OWElNo4qa3txfXXHNN\nXtGbl156CS+//HLadlx00UUIBAKif0cV09vUAruvHsEmXbXMf2yp3AaVmFjzFAwBaJjSgjrde3Y4\nHGhqylAxRFjS4/HCa2Pw6j634VQSifoAGpuaMDR3EZIH9qFReTxis2Gkrg5Nbe1qW4Lh+gBiH74N\nnkohMH8RnFn2QcphRy8Au8ulrtOK5GlnIdnYBGd7FsMyUXXQb7R2kL//++67D93d3YbHVq1ahdWr\nVxe0/gkncOrr61Ffn6Frqo4FCxYgFAphz549qg/n/fffB+cc8+dnnm4txc3hw4exefNm+P3+vLZr\n9erVGT/soaEhxK26tVYRqfYOxN0e9PX1VXpTygL3BYCmKRhIcjDde25qapo0n0Gx4E43wr29iOo+\nt+RAP+Bwoa+vDymnC3xoUP1cU709gMuD/v5+dfkUB3hoBAAw7Asa9kna66VSALMhAZZjX9nQdNoX\naX/WGPQbrR0cDgdaWlpw0UUXlWT9VevBmT59OlasWIGf/exn2LVrFz755BNs2bIFq1atMkRwrrzy\nSrzxxhsAhLi55ZZbsGfPHlx22WVIJBIYGBjAwMAAEolEpd7KhMC24Sqwc9ZVejPKBuuYjbp/31KZ\nTs21hsdrUSYeApP+La8fCI9oj0XD6d4uvecrW5M/KANZfb7cZeIEQUxqJlwEZyxcfvnluPfee3H9\n9derjf7WrTOepA8dOoRwWOT8+/r68NZbbwEArr76asNymzdvxpIlS8qz4ROQaiylJSYIVvOoImFg\nilKdpwgczrkISUcj6dV5UuC0Tc84fsGAL5DbZEwQxKSmqo8QPp8Pl19+edZlHn74YfXvlpYWw22C\nIIqA25M+UTyimxbu9QHJpOhU7HIbH5PI2We5Kqgk/noSOARBZIWOEARBFIbHC27ugxMNAx7RW4p5\n/WJeVWgEcLnFsmkpKqWaLVcFlQJrnCKqtwiCIDJAAocgiIJgLg/4YL/xTtkHBxARHED4cJqmAJEw\nmNdk7h9jBId9fWMhm0wQxCSABA5BEIVhMhnzRAIYHVUjOJBiJqwM4IxGgOZW4zqkByffCA55xgiC\nyAEJHIIgCsPtMVZRyTlUcoyDTxfBASxNxqxzIXDC53KOaSAIgsgXEjgEQRSG22s0GcuKKhnB8YgI\nDg+PgAGKwDF6cFhzK9jF3y79thIEMWmo2j44BEFMEDweIBbRunlHlFSU4sFhDocwEaspKosqKoIg\niCJDAocgiMJwe0QZeHxU3I6YIjiAoRcOohFtCjlBEESJIIFDEERBMJlukqkpUwQHgCJwQkAsCnAO\nuEjgEARRWkjgEARRGDLdpBiNeVhGcPQCxydMxlFpQDb1wSEIgigyJHAIgigMKVYiSiVVNAzY7WAO\np7aM1w8eDmnVVm4SOARBlBYSOARBFIaaolLESyRs9N9AdDNGaEQTQWQyJgiixJDAIQiiMNQUlc6D\nY05BeX3i/qhF+oogCKIEkMAhCKIwlAgOl9VTFhEcyAhOlCI4BEGUBxI4BEEUhtMJMJsxRWUWMD5h\nMubkwSEIokyQwCEIoiAYY6KvjZJ+4lYRHI8fGI0BI0NAnV00/yMIgighJHAIgigct1czEEdCaWXg\nTM6j6uuhJn8EQZQFEjgEQRSOW4xrACBSVF4LDw4A3neYmvwRBFEWSOAQBFE4Ht3ATatZU1Lw9B6h\nCiqCIMoCCRyCIArH5dEMxJmqqACg7wgZjAmCKAskcAiCKBzFZMxTKWWYprkPjiJwhgepRJwgiLJA\nAocgiIJhbi8w2C/EDefpERyXG7CJww3NoSIIohyQwCEIomDYZ04EDuwD/80T4ra5iooxLYpDERyC\nIMoACRyCIAqGLf8s2Ml/A/7/HhF3WEVpSOAQBFFGSOAQBFEU2HkXA+0d4oalwFHSViRwCIIoAyRw\nC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PMBOZvGyYMqNVx6vEKeh1T5m/j5uCtihIPoLWuv71SqviyMuGQ3tRX3/gqo5rMbaAQDNx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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list_prob5, \"loss\")\n", + "util.plot_curve(avg_return_list_prob5, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -477,7 +907,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -523,6 +953,7 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " a = util.discount(a, LAMBDA*self.discount_rate)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -543,99 +974,216 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { - "scrolled": true + "collapsed": false, + "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 25.12\n", - "Iteration 2: Average Return = 31.17\n", - "Iteration 3: Average Return = 30.07\n", - "Iteration 4: Average Return = 31.98\n", - "Iteration 5: Average Return = 36.77\n", - "Iteration 6: Average Return = 36.22\n", - "Iteration 7: Average Return = 43.52\n", - "Iteration 8: Average Return = 45.12\n", - "Iteration 9: Average Return = 50.86\n", - "Iteration 10: Average Return = 58.81\n", - "Iteration 11: Average Return = 58.87\n", - "Iteration 12: Average Return = 65.66\n", - "Iteration 13: