From c8a97bd487cd8c5f0f54645eaa9affdacd070582 Mon Sep 17 00:00:00 2001 From: petersci Date: Mon, 30 Oct 2017 00:04:08 +0800 Subject: [PATCH 01/20] Update policy.py --- policy_gradient/policy.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index 99fecf3..b99a7d0 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -30,6 +30,8 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 2~4 lines. """ # YOUR CODE HERE >>>>>> + fc = tf.layers.dense(self._observations, hidden_dim, tf.tanh) + probs = tf.layers.dense(fc, out_dim, tf.nn.softmax) # <<<<<<<< # -------------------------------------------------- From c4573f4c85c841166e33199e7ea3319154bae311 Mon Sep 17 00:00:00 2001 From: petersci Date: Mon, 30 Oct 2017 04:12:44 +0800 Subject: [PATCH 02/20] Update policy.py --- Lab3-policy-gradient.ipynb | 226 +++++++++++++++++++------------------ policy_gradient/policy.py | 1 + 2 files changed, 117 insertions(+), 110 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..cedda28 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -2,11 +2,18 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "# Automatically reload changes to external code\n", "%load_ext autoreload\n", @@ -28,17 +35,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -103,14 +102,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, "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", + "C:\\Users\\user\\Anaconda3\\envs\\gym\\lib\\site-packages\\tensorflow\\python\\ops\\gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] } @@ -152,7 +151,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "metadata": { "collapsed": true }, @@ -214,6 +213,7 @@ " 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 +258,104 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 35, "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 = 18.68\n", + "Iteration 2: Average Return = 21.27\n", + "Iteration 3: Average Return = 19.61\n", + "Iteration 4: Average Return = 20.3\n", + "Iteration 5: Average Return = 21.19\n", + "Iteration 6: Average Return = 23.29\n", + "Iteration 7: Average Return = 25.92\n", + "Iteration 8: Average Return = 27.26\n", + "Iteration 9: Average Return = 30.09\n", + "Iteration 10: Average Return = 30.73\n", + "Iteration 11: Average Return = 30.69\n", + "Iteration 12: Average Return = 31.96\n", + "Iteration 13: Average Return = 31.6\n", + "Iteration 14: Average Return = 33.19\n", + "Iteration 15: Average Return = 38.17\n", + "Iteration 16: Average Return = 35.84\n", + "Iteration 17: Average Return = 38.28\n", + "Iteration 18: Average Return = 36.32\n", + "Iteration 19: Average Return = 38.41\n", + "Iteration 20: Average Return = 43.0\n", + "Iteration 21: Average Return = 46.69\n", + "Iteration 22: Average Return = 41.24\n", + "Iteration 23: Average Return = 42.39\n", + "Iteration 24: Average Return = 43.54\n", + "Iteration 25: Average Return = 47.97\n", + "Iteration 26: Average Return = 47.15\n", + "Iteration 27: Average Return = 48.98\n", + "Iteration 28: Average Return = 51.27\n", + "Iteration 29: Average Return = 49.18\n", + "Iteration 30: Average Return = 51.74\n", + "Iteration 31: Average Return = 50.5\n", + "Iteration 32: Average Return = 54.61\n", + "Iteration 33: Average Return = 60.19\n", + "Iteration 34: Average Return = 55.1\n", + "Iteration 35: Average Return = 55.57\n", + "Iteration 36: Average Return = 58.26\n", + "Iteration 37: Average Return = 55.79\n", + "Iteration 38: Average Return = 56.52\n", + "Iteration 39: Average Return = 59.0\n", + "Iteration 40: Average Return = 60.85\n", + "Iteration 41: Average Return = 57.51\n", + "Iteration 42: Average Return = 63.47\n", + "Iteration 43: Average Return = 63.75\n", + "Iteration 44: Average Return = 65.08\n", + "Iteration 45: Average Return = 61.49\n", + "Iteration 46: Average Return = 69.36\n", + "Iteration 47: Average Return = 65.39\n", + "Iteration 48: Average Return = 64.68\n", + "Iteration 49: Average Return = 67.54\n", + "Iteration 50: Average Return = 62.69\n", + "Iteration 51: Average Return = 69.14\n", + "Iteration 52: Average Return = 71.56\n", + "Iteration 53: Average Return = 75.62\n", + "Iteration 54: Average Return = 77.19\n", + "Iteration 55: Average Return = 79.56\n", + "Iteration 56: Average Return = 79.62\n", + "Iteration 57: Average Return = 79.34\n", + "Iteration 58: Average Return = 81.3\n", + "Iteration 59: Average Return = 85.27\n", + "Iteration 60: Average Return = 84.41\n", + "Iteration 61: Average Return = 86.48\n", + "Iteration 62: Average Return = 89.69\n", + "Iteration 63: Average Return = 98.28\n", + "Iteration 64: Average Return = 110.61\n", + "Iteration 65: Average Return = 105.89\n", + "Iteration 66: Average Return = 104.28\n", + "Iteration 67: Average Return = 118.64\n", + "Iteration 68: Average Return = 139.39\n", + "Iteration 69: Average Return = 144.17\n", + "Iteration 70: Average Return = 144.88\n", + "Iteration 71: Average Return = 163.18\n", + "Iteration 72: Average Return = 173.54\n", + "Iteration 73: Average Return = 180.01\n", + "Iteration 74: Average Return = 171.04\n", + "Iteration 75: Average Return = 173.86\n", + "Iteration 76: Average Return = 167.16\n", + "Iteration 77: Average Return = 163.29\n", + "Iteration 78: Average Return = 170.94\n", + "Iteration 79: Average Return = 174.21\n", + "Iteration 80: Average Return = 165.81\n", + "Iteration 81: Average Return = 169.57\n", + "Iteration 82: Average Return = 170.37\n", + "Iteration 83: Average Return = 172.33\n", + "Iteration 84: Average Return = 176.04\n", + "Iteration 85: Average Return = 176.06\n", + "Iteration 86: Average Return = 182.87\n", + "Iteration 87: Average Return = 183.52\n", + "Iteration 88: Average Return = 185.36\n", + "Iteration 89: Average Return = 188.72\n", + "Iteration 90: Average Return = 196.75\n", + "Solve at 90 iterations, which equals 9000 episodes.\n" ] } ], @@ -371,14 +377,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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JST4EYExTDW5U4TzzcNzyJvrGv0OF6kXfCX3yqHlhz0If2tvt2G6vnWGDTDu6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\n48UXX+T222+nsbERgLq6OlJSUkhISADAbrfjcrn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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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pKaFr164EBMh0wEII99ElxXBwD/rgHvS+naC1dJv1AJeSxf79+1m0aBH+MlpSCOFGWmvM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\npVSFEJ6liy9hLpw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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -713,7 +719,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.3" + "version": "3.5.4" } }, "nbformat": 4, diff --git a/policy_gradient/policy.py b/policy_gradient/policy.py index b99a7d0..c91ddcb 100644 --- a/policy_gradient/policy.py +++ b/policy_gradient/policy.py @@ -74,6 +74,7 @@ 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) From b249e95c84522082050b1dc14e4b9328f1f908e4 Mon Sep 17 00:00:00 2001 From: petersci Date: Mon, 30 Oct 2017 16:06:36 +0800 Subject: [PATCH 03/20] Update policy-gradient.ipynb --- Lab3-policy-gradient.ipynb | 208 +++++++++++++++++++------------------ 1 file changed, 106 insertions(+), 102 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index cedda28..9c23b81 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 27, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -35,8 +35,10 @@ }, { "cell_type": "code", - "execution_count": 28, - "metadata": {}, + "execution_count": 16, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "import gym\n", @@ -102,9 +104,16 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 17, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Tensor(\"policy/Shape_2:0\", shape=(?,), dtype=int32)\n" + ] + }, { "name": "stderr", "output_type": "stream", @@ -151,7 +160,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -258,104 +267,98 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 18.68\n", - "Iteration 2: Average Return = 21.27\n", - "Iteration 3: Average Return = 19.61\n", - "Iteration 4: Average Return = 20.3\n", - "Iteration 5: Average Return = 21.19\n", - "Iteration 6: Average Return = 23.29\n", - "Iteration 7: Average Return = 25.92\n", - "Iteration 8: Average Return = 27.26\n", - "Iteration 9: Average Return = 30.09\n", - "Iteration 10: Average Return = 30.73\n", - "Iteration 11: Average Return = 30.69\n", - "Iteration 12: Average Return = 31.96\n", - "Iteration 13: Average Return = 31.6\n", - "Iteration 14: Average Return = 33.19\n", - "Iteration 15: Average Return = 38.17\n", - "Iteration 16: Average Return = 35.84\n", - "Iteration 17: Average Return = 38.28\n", - "Iteration 18: Average Return = 36.32\n", - "Iteration 19: Average Return = 38.41\n", - "Iteration 20: Average Return = 43.0\n", - "Iteration 21: Average Return = 46.69\n", - "Iteration 22: Average Return = 41.24\n", - "Iteration 23: Average Return = 42.39\n", - "Iteration 24: Average Return = 43.54\n", - "Iteration 25: Average Return = 47.97\n", - "Iteration 26: Average Return = 47.15\n", - "Iteration 27: Average Return = 48.98\n", - "Iteration 28: Average Return = 51.27\n", - "Iteration 29: Average Return = 49.18\n", - "Iteration 30: Average Return = 51.74\n", - "Iteration 31: Average Return = 50.5\n", - "Iteration 32: Average Return = 54.61\n", - "Iteration 33: Average Return = 60.19\n", - "Iteration 34: Average Return = 55.1\n", - "Iteration 35: Average Return = 55.57\n", - "Iteration 36: Average Return = 58.26\n", - "Iteration 37: Average Return = 55.79\n", - "Iteration 38: Average Return = 56.52\n", - "Iteration 39: Average Return = 59.0\n", - "Iteration 40: Average Return = 60.85\n", - "Iteration 41: Average Return = 57.51\n", - "Iteration 42: Average Return = 63.47\n", - "Iteration 43: Average Return = 63.75\n", - "Iteration 44: Average Return = 65.08\n", - "Iteration 45: Average Return = 61.49\n", - "Iteration 46: Average Return = 69.36\n", - "Iteration 47: Average Return = 65.39\n", - "Iteration 48: Average Return = 64.68\n", - "Iteration 49: Average Return = 67.54\n", - "Iteration 50: Average Return = 62.69\n", - "Iteration 51: Average Return = 69.14\n", - "Iteration 52: Average Return = 71.56\n", - "Iteration 53: Average Return = 75.62\n", - "Iteration 54: Average Return = 77.19\n", - "Iteration 55: Average Return = 79.56\n", - "Iteration 56: Average Return = 79.62\n", - "Iteration 57: Average Return = 79.34\n", - "Iteration 58: Average Return = 81.3\n", - "Iteration 59: Average Return = 85.27\n", - "Iteration 60: Average Return = 84.41\n", - "Iteration 61: Average Return = 86.48\n", - "Iteration 62: Average Return = 89.69\n", - "Iteration 63: Average Return = 98.28\n", - "Iteration 64: Average Return = 110.61\n", - "Iteration 65: Average Return = 105.89\n", - "Iteration 66: Average Return = 104.28\n", - "Iteration 67: Average Return = 118.64\n", - "Iteration 68: Average Return = 139.39\n", - "Iteration 69: Average Return = 144.17\n", - "Iteration 70: Average Return = 144.88\n", - "Iteration 71: Average Return = 163.18\n", - "Iteration 72: Average Return = 173.54\n", - "Iteration 73: Average Return = 180.01\n", - "Iteration 74: Average Return = 171.04\n", - "Iteration 75: Average Return = 173.86\n", - "Iteration 76: Average Return = 167.16\n", - "Iteration 77: Average Return = 163.29\n", - "Iteration 78: Average Return = 170.94\n", - "Iteration 79: Average Return = 174.21\n", - "Iteration 80: Average Return = 165.81\n", - "Iteration 81: Average Return = 169.57\n", - "Iteration 82: Average Return = 170.37\n", - "Iteration 83: Average Return = 172.33\n", - "Iteration 84: Average Return = 176.04\n", - "Iteration 85: Average Return = 176.06\n", - "Iteration 86: Average Return = 182.87\n", - "Iteration 87: Average Return = 183.52\n", - "Iteration 88: Average Return = 185.36\n", - "Iteration 89: Average Return = 188.72\n", - "Iteration 90: Average Return = 196.75\n", - "Solve at 90 iterations, which equals 9000 episodes.\n" + "Iteration 1: Average Return = 34.5\n", + "Iteration 2: Average Return = 37.64\n", + "Iteration 3: Average Return = 36.2\n", + "Iteration 4: Average Return = 45.2\n", + "Iteration 5: Average Return = 41.93\n", + "Iteration 6: Average Return = 43.53\n", + "Iteration 7: Average Return = 48.56\n", + "Iteration 8: Average Return = 45.79\n", + "Iteration 9: Average Return = 48.36\n", + "Iteration 10: Average Return = 43.05\n", + "Iteration 11: Average Return = 51.26\n", + "Iteration 12: Average Return = 46.86\n", + "Iteration 13: Average Return = 53.2\n", + "Iteration 14: Average Return = 56.48\n", + "Iteration 15: Average Return = 55.62\n", + "Iteration 16: Average Return = 56.89\n", + "Iteration 17: Average Return = 56.79\n", + "Iteration 18: Average Return = 57.64\n", + "Iteration 19: Average Return = 61.93\n", + "Iteration 20: Average Return = 58.46\n", + "Iteration 21: Average Return = 64.15\n", + "Iteration 22: Average Return = 63.68\n", + "Iteration 23: Average Return = 66.58\n", + "Iteration 24: Average Return = 68.6\n", + "Iteration 25: Average Return = 67.22\n", + "Iteration 26: Average Return = 75.44\n", + "Iteration 27: Average Return = 77.7\n", + "Iteration 28: Average Return = 81.18\n", + "Iteration 29: Average Return = 82.95\n", + "Iteration 30: Average Return = 83.54\n", + "Iteration 31: Average Return = 89.02\n", + "Iteration 32: Average Return = 93.37\n", + "Iteration 33: Average Return = 104.44\n", + "Iteration 34: Average Return = 95.89\n", + "Iteration 35: Average Return = 101.77\n", + "Iteration 36: Average Return = 103.66\n", + "Iteration 37: Average Return = 116.72\n", + "Iteration 38: Average Return = 112.51\n", + "Iteration 39: Average Return = 116.54\n", + "Iteration 40: Average Return = 119.32\n", + "Iteration 41: Average Return = 114.08\n", + "Iteration 42: Average Return = 123.51\n", + "Iteration 43: Average Return = 125.28\n", + "Iteration 44: Average Return = 128.97\n", + "Iteration 45: Average Return = 134.09\n", + "Iteration 46: Average Return = 131.12\n", + "Iteration 47: Average Return = 134.75\n", + "Iteration 48: Average Return = 139.27\n", + "Iteration 49: Average Return = 144.41\n", + "Iteration 50: Average Return = 139.04\n", + "Iteration 51: Average Return = 144.19\n", + "Iteration 52: Average Return = 144.14\n", + "Iteration 53: Average Return = 159.08\n", + "Iteration 54: Average Return = 151.94\n", + "Iteration 55: Average Return = 168.5\n", + "Iteration 56: Average Return = 162.3\n", + "Iteration 57: Average Return = 166.97\n", + "Iteration 58: Average Return = 179.09\n", + "Iteration 59: Average Return = 179.04\n", + "Iteration 60: Average Return = 178.96\n", + "Iteration 61: Average Return = 182.56\n", + "Iteration 62: Average Return = 185.17\n", + "Iteration 63: Average Return = 186.02\n", + "Iteration 64: Average Return = 186.0\n", + "Iteration 65: Average Return = 185.88\n", + "Iteration 66: Average Return = 181.67\n", + "Iteration 67: Average Return = 184.82\n", + "Iteration 68: Average Return = 184.94\n", + "Iteration 69: Average Return = 190.33\n", + "Iteration 70: Average Return = 187.66\n", + "Iteration 71: Average Return = 185.15\n", + "Iteration 72: Average Return = 186.49\n", + "Iteration 73: Average Return = 185.22\n", + "Iteration 74: Average Return = 182.63\n", + "Iteration 75: Average Return = 181.68\n", + "Iteration 76: Average Return = 183.47\n", + "Iteration 77: Average Return = 183.0\n", + "Iteration 78: Average Return = 178.73\n", + "Iteration 79: Average Return = 182.86\n", + "Iteration 80: Average Return = 186.57\n", + "Iteration 81: Average Return = 187.34\n", + "Iteration 82: Average Return = 191.8\n", + "Iteration 83: Average Return = 194.14\n", + "Iteration 84: Average Return = 196.34\n", + "Solve at 84 iterations, which equals 8400 episodes.\n" ] } ], @@ -367,6 +370,7 @@ "path_length = 200\n", "discount_rate = 0.99\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", @@ -377,14 +381,14 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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JST4EYExTDW5U4TzzcNzyJvrGv0OF6kXfCX3yqHlhz0If2tvt2G6vnWGDTDu6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\n48UXX+T222+nsbERgLq6OlJSUkhISADAbrfjcrn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Gv4OdeiUBFVMxNbQGzEvKdFFdXLltJ+U5xKDNRxE4XTrQx/7OhoVQJlW+S3GN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\nW7u5p2M3vMZX6akUqF53TQPSy4yOEu40Ym1p6f7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XAMvPB/3x/1dq9Dlq0IDN8b6PgFxg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\n8JlOZWUlBQUFVFRUUFBQQGVlZdQxXq+XvXv3snHjRjZu3MjevXvxer0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FRRcwHv+t9HASTlxKFpGRkRw6dMhp28GDB4mKiqqUoIQQ7qX378b80zNwMB01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\n", 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K99ntdkJCQk77PaKjo4mOjq54nZWVVa1YbDZbtet6Emkn1+pTGzm++xoyDpL1n3+jOnQB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\n81794OBg8vLyrAyzQdOFBZh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"text/plain": [ - "" + "" ] }, "metadata": {}, From a8de732d8bdd523a93b9b4112ee502d2a2a01f68 Mon Sep 17 00:00:00 2001 From: petersci Date: Tue, 7 Nov 2017 16:01:50 +0800 Subject: [PATCH 04/20] Update Lab3-policy-gradient.ipynb --- Lab3-policy-gradient.ipynb | 324 +++++++++++++++++++++++++------------ 1 file changed, 221 insertions(+), 103 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 9c23b81..58be33f 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 15, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -35,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 6, "metadata": { "collapsed": true }, @@ -104,16 +104,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 7, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Tensor(\"policy/Shape_2:0\", shape=(?,), dtype=int32)\n" - ] - }, { "name": "stderr", "output_type": "stream", @@ -160,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": { "collapsed": true }, @@ -267,98 +260,82 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 34.5\n", - "Iteration 2: Average Return = 37.64\n", - "Iteration 3: Average Return = 36.2\n", - "Iteration 4: Average Return = 45.2\n", - "Iteration 5: Average Return = 41.93\n", - "Iteration 6: Average Return = 43.53\n", - "Iteration 7: Average Return = 48.56\n", - "Iteration 8: Average Return = 45.79\n", - "Iteration 9: Average Return = 48.36\n", - "Iteration 10: Average Return = 43.05\n", - "Iteration 11: Average Return = 51.26\n", - "Iteration 12: Average Return = 46.86\n", - "Iteration 13: Average Return = 53.2\n", - "Iteration 14: Average Return = 56.48\n", - "Iteration 15: Average Return = 55.62\n", - "Iteration 16: Average Return = 56.89\n", - "Iteration 17: Average Return = 56.79\n", - "Iteration 18: Average Return = 57.64\n", - "Iteration 19: Average Return = 61.93\n", - "Iteration 20: Average Return = 58.46\n", - "Iteration 21: Average Return = 64.15\n", - "Iteration 22: Average Return = 63.68\n", - "Iteration 23: Average Return = 66.58\n", - "Iteration 24: Average Return = 68.6\n", - "Iteration 25: Average Return = 67.22\n", - "Iteration 26: Average Return = 75.44\n", - "Iteration 27: Average Return = 77.7\n", - "Iteration 28: Average Return = 81.18\n", - "Iteration 29: Average Return = 82.95\n", - "Iteration 30: Average Return = 83.54\n", - "Iteration 31: Average Return = 89.02\n", - "Iteration 32: Average Return = 93.37\n", - "Iteration 33: Average Return = 104.44\n", - "Iteration 34: Average Return = 95.89\n", - "Iteration 35: Average Return = 101.77\n", - "Iteration 36: Average Return = 103.66\n", - "Iteration 37: Average Return = 116.72\n", - "Iteration 38: Average Return = 112.51\n", - "Iteration 39: Average Return = 116.54\n", - "Iteration 40: Average Return = 119.32\n", - "Iteration 41: Average Return = 114.08\n", - "Iteration 42: Average Return = 123.51\n", - "Iteration 43: Average Return = 125.28\n", - "Iteration 44: Average Return = 128.97\n", - "Iteration 45: Average Return = 134.09\n", - "Iteration 46: Average Return = 131.12\n", - "Iteration 47: Average Return = 134.75\n", - "Iteration 48: Average Return = 139.27\n", - "Iteration 49: Average Return = 144.41\n", - "Iteration 50: Average Return = 139.04\n", - "Iteration 51: Average Return = 144.19\n", - "Iteration 52: Average Return = 144.14\n", - "Iteration 53: Average Return = 159.08\n", - "Iteration 54: Average Return = 151.94\n", - "Iteration 55: Average Return = 168.5\n", - "Iteration 56: Average Return = 162.3\n", - "Iteration 57: Average Return = 166.97\n", - "Iteration 58: Average Return = 179.09\n", - "Iteration 59: Average Return = 179.04\n", - "Iteration 60: Average Return = 178.96\n", - "Iteration 61: Average Return = 182.56\n", - "Iteration 62: Average Return = 185.17\n", - "Iteration 63: Average Return = 186.02\n", - "Iteration 64: Average Return = 186.0\n", - "Iteration 65: Average Return = 185.88\n", - "Iteration 66: Average Return = 181.67\n", - "Iteration 67: Average Return = 184.82\n", - "Iteration 68: Average Return = 184.94\n", - "Iteration 69: Average Return = 190.33\n", - "Iteration 70: Average Return = 187.66\n", - "Iteration 71: Average Return = 185.15\n", - "Iteration 72: Average Return = 186.49\n", - "Iteration 73: Average Return = 185.22\n", - "Iteration 74: Average Return = 182.63\n", - "Iteration 75: Average Return = 181.68\n", - "Iteration 76: Average Return = 183.47\n", - "Iteration 77: Average Return = 183.0\n", - "Iteration 78: Average Return = 178.73\n", - "Iteration 79: Average Return = 182.86\n", - "Iteration 80: Average Return = 186.57\n", - "Iteration 81: Average Return = 187.34\n", - "Iteration 82: Average Return = 191.8\n", - "Iteration 83: Average Return = 194.14\n", - "Iteration 84: Average Return = 196.34\n", - "Solve at 84 iterations, which equals 8400 episodes.\n" + "Iteration 1: Average Return = 22.94\n", + "Iteration 2: Average Return = 24.37\n", + "Iteration 3: Average Return = 26.45\n", + "Iteration 4: Average Return = 31.35\n", + "Iteration 5: Average Return = 31.27\n", + "Iteration 6: Average Return = 37.81\n", + "Iteration 7: Average Return = 39.03\n", + "Iteration 8: Average Return = 41.01\n", + "Iteration 9: Average Return = 43.59\n", + "Iteration 10: Average Return = 50.65\n", + "Iteration 11: Average Return = 52.83\n", + "Iteration 12: Average Return = 51.94\n", + "Iteration 13: Average Return = 50.3\n", + "Iteration 14: Average Return = 49.6\n", + "Iteration 15: Average Return = 55.17\n", + "Iteration 16: Average Return = 55.46\n", + "Iteration 17: Average Return = 57.45\n", + "Iteration 18: Average Return = 62.21\n", + "Iteration 19: Average Return = 62.16\n", + "Iteration 20: Average Return = 60.59\n", + "Iteration 21: Average Return = 60.19\n", + "Iteration 22: Average Return = 69.07\n", + "Iteration 23: Average Return = 67.38\n", + "Iteration 24: Average Return = 67.52\n", + "Iteration 25: Average Return = 73.39\n", + "Iteration 26: Average Return = 70.13\n", + "Iteration 27: Average Return = 75.38\n", + "Iteration 28: Average Return = 74.77\n", + "Iteration 29: Average Return = 76.39\n", + "Iteration 30: Average Return = 75.78\n", + "Iteration 31: Average Return = 85.59\n", + "Iteration 32: Average Return = 81.99\n", + "Iteration 33: Average Return = 87.88\n", + "Iteration 34: Average Return = 90.76\n", + "Iteration 35: Average Return = 103.25\n", + "Iteration 36: Average Return = 101.93\n", + "Iteration 37: Average Return = 107.48\n", + "Iteration 38: Average Return = 121.69\n", + "Iteration 39: Average Return = 127.77\n", + "Iteration 40: Average Return = 118.29\n", + "Iteration 41: Average Return = 129.85\n", + "Iteration 42: Average Return = 132.29\n", + "Iteration 43: Average Return = 127.7\n", + "Iteration 44: Average Return = 124.58\n", + "Iteration 45: Average Return = 129.25\n", + "Iteration 46: Average Return = 130.62\n", + "Iteration 47: Average Return = 140.4\n", + "Iteration 48: Average Return = 143.05\n", + "Iteration 49: Average Return = 145.02\n", + "Iteration 50: Average Return = 145.73\n", + "Iteration 51: Average Return = 146.86\n", + "Iteration 52: Average Return = 161.13\n", + "Iteration 53: Average Return = 161.06\n", + "Iteration 54: Average Return = 166.03\n", + "Iteration 55: Average Return = 164.27\n", + "Iteration 56: Average Return = 173.94\n", + "Iteration 57: Average Return = 173.95\n", + "Iteration 58: Average Return = 184.27\n", + "Iteration 59: Average Return = 190.77\n", + "Iteration 60: Average Return = 192.1\n", + "Iteration 61: Average Return = 189.97\n", + "Iteration 62: Average Return = 189.21\n", + "Iteration 63: Average Return = 192.95\n", + "Iteration 64: Average Return = 192.87\n", + "Iteration 65: Average Return = 193.72\n", + "Iteration 66: Average Return = 193.65\n", + "Iteration 67: Average Return = 192.04\n", + "Iteration 68: Average Return = 195.69\n", + "Solve at 68 iterations, which equals 6800 episodes.