Average Return = 69.72\n", - "Iteration 14: Average Return = 76.32\n", - "Iteration 15: Average Return = 77.74\n", - "Iteration 16: Average Return = 78.17\n", - "Iteration 17: Average Return = 94.97\n", - "Iteration 18: Average Return = 89.34\n", - "Iteration 19: Average Return = 98.15\n", - "Iteration 20: Average Return = 103.35\n", - "Iteration 21: Average Return = 106.54\n", - "Iteration 22: Average Return = 109.03\n", - "Iteration 23: Average Return = 113.63\n", - "Iteration 24: Average Return = 119.11\n", - "Iteration 25: Average Return = 115.67\n", - "Iteration 26: Average Return = 126.51\n", - "Iteration 27: Average Return = 131.33\n", - "Iteration 28: Average Return = 138.83\n", - "Iteration 29: Average Return = 143.7\n", - "Iteration 30: Average Return = 146.15\n", - "Iteration 31: Average Return = 146.41\n", - "Iteration 32: Average Return = 157.34\n", - "Iteration 33: Average Return = 160.51\n", - "Iteration 34: Average Return = 159.67\n", - "Iteration 35: Average Return = 169.42\n", - "Iteration 36: Average Return = 170.71\n", - "Iteration 37: Average Return = 174.41\n", - "Iteration 38: Average Return = 172.93\n", - "Iteration 39: Average Return = 173.29\n", - "Iteration 40: Average Return = 177.32\n", - "Iteration 41: Average Return = 177.14\n", - "Iteration 42: Average Return = 179.85\n", - "Iteration 43: Average Return = 181.82\n", - "Iteration 44: Average Return = 182.0\n", - "Iteration 45: Average Return = 181.89\n", - "Iteration 46: Average Return = 183.19\n", - "Iteration 47: Average Return = 183.87\n", - "Iteration 48: Average Return = 183.26\n", - "Iteration 49: Average Return = 183.27\n", - "Iteration 50: Average Return = 189.11\n", - "Iteration 51: Average Return = 181.45\n", - "Iteration 52: Average Return = 186.91\n", - "Iteration 53: Average Return = 188.84\n", - "Iteration 54: Average Return = 189.76\n", - "Iteration 55: Average Return = 189.51\n", - "Iteration 56: Average Return = 186.36\n", - "Iteration 57: Average Return = 190.55\n", - "Iteration 58: Average Return = 189.35\n", - "Iteration 59: Average Return = 189.84\n", - "Iteration 60: Average Return = 187.14\n", - "Iteration 61: Average Return = 191.82\n", - "Iteration 62: Average Return = 189.32\n", - "Iteration 63: Average Return = 190.74\n", - "Iteration 64: Average Return = 188.13\n", - "Iteration 65: Average Return = 190.99\n", - "Iteration 66: Average Return = 189.23\n", - "Iteration 67: Average Return = 186.98\n", - "Iteration 68: Average Return = 188.0\n", - "Iteration 69: Average Return = 191.68\n", - "Iteration 70: Average Return = 188.03\n", - "Iteration 71: Average Return = 193.07\n", - "Iteration 72: Average Return = 191.96\n", - "Iteration 73: Average Return = 189.53\n", - "Iteration 74: Average Return = 186.71\n", - "Iteration 75: Average Return = 190.05\n", - "Iteration 76: Average Return = 191.1\n", - "Iteration 77: Average Return = 193.49\n", - "Iteration 78: Average Return = 188.66\n", - "Iteration 79: Average Return = 191.49\n", - "Iteration 80: Average Return = 191.68\n", - "Iteration 81: Average Return = 193.19\n", - "Iteration 82: Average Return = 193.87\n", - "Iteration 83: Average Return = 195.04\n", - "Solve at 83 iterations, which equals 8300 episodes.