\n" ] } ], @@ -370,7 +347,6 @@ "path_length = 200\n", "discount_rate = 0.99\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", @@ -381,14 +357,14 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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Gv4OdeiUBFVMxNbQGzEvKdFFdXLltJ+U5xKDNRxE4XTrQx/7OhoVQJlW+S3GN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\nW7u5p2M3vMZX6akUqF53TQPSy4yOEu40Ym1p6f7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XAMvPB/3x/1dq9Dlq0IDN8b6PgFxg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\n8JlOZWUlBQUFVFRUUFBQQGVlZdQxXq+XvXv3snHjRjZu3MjevXvxer0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9Hf3jR/De9yW8X/sMuvZ0wmzQ//nvcOIw6jN3jCrdoZQy3WQHdkeW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\nFPPmzePWW29NaZuH8uabb/KhD30IiO49RBxMhOg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AAAEBARVlckuraCi06YCfnXcy/ZkKsUFI3VlyQQiruJUsxo8ff9pyLy8vgoOD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\nXJJkIYQQwiVJFkIIIVySZCGEEMKl/w932NnI9N3aUgAAAABJRU5ErkJggg==\n", 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P13cWIYQbMtd/BgHBGA8/IzdNagSc6tPo0qULf/nLX4iKiqoyv4r0aQjReOnMs7B3J2rs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\nsF/bKwVDiIvTuVn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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -438,6 +414,148 @@ "Modify the code to compare the variance and performance before and after adding baseline. And explain wht the baseline won't introduce bias. Then, write a report about your findings and explainations. " ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 22.61\n", + "Iteration 2: Average Return = 22.21\n", + "Iteration 3: Average Return = 24.54\n", + "Iteration 4: Average Return = 27.82\n", + "Iteration 5: Average Return = 27.11\n", + "Iteration 6: Average Return = 30.55\n", + "Iteration 7: Average Return = 31.89\n", + "Iteration 8: Average Return = 32.99\n", + "Iteration 9: Average Return = 33.5\n", + "Iteration 10: Average Return = 37.71\n", + "Iteration 11: Average Return = 37.8\n", + "Iteration 12: Average Return = 39.83\n", + "Iteration 13: Average Return = 41.84\n", + "Iteration 14: Average Return = 43.65\n", + "Iteration 15: Average Return = 46.09\n", + "Iteration 16: Average Return = 45.93\n", + "Iteration 17: Average Return = 44.47\n", + "Iteration 18: Average Return = 43.3\n", + "Iteration 19: Average Return = 47.1\n", + "Iteration 20: Average Return = 48.33\n", + "Iteration 21: Average Return = 51.43\n", + "Iteration 22: Average Return = 46.04\n", + "Iteration 23: Average Return = 45.47\n", + "Iteration 24: Average Return = 50.16\n", + "Iteration 25: Average Return = 53.08\n", + "Iteration 26: Average Return = 51.65\n", + "Iteration 27: Average Return = 54.16\n", + "Iteration 28: Average Return = 52.49\n", + "Iteration 29: Average Return = 53.25\n", + "Iteration 30: Average Return = 57.1\n", + "Iteration 31: Average Return = 57.46\n", + "Iteration 32: Average Return = 55.59\n", + "Iteration 33: Average Return = 52.67\n", + "Iteration 34: Average Return = 55.91\n", + "Iteration 35: Average Return = 59.71\n", + "Iteration 36: Average Return = 62.7\n", + "Iteration 37: Average Return = 63.11\n", + "Iteration 38: Average Return = 61.32\n", + "Iteration 39: Average Return = 61.71\n", + "Iteration 40: Average Return = 65.42\n", + "Iteration 41: Average Return = 62.2\n", + "Iteration 42: Average Return = 62.21\n", + "Iteration 43: Average Return = 64.61\n", + "Iteration 44: Average Return = 65.86\n", + "Iteration 45: Average Return = 63.98\n", + "Iteration 46: Average Return = 66.46\n", + "Iteration 47: Average Return = 73.46\n", + "Iteration 48: Average Return = 69.1\n", + "Iteration 49: Average Return = 69.37\n", + "Iteration 50: Average Return = 69.53\n", + "Iteration 51: Average Return = 72.55\n", + "Iteration 52: Average Return = 69.64\n", + "Iteration 53: Average Return = 74.99\n", + "Iteration 54: Average Return = 75.06\n", + "Iteration 55: Average Return = 81.49\n", + "Iteration 56: Average Return = 79.37\n", + "Iteration 57: Average Return = 84.12\n", + "Iteration 58: Average Return = 82.77\n", + "Iteration 59: Average Return = 84.95\n", + "Iteration 60: Average Return = 86.63\n", + "Iteration 61: Average Return = 88.97\n", + "Iteration 62: Average Return = 92.04\n", + "Iteration 63: Average Return = 98.02\n", + "Iteration 64: Average Return = 88.16\n", + "Iteration 65: Average Return = 97.07\n", + "Iteration 66: Average Return = 102.45\n", + "Iteration 67: Average Return = 103.6\n", + "Iteration 68: Average Return = 114.49\n", + "Iteration 69: Average Return = 120.03\n", + "Iteration 70: Average Return = 121.19\n", + "Iteration 71: Average Return = 136.53\n", + "Iteration 72: Average Return = 147.45\n", + "Iteration 73: Average Return = 162.29\n", + "Iteration 74: Average Return = 166.93\n", + "Iteration 75: Average Return = 173.9\n", + "Iteration 76: Average Return = 177.76\n", + "Iteration 77: Average Return = 184.73\n", + "Iteration 78: Average Return = 188.6\n", + "Iteration 79: Average Return = 191.03\n", + "Iteration 80: Average Return = 192.34\n", + "Iteration 81: Average Return = 194.82\n", + "Iteration 82: Average Return = 195.8\n", + "Solve at 82 iterations, which equals 8200 episodes.\n" + ] + } + ], + "source": [ + "sess.run(tf.global_variables_initializer())\n", + "\n", + "n_iter = 200\n", + "n_episode = 100\n", + "path_length = 200\n", + "discount_rate = 0.99\n", + "baseline = None\n", + "\n", + "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate)\n", + "\n", + "# Train the policy optimizer\n", + "loss_list, avg_return_list = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qs87tAfJZqZ04zWWBC2chzE6DLkCVmJbLrLDTzzwdzEUrWpYDIqU3ox9ew9hK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, From b5616b6af991fa8dfa07258992b0104f0f69adbd Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 01:27:40 +0800 Subject: [PATCH 05/20] Update util.py --- Lab3-policy-gradient.ipynb | 197 +++++++++++++++++++++++++++++++++++++ policy_gradient/util.py | 4 + 2 files changed, 201 insertions(+) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 58be33f..46f8f89 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -581,6 +581,203 @@ "If you answer is right, your will solve CartPole with roughly ~ 80 iterations." ] }, + { + "cell_type": "code", + "execution_count": 9, + "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": 11, + "metadata": { + "scrolled": true + }, + "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" + ] + } + ], + "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, avg_return_list = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NnnIp+h+voj98xxgdFmnB9It7UAHNf2mdyjTlMpx5X6H/9TZE90N1YIkjFR6J6Z7Fxl/w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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" + } + ], + "source": [ + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..4ba3272 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,6 +32,10 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> + b.append(0) + b_t_next = b[1:] + y = x + discount_rate*b_t_next + return y # <<<<<<<<<<<<<<<<<<<<<<<<<<<< def plot_curve(data, key, filename=None): From 370644246a9cb6d7670ffa536eabc5fa7329582e Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 