\n" + "Iteration 1: Average Return = 15.88\n", + "Iteration 2: Average Return = 17.47\n", + "Iteration 3: Average Return = 15.66\n", + "Iteration 4: Average Return = 17.94\n", + "Iteration 5: Average Return = 18.67\n", + "Iteration 6: Average Return = 19.6\n", + "Iteration 7: Average Return = 23.39\n", + "Iteration 8: Average Return = 23.64\n", + "Iteration 9: Average Return = 28.31\n", + "Iteration 10: Average Return = 24.77\n", + "Iteration 11: Average Return = 27.65\n", + "Iteration 12: Average Return = 30.64\n", + "Iteration 13: Average Return = 33.44\n", + "Iteration 14: Average Return = 34.33\n", + "Iteration 15: Average Return = 39.15\n", + "Iteration 16: Average Return = 38.38\n", + "Iteration 17: Average Return = 38.77\n", + "Iteration 18: Average Return = 39.82\n", + "Iteration 19: Average Return = 42.74\n", + "Iteration 20: Average Return = 43.78\n", + "Iteration 21: Average Return = 48.76\n", + "Iteration 22: Average Return = 50.2\n", + "Iteration 23: Average Return = 51.55\n", + "Iteration 24: Average Return = 51.3\n", + "Iteration 25: Average Return = 55.23\n", + "Iteration 26: Average Return = 54.92\n", + "Iteration 27: Average Return = 59.14\n", + "Iteration 28: Average Return = 56.96\n", + "Iteration 29: Average Return = 62.91\n", + "Iteration 30: Average Return = 59.64\n", + "Iteration 31: Average Return = 66.18\n", + "Iteration 32: Average Return = 67.3\n", + "Iteration 33: Average Return = 71.99\n", + "Iteration 34: Average Return = 67.11\n", + "Iteration 35: Average Return = 71.5\n", + "Iteration 36: Average Return = 74.11\n", + "Iteration 37: Average Return = 78.79\n", + "Iteration 38: Average Return = 83.58\n", + "Iteration 39: Average Return = 92.21\n", + "Iteration 40: Average Return = 100.07\n", + "Iteration 41: Average Return = 114.39\n", + "Iteration 42: Average Return = 112.04\n", + "Iteration 43: Average Return = 113.59\n", + "Iteration 44: Average Return = 110.39\n", + "Iteration 45: Average Return = 113.58\n", + "Iteration 46: Average Return = 126.39\n", + "Iteration 47: Average Return = 126.63\n", + "Iteration 48: Average Return = 125.95\n", + "Iteration 49: Average Return = 127.56\n", + "Iteration 50: Average Return = 133.91\n", + "Iteration 51: Average Return = 139.48\n", + "Iteration 52: Average Return = 138.17\n", + "Iteration 53: Average Return = 149.69\n", + "Iteration 54: Average Return = 153.67\n", + "Iteration 55: Average Return = 156.83\n", + "Iteration 56: Average Return = 153.85\n", + "Iteration 57: Average Return = 159.9\n", + "Iteration 58: Average Return = 164.29\n", + "Iteration 59: Average Return = 163.28\n", + "Iteration 60: Average Return = 163.77\n", + "Iteration 61: Average Return = 166.5\n", + "Iteration 62: Average Return = 173.83\n", + "Iteration 63: Average Return = 156.3\n", + "Iteration 64: Average Return = 166.32\n", + "Iteration 65: Average Return = 163.62\n", + "Iteration 66: Average Return = 160.52\n", + "Iteration 67: Average Return = 164.82\n", + "Iteration 68: Average Return = 175.67\n", + "Iteration 69: Average Return = 168.92\n", + "Iteration 70: Average Return = 177.7\n", + "Iteration 71: Average Return = 184.95\n", + "Iteration 72: Average Return = 172.02\n", + "Iteration 73: Average Return = 179.71\n", + "Iteration 