03:08:47 +0800 Subject: [PATCH 06/20] Update Lab3-policy-gradient.ipynb --- Lab3-policy-gradient.ipynb | 818 +++++++++++++++++++++---------------- policy_gradient/util.py | 4 +- 2 files changed, 461 insertions(+), 361 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 46f8f89..d3ede73 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# Automatically reload changes to external code\n", "%load_ext autoreload\n", @@ -35,7 +26,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -104,7 +95,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -153,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": { "collapsed": true }, @@ -260,82 +251,72 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 22.94\n", - "Iteration 2: Average Return = 24.37\n", - "Iteration 3: Average Return = 26.45\n", - "Iteration 4: Average Return = 31.35\n", - "Iteration 5: Average Return = 31.27\n", - "Iteration 6: Average Return = 37.81\n", - "Iteration 7: Average Return = 39.03\n", - "Iteration 8: Average Return = 41.01\n", - "Iteration 9: Average Return = 43.59\n", - "Iteration 10: Average Return = 50.65\n", - "Iteration 11: Average Return = 52.83\n", - "Iteration 12: Average Return = 51.94\n", - "Iteration 13: Average Return = 50.3\n", - "Iteration 14: Average Return = 49.6\n", - "Iteration 15: Average Return = 55.17\n", - "Iteration 16: Average Return = 55.46\n", - "Iteration 17: Average Return = 57.45\n", - "Iteration 18: Average Return = 62.21\n", - "Iteration 19: Average Return = 62.16\n", - "Iteration 20: Average Return = 60.59\n", - "Iteration 21: Average Return = 60.19\n", - "Iteration 22: Average Return = 69.07\n", - "Iteration 23: Average Return = 67.38\n", - "Iteration 24: Average Return = 67.52\n", - "Iteration 25: Average Return = 73.39\n", - "Iteration 26: Average Return = 70.13\n", - "Iteration 27: Average Return = 75.38\n", - "Iteration 28: Average Return = 74.77\n", - "Iteration 29: Average Return = 76.39\n", - "Iteration 30: Average Return = 75.78\n", - "Iteration 31: Average Return = 85.59\n", - "Iteration 32: Average Return = 81.99\n", - "Iteration 33: Average Return = 87.88\n", - "Iteration 34: Average Return = 90.76\n", - "Iteration 35: Average Return = 103.25\n", - "Iteration 36: Average Return = 101.93\n", - "Iteration 37: Average Return = 107.48\n", - "Iteration 38: Average Return = 121.69\n", - "Iteration 39: Average Return = 127.77\n", - "Iteration 40: Average Return = 118.29\n", - "Iteration 41: Average Return = 129.85\n", - "Iteration 42: Average Return = 132.29\n", - "Iteration 43: Average Return = 127.7\n", - "Iteration 44: Average Return = 124.58\n", - "Iteration 45: Average Return = 129.25\n", - "Iteration 46: Average Return = 130.62\n", - "Iteration 47: Average Return = 140.4\n", - "Iteration 48: Average Return = 143.05\n", - "Iteration 49: Average Return = 145.02\n", - "Iteration 50: Average Return = 145.73\n", - "Iteration 51: Average Return = 146.86\n", - "Iteration 52: Average Return = 161.13\n", - "Iteration 53: Average Return = 161.06\n", - "Iteration 54: Average Return = 166.03\n", - "Iteration 55: Average Return = 164.27\n", - "Iteration 56: Average Return = 173.94\n", - "Iteration 57: Average Return = 173.95\n", - "Iteration 58: Average Return = 184.27\n", - "Iteration 59: Average Return = 190.77\n", - "Iteration 60: Average Return = 192.1\n", - "Iteration 61: Average Return = 189.97\n", - "Iteration 62: Average Return = 189.21\n", - "Iteration 63: Average Return = 192.95\n", - "Iteration 64: Average Return = 192.87\n", - "Iteration 65: Average Return = 193.72\n", - "Iteration 66: Average Return = 193.65\n", - "Iteration 67: Average Return = 192.04\n", - "Iteration 68: Average Return = 195.69\n", - "Solve at 68 iterations, which equals 6800 episodes.\n" + "Iteration 1: Average Return = 36.3\n", + "Iteration 2: Average Return = 35.62\n", + "Iteration 3: Average Return = 41.83\n", + "Iteration 4: Average Return = 41.59\n", + "Iteration 5: Average Return = 45.35\n", + "Iteration 6: Average Return = 47.75\n", + "Iteration 7: Average Return = 43.01\n", + "Iteration 8: Average Return = 48.09\n", + "Iteration 9: Average Return = 46.28\n", + "Iteration 10: Average Return = 49.92\n", + "Iteration 11: Average Return = 50.93\n", + "Iteration 12: Average Return = 51.42\n", + "Iteration 13: Average Return = 51.7\n", + "Iteration 14: Average Return = 50.86\n", + "Iteration 15: Average Return = 51.94\n", + "Iteration 16: Average Return = 56.21\n", + "Iteration 17: Average Return = 54.27\n", + "Iteration 18: Average Return = 56.5\n", + "Iteration 19: Average Return = 62.7\n", + "Iteration 20: Average Return = 62.08\n", + "Iteration 21: Average Return = 65.68\n", + "Iteration 22: Average Return = 67.45\n", + "Iteration 23: Average Return = 68.25\n", + "Iteration 24: Average Return = 65.85\n", + "Iteration 25: Average Return = 68.73\n", + "Iteration 26: Average Return = 73.22\n", + "Iteration 27: Average Return = 74.65\n", + "Iteration 28: Average Return = 82.6\n", + "Iteration 29: Average Return = 88.59\n", + "Iteration 30: Average Return = 93.62\n", + "Iteration 31: Average Return = 94.23\n", + "Iteration 32: Average Return = 107.5\n", + "Iteration 33: Average Return = 117.67\n", + "Iteration 34: Average Return = 130.97\n", + "Iteration 35: Average Return = 144.9\n", + "Iteration 36: Average Return = 150.8\n", + "Iteration 37: Average Return = 146.76\n", + "Iteration 38: Average Return = 143.07\n", + "Iteration 39: Average Return = 151.75\n", + "Iteration 40: Average Return = 143.04\n", + "Iteration 41: Average Return = 152.31\n", + "Iteration 42: Average Return = 145.89\n", + "Iteration 43: Average Return = 145.5\n", + "Iteration 44: Average Return = 150.78\n", + "Iteration 45: Average Return = 151.63\n", + "Iteration 46: Average Return = 154.9\n", + "Iteration 47: Average Return = 160.54\n", + "Iteration 48: Average Return = 167.68\n", + "Iteration 49: Average Return = 171.19\n", + "Iteration 50: Average Return = 180.17\n", + "Iteration 51: Average Return = 182.87\n", + "Iteration 52: Average Return = 190.01\n", + "Iteration 53: Average Return = 188.21\n", + "Iteration 54: Average Return = 191.13\n", + "Iteration 55: Average Return = 193.14\n", + "Iteration 56: Average Return = 191.94\n", + "Iteration 57: Average Return = 193.99\n", + "Iteration 58: Average Return = 195.12\n", + "Solve at 58 iterations, which equals 5800 episodes.\n" ] } ], @@ -357,14 +338,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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9Hf3jR/De9yW8X/sMuvZ0wmzQ//nvcOIw6jN3jCrdoZQy3WQHdkeW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\nFPPmzePWW29NaZuH8uabb/KhD30IiO49RBxMhOg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vaQKDASy+w5tF/mKVLHrwP5AD/ZNOF2zjNn+IlFRYVDxlv46UEs6fRlxZpkK3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\nZxuV3wdU5NpcC5jHlGMx50BHu8r1mQK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do6OZlLhrOp2dnZjNZgDMZjNdXV1eY2w2Gzk5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\niNFoRK+X/xrEzJArS4ggSE9PByAvL4/t27cDkJ+fz9jYGEV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P13cWIYQbMtd/BgHBGA8/IzdNagSc6tPo0qULf/nLX4iKiqoyv4r0aQjReOnMs7B3J2rs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\nEMJp/x/o5Cmmv+0NOwAAAABJRU5ErkJggg==\n", 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LA8Df359jx45x4cKFkjWjytKhQwcaNSq9tk1cXFzJ1NvQ0FDi4uIAiI+PZ8CA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\nrkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -416,96 +397,100 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 22.61\n", - "Iteration 2: Average Return = 22.21\n", - "Iteration 3: Average Return = 24.54\n", - "Iteration 4: Average Return = 27.82\n", - "Iteration 5: Average Return = 27.11\n", - "Iteration 6: Average Return = 30.55\n", - "Iteration 7: Average Return = 31.89\n", - "Iteration 8: Average Return = 32.99\n", - "Iteration 9: Average Return = 33.5\n", - "Iteration 10: Average Return = 37.71\n", - "Iteration 11: Average Return = 