74: Average Return = 184.55\n", + "Iteration 75: Average Return = 187.1\n", + "Iteration 76: Average Return = 182.99\n", + "Iteration 77: Average Return = 179.12\n", + "Iteration 78: Average Return = 185.82\n", + "Iteration 79: Average Return = 185.08\n", + "Iteration 80: Average Return = 188.61\n", + "Iteration 81: Average Return = 190.15\n", + "Iteration 82: Average Return = 187.36\n", + "Iteration 83: Average Return = 189.77\n", + "Iteration 84: Average Return = 189.45\n", + "Iteration 85: Average Return = 193.39\n", + "Iteration 86: Average Return = 189.31\n", + "Iteration 87: Average Return = 189.57\n", + "Iteration 88: Average Return = 185.35\n", + "Iteration 89: Average Return = 192.96\n", + "Iteration 90: Average Return = 186.98\n", + "Iteration 91: Average Return = 190.34\n", + "Iteration 92: Average Return = 189.92\n", + "Iteration 93: Average Return = 191.53\n", + "Iteration 94: Average Return = 192.21\n", + "Iteration 95: Average Return = 192.78\n", + "Iteration 96: Average Return = 188.44\n", + "Iteration 97: Average Return = 187.94\n", + "Iteration 98: Average Return = 192.23\n", + "Iteration 99: Average Return = 189.92\n", + "Iteration 100: Average Return = 191.52\n", + "Iteration 101: Average Return = 191.95\n", + "Iteration 102: Average Return = 187.57\n", + "Iteration 103: Average Return = 192.81\n", + "Iteration 104: Average Return = 192.45\n", + "Iteration 105: Average Return = 189.81\n", + "Iteration 106: Average Return = 186.44\n", + "Iteration 107: Average Return = 189.75\n", + "Iteration 108: Average Return = 186.55\n", + "Iteration 109: Average Return = 192.98\n", + "Iteration 110: Average Return = 189.57\n", + "Iteration 111: Average Return = 188.49\n", + "Iteration 112: Average Return = 190.14\n", + "Iteration 113: Average Return = 188.33\n", + "Iteration 114: Average Return = 190.31\n", + "Iteration 115: Average Return = 191.51\n", + "Iteration 116: Average Return = 191.15\n", + "Iteration 117: Average Return = 185.53\n", + "Iteration 118: Average Return = 187.79\n", + "Iteration 119: Average Return = 190.49\n", + "Iteration 120: Average Return = 185.26\n", + "Iteration 121: Average Return = 189.35\n", + "Iteration 122: Average Return = 188.66\n", + "Iteration 123: Average Return = 188.54\n", + "Iteration 124: Average Return = 190.59\n", + "Iteration 125: Average Return = 188.2\n", + "Iteration 126: Average Return = 188.53\n", + "Iteration 127: Average Return = 187.0\n", + "Iteration 128: Average Return = 186.78\n", + "Iteration 129: Average Return = 188.62\n", + "Iteration 130: Average Return = 193.38\n", + "Iteration 131: Average Return = 190.85\n", + "Iteration 132: Average Return = 184.6\n", + "Iteration 133: Average Return = 188.73\n", + "Iteration 134: Average Return = 191.41\n", + "Iteration 135: Average Return = 188.19\n", + "Iteration 136: Average Return = 194.02\n", + "Iteration 137: Average Return = 187.84\n", + "Iteration 138: Average Return = 190.8\n", + "Iteration 139: Average Return = 191.46\n", + "Iteration 140: Average Return = 191.8\n", + "Iteration 141: Average Return = 188.58\n", + "Iteration 142: Average Return = 190.76\n", + "Iteration 143: Average Return = 187.65\n", + "Iteration 144: Average Return = 