37.8\n", - "Iteration 12: Average Return = 39.83\n", - "Iteration 13: Average Return = 41.84\n", - "Iteration 14: Average Return = 43.65\n", - "Iteration 15: Average Return = 46.09\n", - "Iteration 16: Average Return = 45.93\n", - "Iteration 17: Average Return = 44.47\n", - "Iteration 18: Average Return = 43.3\n", - "Iteration 19: Average Return = 47.1\n", - "Iteration 20: Average Return = 48.33\n", - "Iteration 21: Average Return = 51.43\n", - "Iteration 22: Average Return = 46.04\n", - "Iteration 23: Average Return = 45.47\n", - "Iteration 24: Average Return = 50.16\n", - "Iteration 25: Average Return = 53.08\n", - "Iteration 26: Average Return = 51.65\n", - "Iteration 27: Average Return = 54.16\n", - "Iteration 28: Average Return = 52.49\n", - "Iteration 29: Average Return = 53.25\n", - "Iteration 30: Average Return = 57.1\n", - "Iteration 31: Average Return = 57.46\n", - "Iteration 32: Average Return = 55.59\n", - "Iteration 33: Average Return = 52.67\n", - "Iteration 34: Average Return = 55.91\n", - "Iteration 35: Average Return = 59.71\n", - "Iteration 36: Average Return = 62.7\n", - "Iteration 37: Average Return = 63.11\n", - "Iteration 38: Average Return = 61.32\n", - "Iteration 39: Average Return = 61.71\n", - "Iteration 40: Average Return = 65.42\n", - "Iteration 41: Average Return = 62.2\n", - "Iteration 42: Average Return = 62.21\n", - "Iteration 43: Average Return = 64.61\n", - "Iteration 44: Average Return = 65.86\n", - "Iteration 45: Average Return = 63.98\n", - "Iteration 46: Average Return = 66.46\n", - "Iteration 47: Average Return = 73.46\n", - "Iteration 48: Average Return = 69.1\n", - "Iteration 49: Average Return = 69.37\n", - "Iteration 50: Average Return = 69.53\n", - "Iteration 51: Average Return = 72.55\n", - "Iteration 52: Average Return = 69.64\n", - "Iteration 53: Average Return = 74.99\n", - "Iteration 54: Average Return = 75.06\n", - "Iteration 55: Average Return = 81.49\n", - "Iteration 56: Average Return = 79.37\n", - "Iteration 57: Average Return = 84.12\n", - "Iteration 58: Average Return = 82.77\n", - "Iteration 59: Average Return = 84.95\n", - "Iteration 60: Average Return = 86.63\n", - "Iteration 61: Average Return = 88.97\n", - "Iteration 62: Average Return = 92.04\n", - "Iteration 63: Average Return = 98.02\n", - "Iteration 64: Average Return = 88.16\n", - "Iteration 65: Average Return = 97.07\n", - "Iteration 66: Average Return = 102.45\n", - "Iteration 67: Average Return = 103.6\n", - "Iteration 68: Average Return = 114.49\n", - "Iteration 69: Average Return = 120.03\n", - "Iteration 70: Average Return = 121.19\n", - "Iteration 71: Average Return = 136.53\n", - "Iteration 72: Average Return = 147.45\n", - "Iteration 73: Average Return = 162.29\n", - "Iteration 74: Average Return = 166.93\n", - "Iteration 75: Average Return = 173.9\n", - "Iteration 76: Average Return = 177.76\n", - "Iteration 77: Average Return = 184.73\n", - "Iteration 78: Average Return = 188.6\n", - "Iteration 79: Average Return = 191.03\n", - "Iteration 80: Average Return = 192.34\n", - "Iteration 81: Average Return = 194.82\n", - "Iteration 82: Average Return = 195.8\n", - "Solve at 82 iterations, which equals 8200 episodes.\n" + "Iteration 1: Average Return = 20.1\n", + "Iteration 2: Average Return = 23.47\n", + "Iteration 3: Average Return = 23.32\n", + "Iteration 4: Average Return = 26.65\n", + "Iteration 5: Average Return = 27.08\n", + "Iteration 6: Average Return = 31.7\n", + "Iteration 7: Average Return = 31.25\n", + "Iteration 8: Average Return = 30.92\n", + "Iteration 9: Average Return = 33.59\n", + "Iteration 10: Average Return = 36.52\n", + "Iteration 11: Average Return = 40.5\n", + "Iteration 12: Average Return = 39.73\n", + "Iteration 13: Average Return = 39.07\n", + "Iteration 14: Average Return = 39.96\n", + "Iteration 15: Average Return = 41.08\n", + "Iteration 16: Average Return = 41.64\n", + "Iteration 17: Average Return = 40.74\n", + "Iteration 18: Average Return = 40.6\n", + "Iteration 19: Average Return = 45.97\n", + "Iteration 20: Average Return = 46.91\n", + "Iteration 21: Average Return = 43.62\n", + "Iteration 22: Average Return = 47.65\n", + "Iteration 23: Average Return = 48.4\n", + "Iteration 24: Average Return = 46.54\n", + "Iteration 25: Average Return = 52.47\n", + "Iteration 26: Average Return = 49.33\n", + "Iteration 27: Average Return = 49.14\n", + "Iteration 28: Average Return = 52.58\n", + "Iteration 29: Average Return = 48.55\n", + "Iteration 30: Average Return = 51.14\n", + "Iteration 31: Average Return = 52.14\n", + "Iteration 32: Average Return = 56.55\n", + "Iteration 33: Average Return = 53.23\n", + "Iteration 34: Average Return = 54.4\n", + "Iteration 35: Average Return = 54.32\n", + "Iteration 36: Average Return = 52.81\n", + "Iteration 37: Average Return = 55.96\n", + "Iteration 38: Average Return = 56.55\n", + "Iteration 39: Average Return = 54.79\n", + "Iteration 40: Average Return = 54.97\n", + "Iteration 41: Average Return = 54.94\n", + "Iteration 42: Average Return = 55.18\n", + "Iteration 43: Average Return = 64.06\n", + "Iteration 44: Average Return = 60.15\n", + "Iteration 45: Average Return = 60.81\n", + "Iteration 46: Average Return = 66.59\n", + "Iteration 47: Average Return = 64.09\n", + "Iteration 48: Average Return = 60.87\n", + "Iteration 49: Average Return = 64.64\n", + "Iteration 50: Average Return = 65.73\n", + "Iteration 51: Average Return = 67.45\n", + "Iteration 52: Average Return = 64.71\n", + "Iteration 53: Average Return = 72.9\n", + "Iteration 54: Average Return = 75.25\n", + "Iteration 55: Average Return = 80.11\n", + "Iteration 56: Average Return = 72.7\n", + "Iteration 57: Average Return = 80.52\n", + "Iteration 58: Average Return = 83.91\n", + "Iteration 59: Average Return = 84.67\n", + "Iteration 60: Average Return = 87.1\n", + "Iteration 61: Average Return = 96.67\n", + "Iteration 62: Average Return = 95.72\n", + "Iteration 63: Average Return = 105.09\n", + "Iteration 64: Average Return = 112.31\n", + "Iteration 65: Average Return = 135.78\n", + "Iteration 66: Average Return = 137.28\n", + "Iteration 67: Average Return = 146.69\n", + "Iteration 68: Average Return = 149.38\n", + "Iteration 69: Average Return = 156.18\n", + "Iteration 70: Average Return = 166.69\n", + "Iteration 71: Average Return = 160.88\n", + "Iteration 72: Average Return = 164.94\n", + "Iteration 73: Average Return = 162.63\n", + "Iteration 74: Average Return = 158.43\n", + "Iteration 75: Average Return = 158.95\n", + "Iteration 76: Average Return = 169.29\n", + "Iteration 77: Average Return = 174.37\n", + "Iteration 78: Average Return = 168.45\n", + "Iteration 79: Average Return = 175.18\n", + "Iteration 80: Average Return = 179.8\n", + "Iteration 81: Average Return = 185.64\n", + "Iteration 82: Average Return = 186.79\n", + "Iteration 83: Average Return = 189.48\n", + "Iteration 84: Average Return = 194.17\n", + "Iteration 85: Average Return = 190.49\n", + "Iteration 86: Average Return = 195.75\n", + "Solve at 86 iterations, which equals 8600 episodes.\n" ] } ], @@ -527,14 +512,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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qs87tAfJZqZ04zWWBC2chzE6DLkCVmJbLrLDTzzwdzEUrWpYDIqU3ox9ew9hK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wLoIaqMMMkZpkdeA4GhEw8DI1ZlW7OTk5XUwIwEvdaCp26+CuEqw3ax/B4XQC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\ntB+uzsRdcapPfnfu706CLTnosdzxY+DsGVrOalUanM1uFIe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ULaNOr+QFtHJJhnVbe7xI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7O5tGjRpV6+AhISHMnz8fsK6bMWXKFG677Ta2bNnC0KFDGT58eLWeXwhRPTo3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\nQmCC\n", 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IIa6EJAsnoLVGr3wVzpzEePAfqIa/Pvd8TUfUNR1tx5CcYF2voWkL\nVH2Z/kQIUXUkWTgB/e2n6G1xqNETSp0PSSllXatB+hiEEA4g4ywczNz4Bfo/K6yPsMqcSEIIJyU1\nCwfRWqPXfohe+2/o0gtDptEQQjgxSRYOoE0T/eHr6I1foPoOtk6j4eLi6LCEEKJUkiyqmM7LQ69Y\niN6xBTV0DGrsJKlRCCGcniSLKqTTkjFfnQf/+wl12z0YQ0c7OiQhhLCLJIsKon86jPn2IvDxQzVo\nBIGNwNIA5RsAvv6QmY75r0jIycZ44ElU52sdHbIQQthNkkUF0Lk5mG++DPn54FUfvftHyEy37vvj\ngf6BGLMjUU2aOyROIYS4UpIsKoD+aAUkXcB45HnrfE5Y16smJRHSUtBpKZCThbr2OpS3n4OjFUKI\nyyfJ4irpfTvQm79G3TDGlijAOr03jf8Cjf+CdF8LIao7GZR3FXRWBuY7i60J4eY7HR2OEEJUGofW\nLJKSkliyZAlpaWkopQgPD2f48OGsXr2a9evX4+1tnf9o/PjxdOvWzZGhAqDNIjh+GJ14DhLPow/u\nhqwMjBlPl7rIkBBC1AQOTRYuLi7cddddhIaGkpOTw+zZs+nUqRMAI0aMYNSoUY4MrxhdWIi5LBL2\nbLNuMAzwD0TdOQ3VNNSxwQkhRCVzaLLw8/PDz8/a4Vu3bl0aN25MSkqKI0MqkTaL0G9Fw55tqFvu\nRnXva00UrtLlI4SoHZzm2y4hIYETJ07QsmVLDh8+zLp169i8eTOhoaFMnDgRL69LV4uLjY0lNjYW\ngMjISCwWyxW/v6ura4nna9MkY8nz5Mb/F6+7p1Nv9B1X/B7VVWllI6ykfEonZVO26lQ+Smutyz+s\ncuXm5vLMM89wyy230KtXL9LS0mz9FatWrSI1NZWIiIhyr3P27NkrjsFisZCUlITevxO9cyu4e4BH\nXbjwi3VqjptuxxhV+xIF/F42omRSPqWTsimbM5RPcHCwXcc5vGZRWFhIVFQUAwYMoFevXgD4+vra\n9g8ePJj58+dXSSx633bMJfOsiQIFuTmARt14K+qm8VUSgxBCOCOHJgutNcuWLaNx48aMHDnStj01\nNdXWl7Ft2zZCQkIqPZb8/bus03E0bobx9+dQnvWsq9MVFUnfhBCi1nPot+CRI0fYvHkzTZs25dFH\nHwWsj8lu2bKFkydPopQiMDCQqVOnVmoc+sRR0l5+GiwNMR6eg/KsB/y6Op0kCiGEcGyyaNOmDatX\nr75ke1WOqdC/nMJc+E9cfHzRs56Vta2FEKIE8mdzPS9o1hLfB58kzdXD0dEIIYRTqvXTfSjfAFxm\nPotrUGNHhyKEEE6r1icLIYQQ5ZNkIYQQolySLIQQQpRLkoUQQohySbIQQghRLkkWQgghyiXJQggh\nRLkkWQghhCiXU0xRLoQQwrlJzeJXs2fPdnQITkvKpmxSPqWTsilbdSofSRZCCCHKJclCCCFEuSRZ\n/Co8PNzRITgtKZuySfmUTsqmbNWpfKSDWwghRLmkZiGEEKJctX7xo927d7NixQpM02Tw4MGMHj3a\n0SE5VFJSEkuWLCEtLQ2lFOHh4QwfPpysrCyio6NJTEwkMDCQmTNn4uXl5ehwHcI0TWbPno2/vz+z\nZ88mISGBhQsXkpWVRfPmzXnwwQdxraXL8V68eJFly5Zx+vRplFLcf//9BAcHy70DfP7552zYsAGl\nFCEhIURERJCWllZt7p1aXbMwTZM333yT//u//yM6OpotW7Zw5swZR4flUC4uLtx1111ER0czb948\n1q1bx5kzZ4iJiaFjx44sWrSIjh07EhMT4+hQHebLL7+kcePfF8t67733GDFiBIsWLaJevXps2LDB\ngdE51ooVK+jSpQsLFy5kwYIFNG7cWO4dICUlha+++orIyEiioqIwTZOtW7dWq3unVieL48ePExQU\nRMOGDXF1daVv377Ex8c7OiyH8vPzIzQ0FIC6devSuHFjUlJSiI+PJywsDICwsLBaW07Jycns3LmT\nwYMHA6C15sCBA/Tu3RuAgQMH1tqyyc7O5tChQwwaNAgAV1dX6tWrJ/fOr0zTJD8/n6KiIvLz8/H1\n9a1W945z1neqSEpKCgEBAbbXAQEBHDt2zIEROZeEhAROnDhBy5YtSU9Px8/PD7AmlIyMDAdH5xhv\nv/02EyZMICcnB4DMzEw8PT1xcXEBwN/fn5SUFEeG6DAJCQl4e3uzdOlSTp06RWhoKJMmTZJ7B+t9\ncdNNN3H//ffj7u5O586dCQ0NrVb3Tq2uWZT0IJhSygGROJ/c3FyioqKYNGkSnp6ejg7HKezYsQMf\nHx9bzUsUV1RUxIkTJxg6dCgvvvgiHh4etbLJqSRZWVnEx8ezZMkSli9fTm5uLrt373Z0WJelVtcs\nAgICSE5Otr1OTk62/QVUmxUWFhIVFcWAAQPo1asXAD4+PqSmpuLn50dqaire3t4OjrLqHTlyhO3b\nt7Nr1y7y8/PJycnh7bffJjs7m6KiIlxcXEhJScHf39/RoTpEQEAAAQEBtGrVCoDevXsTExMj9w6w\nb98+GjRoYPvsvXr14siRI9Xq3qnVNYsWLVpw7tw5EhISKCwsZOvWrfTo0cPRYTmU1pply5bRuHFj\nRo4cadveo0cP4uLiAIiLi6Nnz56OCtFh7rjjDpYtW8aSJUt4+OGH6dChAzNmzKB9+/b88MMPAGza\ntKnW3kO+vr4EBARw9uxZwPoF2aRJE7l3AIvFwrFjx8jLy0NrbSub6nTv1PpBeTt37uSdd97BNE2u\nv/56brnlFkeH5FCHDx/m6aefpmnTprYmufHjx9OqVSuio6NJSkrCYrEwa9asWvn4428OHDjA2rVr\nmT17NhcuXLjk8Uc3NzdHh+gQJ0+eZNmyZRQWFtKgQQMiIiLQWsu9A6xevZqtW7fi4uJCs2bNmDZt\nGikpKdXm3qn1yUIIIUT5anUzlBBCCPtIshBCCFEuSRZCCCHKJclCCCFEuSRZCCGEKJckC1ErzZo1\niwMHDjjkvZOSkrjrrrswTdMh7y/ElZBHZ0Wttnr1as6fP8+MGTMq7T0eeOAB7rvvPjp16lRp7yFE\nZZOahRBXoaioyNEhCFElpGYhaqUHHniAe+65h5deegmwTqcdFBTEggULyM7O5p133mHXrl0opbj+\n+usZN24chmGwadMm1q9fT4sWLYiLi+OGG25g4MCBLF++nFOnTqGUonPnzkyZMoV69eqxePFivvvu\nO1xdXTEMg7Fjx9KnTx+mT5/Ov//9b9ucQK+//jqHDx/Gy8uLm2++2bY28+rVqzlz5gzu7u5s27YN\ni8XCAw88QIsWLQCIiYnhq6++IicnBz8/P/72t7/RsWNHh5WrqLlq9USConZzc3NjzJgxlzRDvfrq\nq/j6+rJo0SLy8vKIjIwkICCAIUOGAHDs2DH69u3LG2+8QVFRESkpKYwZM4a2bduSk5NDVFQUH330\nEZMmTeLBBx/k8OHDxZqhEhISisXxyiuvEBISwvLlyzl79ixz586lYcOGti/9HTt28Pe//52IiAg+\n/PBD3nrrLebNm8fZs2dZt24dL7zwAv7+/iQkJEg/iKg00gwlxB+kpaWxe/duJk2aRJ06dfDx8WHE\niBFs3brVdoyfnx833ngjLi4uuLu7ExQURKdOnXBzc8Pb25sRI0Zw8OBBu94vKSmJw4cPc+edd+Lu\n7k6zZs0YPHgwmzdvth3Tpk0bunXrhmEYXHfddZw8eRIAwzAoKCjgzJkztrmYgoKCKrQ8hPiN1CyE\n+IOkpCSKioqYOnWqbZvWutgiWRaLpdg56enprFixgkOHDpGbm4tpmnZPlJeamoqXlxd169Ytdv2f\nfvrJ9trHx8f2s7u7OwUFBRQVFREUFMSkSZP46KOPOHPmDJ07d2bixIlOPc21qL4kWYha7c+LXQUE\nBODq6sqbb75pW8GsPB988AEAL730EvXr12fbtm289dZbdp3r5+dHVlYWOTk5toSRlJRk9xd+//79\n6d+/P9nZ2bz22mu8//77PPjgg3adK8TlkGYoUav5+PiQmJhoa+v38/Ojc+fOrFy5kuzsbEzT5Pz5\n82U2K+Xk5FCnTh3q1atHSkoKa9euLbbf19f3kn6K31gsFq655ho++OAD8vPzOXXqFBs3bmTAgAHl\nxn727Fn2799PQUEB7u7uuLu7YxjyT1pUDrmzRK3Wp08fAKZMmcLjjz8OwPTp0yksLGTWrFlMnjyZ\nl19+mdTU1FKvcdttt3HixAnuvvtuXnjhBa699tpi+0ePHs3HH3/MpEmT+Oyzzy45/6GHHiIxMZH7\n7ruPl156idtuu82uMRkFBQW8//77TJkyhXvvvZeMjAzGjx9/OR9fCLvJo7NCCCHKJTULIYQQ5ZJk\nIYQQolySLIQQQpRLkoUQQohySbIQQghRLkkWQgghyiXJQgghRLkkWQghhCiXJAshhBDl+n93AEug\n/VhCuQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -634,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "scrolled": true }, @@ -643,90 +628,206 @@ "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 = 15.58\n", + "Iteration 2: Average Return = 15.27\n", + "Iteration 3: Average Return = 14.87\n", + "Iteration 4: Average Return = 15.79\n", + "Iteration 5: Average Return = 15.16\n", + "Iteration 6: Average Return = 17.15\n", + "Iteration 7: Average Return = 16.77\n", + "Iteration 8: Average Return = 19.63\n", + "Iteration 9: Average Return = 18.92\n", + "Iteration 10: Average Return = 18.62\n", + "Iteration 11: Average Return = 20.91\n", + "Iteration 12: Average Return = 20.77\n", + "Iteration 13: Average Return = 21.79\n", + "Iteration 14: Average Return = 21.58\n", + "Iteration 15: Average Return = 25.72\n", + "Iteration 16: Average Return = 25.73\n", + "Iteration 17: Average Return = 28.32\n", + "Iteration 18: Average Return = 25.81\n", + "Iteration 19: Average Return = 29.66\n", + "Iteration 20: Average Return = 31.96\n", + "Iteration 21: Average Return = 31.97\n", + "Iteration 22: Average Return = 37.81\n", + "Iteration 23: Average Return = 34.28\n", + "Iteration 24: Average Return = 32.66\n", + "Iteration 25: Average Return = 40.06\n", + "Iteration 26: Average Return = 41.08\n", + "Iteration 27: Average Return = 44.96\n", + "Iteration 28: Average Return = 42.21\n", + "Iteration 29: Average Return = 40.91\n", + "Iteration 30: Average Return = 44.04\n", + "Iteration 31: Average Return = 46.89\n", + "Iteration 32: Average Return = 46.89\n", + "Iteration 33: Average Return = 48.9\n", + "Iteration 34: Average Return = 50.14\n", + "Iteration 35: Average Return = 46.95\n", + "Iteration 36: Average Return = 54.99\n", + "Iteration 37: Average Return = 52.45\n", + "Iteration 38: Average Return = 54.82\n", + "Iteration 39: Average Return = 59.08\n", + "Iteration 40: Average Return = 58.38\n", + "Iteration 41: Average Return = 54.59\n", + "Iteration 42: Average Return = 61.3\n", + "Iteration 43: Average Return = 62.26\n", + "Iteration 44: Average Return = 61.51\n", + "Iteration 45: Average Return = 59.4\n", + "Iteration 46: Average Return = 62.32\n", + "Iteration 47: Average Return = 61.84\n", + "Iteration 48: Average Return = 62.1\n", + "Iteration 49: Average Return = 64.87\n", + "Iteration 50: Average Return = 58.23\n", + "Iteration 51: Average Return = 71.57\n", + "Iteration 52: Average Return = 65.5\n", + "Iteration 53: Average Return = 69.07\n", + "Iteration 54: Average Return = 66.68\n", + "Iteration 55: Average Return = 73.35\n", + "Iteration 56: Average Return = 69.2\n", + "Iteration 57: Average Return = 75.52\n", + "Iteration 58: Average Return = 78.99\n", + "Iteration 59: Average Return = 75.31\n", + "Iteration 60: Average Return = 87.01\n", + "Iteration 61: Average Return = 85.15\n", + "Iteration 62: Average Return = 88.56\n", + "Iteration 63: Average Return = 90.55\n", + "Iteration 64: Average Return = 92.69\n", + "Iteration 65: Average Return = 90.0\n", + "Iteration 66: Average Return = 95.58\n", + "Iteration 67: Average Return = 106.97\n", + "Iteration 68: Average Return = 108.86\n", + "Iteration 69: Average Return = 112.72\n", + "Iteration 70: Average Return = 116.06\n", + "Iteration 71: Average Return = 122.27\n", + "Iteration 72: Average Return = 127.2\n", + "Iteration 73: Average Return = 117.92\n", + "Iteration 74: Average Return = 120.04\n", + "Iteration 75: Average Return = 121.56\n", + "Iteration 76: Average Return = 117.68\n", + "Iteration 77: Average Return = 129.27\n", + "Iteration 78: Average Return = 125.99\n", + "Iteration 79: Average Return = 