187.95\n", + "Iteration 145: Average Return = 190.06\n", + "Iteration 146: Average Return = 190.64\n", + "Iteration 147: Average Return = 190.55\n", + "Iteration 148: Average Return = 189.19\n", + "Iteration 149: Average Return = 190.44\n", + "Iteration 150: Average Return = 185.79\n", + "Iteration 151: Average Return = 186.49\n", + "Iteration 152: Average Return = 187.2\n", + "Iteration 153: Average Return = 188.6\n", + "Iteration 154: Average Return = 190.33\n", + "Iteration 155: Average Return = 188.79\n", + "Iteration 156: Average Return = 186.45\n", + "Iteration 157: Average Return = 189.07\n", + "Iteration 158: Average Return = 187.54\n", + "Iteration 159: Average Return = 188.07\n", + "Iteration 160: Average Return = 184.77\n", + "Iteration 161: Average Return = 188.89\n", + "Iteration 162: Average Return = 187.53\n", + "Iteration 163: Average Return = 190.9\n", + "Iteration 164: Average Return = 188.56\n", + "Iteration 165: Average Return = 189.33\n", + "Iteration 166: Average Return = 190.88\n", + "Iteration 167: Average Return = 188.61\n", + "Iteration 168: Average Return = 183.21\n", + "Iteration 169: Average Return = 187.65\n", + "Iteration 170: Average Return = 189.46\n", + "Iteration 171: Average Return = 184.52\n", + "Iteration 172: Average Return = 186.57\n", + "Iteration 173: Average Return = 187.26\n", + "Iteration 174: Average Return = 183.66\n", + "Iteration 175: Average Return = 183.15\n", + "Iteration 176: Average Return = 186.09\n", + "Iteration 177: Average Return = 180.37\n", + "Iteration 178: Average Return = 182.64\n", + "Iteration 179: Average Return = 182.81\n", + "Iteration 180: Average Return = 187.23\n", + "Iteration 181: Average Return = 180.48\n", + "Iteration 182: Average Return = 184.36\n", + "Iteration 183: Average Return = 183.9\n", + "Iteration 184: Average Return = 187.13\n", + "Iteration 185: Average Return = 189.34\n", + "Iteration 186: Average Return = 191.11\n", + "Iteration 187: Average Return = 191.74\n", + "Iteration 188: Average Return = 190.09\n", + "Iteration 189: Average Return = 189.77\n", + "Iteration 190: Average Return = 189.39\n", + "Iteration 191: Average Return = 188.62\n", + "Iteration 192: Average Return = 191.42\n", + "Iteration 193: Average Return = 186.4\n", + "Iteration 194: Average Return = 188.03\n", + "Iteration 195: Average Return = 187.65\n", + "Iteration 196: Average Return = 188.84\n", + "Iteration 197: Average Return = 191.47\n", + "Iteration 198: Average Return = 184.26\n", + "Iteration 199: Average Return = 189.8\n", + "Iteration 200: Average Return = 186.62\n" ] } ], @@ -653,19 +1201,21 @@ " discount_rate)\n", "\n", "# Train the policy optimizer\n", - "loss_list, avg_return_list = po.train()" + "loss_list_prob6, avg_return_list_prob6 = po.train()" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": {}, + "execution_count": 16, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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QQghx0dx6BAhg6NCh5Ofns3jxYkchxBkzZhAQEABYSc9ZWVmO9uHh4UyfPp13332X5cuX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\n9gId6b+0tDSCgoI4duyYr05BQQGlpaWMHDmyy9ssV+/ChQtUVlb6frG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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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E0MsfYJLP9h5n+l6O59nX8bz6Eea8vth537t/vDatg4TWbrdQYjvskgXuN/4LLsXUqeu2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\nfu9hwIABvPbaa3zwwQfs3r2bTZs2UVxcTHZ2NnfeeWen1Cl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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -683,10 +1233,101 @@ } ], "source": [ - "util.plot_curve(loss_list, \"loss\")\n", - "util.plot_curve(avg_return_list, \"average return\")" + "util.plot_curve(loss_list_prob6, \"loss\")\n", + "util.plot_curve(avg_return_list_prob6, \"average return\")" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sFh544AFiYmIG/Bb0pdlenl1XRrtqIDfR/8FpzRpL7rFjHFH1eD0+uu2RJCYb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\nQkgWUEQRQgghhGQBRRQhhBBCSBZQRBFCC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k4cKFyoSn1WpZtmwZ7733Hrm5ucyYMYOCggKysrJob29n27ZtxMXFKW6lVatW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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "loss_dict = {\n", + " 'prob3': loss_list_prob3,\n", + " 'prob4': loss_list_prob4,\n", + " 'prob5': loss_list_prob5,\n", + " 'prob6': loss_list_prob6,\n", + "}\n", + "util.plot_curves(loss_dict, \"all_loss\")\n", + "\n", + "avg_return_dict = {\n", + " 'prob3': avg_return_list_prob3,\n", + " 'prob4': avg_return_list_prob4,\n", + " 'prob5': avg_return_list_prob5,\n", + " 'prob6': avg_return_list_prob6,\n", + "}\n", + "util.plot_curves(avg_return_dict, \"all_average_return\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, @@ -698,8 +1339,9 @@ } ], "metadata": { + "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python [default]", "language": "python", "name": "python3" }, @@ -713,7 +1355,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 99fecf3..da0d8ca 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -1,4 +1,5 @@ import tensorflow as tf +import tensorflow.contrib.slim as slim import numpy as np # In this Lab, we just use categorical policy, which used for MDPs with discrete action space @@ -30,6 +31,8 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> + x = slim.fully_connected(self._observations, hidden_dim, activation_fn=tf.nn.tanh, scope='policy/fc_1') + probs = slim.fully_connected(x, out_dim, activation_fn=tf.nn.softmax, scope='policy/fc_2') # <<<<<<<< # -------------------------------------------------- @@ -72,6 +75,7 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 1~3 lines. """ # YOUR CODE HERE >>>>>> + surr_loss = - tf.reduce_mean(log_prob * self._advantages) # <<<<<<<< grads_and_vars = self._opt.compute_gradients(surr_loss) diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..43d5780 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -27,11 +27,14 @@ def discount_bootstrap(x, discount_rate, b): x: the immediate reward for each timestep. e.g. [1, 1, 0] discount_factor: the \gamma in standard reinforcement learning b: the prediction of the baseline. e.g. [1.3, 0.4, 0.2] - Returns: a numpy array y = r(s_t,a,s_{t+1}) + \gamma*V_t + Returns: a numpy array y = r(s_t,a,s_{t+1}) + \gamma*V_t (the shape of it should be the same as the `x` and `b`) Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> + left_shift_arr = lambda arr, shift: np.hstack([arr[shift:], [0]*shift]) + # return x + discount_rate * left_shift_arr(b, 1) + return x + discount_rate * left_shift_arr(b, 1) # <<<<<<<<<<<<<<<<<<<<<<<<<<<< def plot_curve(data, key, filename=None): @@ -45,6 +48,19 @@ def plot_curve(data, key, filename=None): plt.show() plt.close() +def plot_curves(data_dict, key, filename=None): + # plot the surrogate loss curve + for data_label in data_dict: + x = np.arange(len(data_dict[data_label])) + plt.plot(x, data_dict[data_label]) + plt.text(x[-1], data_dict[data_label][-1], data_label) + plt.xlabel("iterations") + plt.ylabel(key) + if filename is not None: + plt.savefig(filename) + plt.show() + plt.close() + def discount(x, discount_factor): return scipy.signal.lfilter([1.0], [1.0, -discount_factor], x[::-1])[::-1] diff --git a/prob3.png b/prob3.png new file mode 100644 index 0000000..ac45785 Binary files /dev/null and b/prob3.png differ diff --git a/prob4.png b/prob4.png new file mode 100644 index 0000000..850bb15 Binary files /dev/null and b/prob4.png differ diff --git a/prob4_bias.png b/prob4_bias.png new file mode 100644 index 0000000..e0cfff0 Binary files /dev/null and b/prob4_bias.png differ diff --git a/prob5.png b/prob5.png new file mode 100644 index 0000000..b74866f Binary files /dev/null and b/prob5.png differ diff --git a/prob6.png b/prob6.png new file mode 100644 index 0000000..b88ff60 Binary files /dev/null and b/prob6.png differ diff --git a/report.md b/report.md index 1e5017e..99a70ec 100644 --- a/report.md +++ b/report.md @@ -1,3 +1,49 @@ # Homework3-Policy-Gradient report -TA: try to elaborate the algorithms that you implemented and any details worth mentioned. +## Problem 1 + +Basically just use Tensorflow construct 2 fc layers, and set activation functions to tanh and softmax. I use slim here for simplicity. + +## Problem 2 + +Simply multiply the calculated log probability with the advantage. And use it to compute loss. +The only thing iportant here is that we want to maximize the surrogate loss, so we need to add a negative sign to the loss tensor, fulfilling the optimizer's minimization objective. + +## Problem 3 + +Simply calculating advantages by subtracting reward with predicted baseline. + + + +## Problem 4 + +Comparing against plot in problem 3. The variance in loss plot becomes relatively large. + + + +About the proof of unbias: + + +Basically is because the expectation of gradient of log-probability. The gradient itself can be moved in front of summation. And since the summation result is 1, its gradient is zero, thus it is unbiased. + +## Problem 5 + +Here we implement the actor-critic strategy to replace original one. + +But the loss here can't converge very well and tends to have a long-tail training. Possibly current implementation is still too naive. + + + +## Problem 6 + +We use GAE in this example, its a kind of compromise between our original strategy and actor-critic. And the result is totally as we expected, its a smooth form between two approaches. + + + +## Plot everything togather + +This plot can make it more clear to compare between prob 3/4 and 3/5/6. + + + + diff --git a/total.png b/total.png new file mode 100644 index 0000000..a75548c Binary files /dev/null and b/total.png differ