119.74\n", + "Iteration 80: Average Return = 122.39\n", + "Iteration 81: Average Return = 128.95\n", + "Iteration 82: Average Return = 134.99\n", + "Iteration 83: Average Return = 133.44\n", + "Iteration 84: Average Return = 136.84\n", + "Iteration 85: Average Return = 139.35\n", + "Iteration 86: Average Return = 145.28\n", + "Iteration 87: Average Return = 141.45\n", + "Iteration 88: Average Return = 141.47\n", + "Iteration 89: Average Return = 134.68\n", + "Iteration 90: Average Return = 136.97\n", + "Iteration 91: Average Return = 134.16\n", + "Iteration 92: Average Return = 144.93\n", + "Iteration 93: Average Return = 139.9\n", + "Iteration 94: Average Return = 137.18\n", + "Iteration 95: Average Return = 138.02\n", + "Iteration 96: Average Return = 138.85\n", + "Iteration 97: Average Return = 135.37\n", + "Iteration 98: Average Return = 131.78\n", + "Iteration 99: Average Return = 136.19\n", + "Iteration 100: Average Return = 138.71\n", + "Iteration 101: Average Return = 130.51\n", + "Iteration 102: Average Return = 128.65\n", + "Iteration 103: Average Return = 135.1\n", + "Iteration 104: Average Return = 138.46\n", + "Iteration 105: Average Return = 145.99\n", + "Iteration 106: Average Return = 146.95\n", + "Iteration 107: Average Return = 146.54\n", + "Iteration 108: Average Return = 149.18\n", + "Iteration 109: Average Return = 148.54\n", + "Iteration 110: Average Return = 149.14\n", + "Iteration 111: Average Return = 143.38\n", + "Iteration 112: Average Return = 151.95\n", + "Iteration 113: Average Return = 152.59\n", + "Iteration 114: Average Return = 154.05\n", + "Iteration 115: Average Return = 158.63\n", + "Iteration 116: Average Return = 156.96\n", + "Iteration 117: Average Return = 153.63\n", + "Iteration 118: Average Return = 155.61\n", + "Iteration 119: Average Return = 151.14\n", + "Iteration 120: Average Return = 155.31\n", + "Iteration 121: Average Return = 151.17\n", + "Iteration 122: Average Return = 151.2\n", + "Iteration 123: Average Return = 147.83\n", + "Iteration 124: Average Return = 145.58\n", + "Iteration 125: Average Return = 137.98\n", + "Iteration 126: Average Return = 140.12\n", + "Iteration 127: Average Return = 143.54\n", + "Iteration 128: Average Return = 138.92\n", + "Iteration 129: Average Return = 144.35\n", + "Iteration 130: Average Return = 140.67\n", + "Iteration 131: Average Return = 139.8\n", + "Iteration 132: Average Return = 147.92\n", + "Iteration 133: Average Return = 142.12\n", + "Iteration 134: Average Return = 147.82\n", + "Iteration 135: Average Return = 151.31\n", + "Iteration 136: Average Return = 158.1\n", + "Iteration 137: Average Return = 154.29\n", + "Iteration 138: Average Return = 156.21\n", + "Iteration 139: Average Return = 159.23\n", + "Iteration 140: Average Return = 154.62\n", + "Iteration 141: Average Return = 156.02\n", + "Iteration 142: Average Return = 159.6\n", + "Iteration 143: Average Return = 159.07\n", + "Iteration 144: Average Return = 160.45\n", + "Iteration 145: Average Return = 162.08\n", + "Iteration 146: Average Return = 170.15\n", + "Iteration 147: Average Return = 163.68\n", + "Iteration 148: Average Return = 160.86\n", + "Iteration 149: Average Return = 165.91\n", + "Iteration 150: Average Return = 160.33\n", + "Iteration 151: Average Return = 156.86\n", + "Iteration 152: Average Return = 159.02\n", + "Iteration 153: Average Return = 156.21\n", + "Iteration 154: Average Return = 160.18\n", + "Iteration 155: Average Return = 151.82\n", + "Iteration 156: Average Return = 158.0\n", + "Iteration 157: Average Return = 155.82\n", + "Iteration 158: Average Return = 158.78\n", + "Iteration 159: Average Return = 158.07\n", + "Iteration 160: Average Return = 153.29\n", + "Iteration 161: Average Return = 149.6\n", + "Iteration 162: Average Return = 151.32\n", + "Iteration 163: Average Return = 156.6\n", + "Iteration 164: Average Return = 161.83\n", + "Iteration 165: Average Return = 158.11\n", + "Iteration 166: Average Return = 161.3\n", + "Iteration 167: Average Return = 155.03\n", + "Iteration 168: Average Return = 162.12\n", + "Iteration 169: Average Return = 159.91\n", + "Iteration 170: Average Return = 161.16\n", + "Iteration 171: Average Return = 164.55\n", + "Iteration 172: Average Return = 168.07\n", + "Iteration 173: Average Return = 166.54\n", + "Iteration 174: Average Return = 160.41\n", + "Iteration 175: Average Return = 160.93\n", + "Iteration 176: Average Return = 157.23\n", + "Iteration 177: Average Return = 159.94\n", + "Iteration 178: Average Return = 155.53\n", + "Iteration 179: Average Return = 157.9\n", + "Iteration 180: Average Return = 163.03\n", + "Iteration 181: Average Return = 167.13\n", + "Iteration 182: Average Return = 168.55\n", + "Iteration 183: Average Return = 157.28\n", + "Iteration 184: Average Return = 159.7\n", + "Iteration 185: Average Return = 164.65\n", + "Iteration 186: Average Return = 153.78\n", + "Iteration 187: Average Return = 161.19\n", + "Iteration 188: Average Return = 148.61\n", + "Iteration 189: Average Return = 157.44\n", + "Iteration 190: Average Return = 161.3\n", + "Iteration 191: Average Return = 158.44\n", + "Iteration 192: Average Return = 160.78\n", + "Iteration 193: Average Return = 170.6\n", + "Iteration 194: Average Return = 162.48\n", + "Iteration 195: Average Return = 172.45\n", + "Iteration 196: Average Return = 165.99\n", + "Iteration 197: Average Return = 161.56\n", + "Iteration 198: Average Return = 170.44\n", + "Iteration 199: Average Return = 173.01\n", + "Iteration 200: Average Return = 162.66\n" ] } ], @@ -750,13 +851,15 @@ { "cell_type": "code", "execution_count": 13, - "metadata": {}, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP\nGT+PHGoxuXCgblvjYaO8EJQQwufNYt1GWKTxV7Twrrrkog8favE0vc9ILgyRkWJCeIMkF0+RJWC8\nTmvdsObS0rn7dkLsIFRoHy9EJoSQ5OIhKjwSZPFK76qqAIfRkd9SzUVrDft2oobJ5EkhvEWSi6eE\nR8qGYd5WX2vpHdxyzcVWbNQqpb9FCK+R5OIpYRFQVYk+cdzXkfQc5XXJJWEM2I+h6x//UF1/ixoq\nyUUIb5Hk4in1S8BI05j31NVc1Ii6CbXN1F70vp0QGAiDhnopMCGEJBcPUa7kIk1j3qLrk8tII7no\nw01ve6D374L44ahevbwWmxA9nSQXT5GJlN5XP/R7yAjoFdRkzUU7HbB/N2qodOYL4U2SXDxFloDx\nPvsxCOwFwSHQfyD6yPeNzzn8PdRUSWe+EF4mycVTwqRZzOvsxyAswlhNe8AgaKJZTO/bASDDkIXw\nMkkuHqJ694beIbKnixfp8mPGsjsAsYOgpBB9vKbhSft2QUgf6OfeSq5CCM+Q5OJJ4RFSc/GmupoL\nAAMGgdZQWNDgFL1/JwwdgTLJR10Ib5J/cZ4UHomW5OI95WUos7H/jxowCGg4U18fr4FD+2WxSiF8\nQJKLJ4VFSrOYN9nLoS650C8OlIJTk0tejrGtccIYHwUoRM8lycWDlCxe6TW6ttZYWyysruYS1Bui\n+7mGI2uHA/3e6zAgHsaf4ctQheiR3E4uW7ZsobCwEACbzcazzz7L888/T2mpfJm6hEcay5A4Wt8V\nUXRQ/bpi5lO2xR4Q72oW0xvWwpFDmNJnoUwB3o9PiB7O7eSycuVKTHWdon//+99xOBwopVixYkWn\nBdflREQZncrWIl9H0v3Vz86v79AHVOxAOPo9+sRx9PtvGJMrT5/kqwiF6NHc3onSarUSExODw+Eg\nLy+P559/nsDAQG6//fbOjK9LUSPHogG9fROqpV0RRcfV922dWnOJHQQnjqMzX4WSQkw33oFSyjfx\nCdHDuV1zCQkJobS0lPz8fAYNGkRwcDAAtbW1nRZclxM3GCKj0Vu/9XUk3Z62lxu/mE+puQyIN577\n+J8wajyMTfZFaEII2lBzueSSS1iwYAG1tbXMnj0bgO3btzNw4MAOBWC328nIyKCoqIi+ffsyf/58\nzGZzo/PWrl3L6tWrAZg2bRpTpkwBYPHixZSWluJwOBgzZgw///nPXc133qaUQo1LRm/cgHY4UAHS\n1t9p7HWKgB70AAAgAElEQVQ1l7Cwk8dijeHIaI3pmllSaxHCh9xOLunp6Zx11lmYTCZiY40mH4vF\nwpw5czoUQGZmJomJiaSnp5OZmUlmZiazZs1qcI7dbmfVqlUsXboUgHvvvZeUlBTMZjPz588nNDQU\nrTXLli3jiy++4Ec/+lGHYuqQcWfC55/A/l3GPiOic9Tv3RJ6MrmosHCIjIb4YSeX4RdC+ESb/sSP\ni4tzJZYtW7ZQWlrK4MGDOxRATk4OqampAKSmppKTk9PonNzcXJKSkjCbzZjNZpKSksjNzQUgNDQU\nAIfDQW1trc//WlVjJ4AyobdI01insh+DUDMqsOHfR6bfLcX0i3t8FJQQop7byeXBBx9k+/btgFHb\neOqpp3jqqadcTVXtVVZWRlRUFACRkZGUlTWehGi1WomOjnY9tlgsWK1W1+PFixdz2223ERISwqRJ\nvh0dpPqEwdAR0u/S2ezHGnbm11Ex/VHBoT4ISAhxKrebxQ4ePMioUcYyGp988gkPPvggwcHBLFy4\nkGnTprV47aJFi5qcDzNz5swGj5VS7ap53H///Rw/fpynn36aLVu2kJSU1OR5WVlZZGVlAbB06VJi\nYmLa/FoAgYGBLV5rn3guFatewtI7CFNY4y/AnqK1cuoIW00V2hKNpZPu702dWU7djZSVe/yhnNxO\nLlprAI4cOQLAoEFG52lFRUWr1y5cuLDZ5yIiIrDZbERFRWGz2QgPb/xlbLFYyM/Pdz22Wq2MHduw\nTT0oKIiJEyeSk5PTbHJJS0sjLS3N9bi4uLjV2JsSExPT4rV62GhwOin+bA2miee26zW6g9bKqSMc\n1mKI7tdp9/emziyn7kbKyj2dWU5xce6tMO52s9jo0aP529/+xiuvvMLEiRMBI9GEnTpapx1SUlLI\nzs4GIDs723XvUyUnJ5OXl4fdbsdut5OXl0dycjLV1dXYbDbA6HP59ttvOzx6zSOGjTKWeZemsc5j\nP+ZatFII4X/crrnccccdvP/++4SHh3PVVVcBUFBQwGWXXdahANLT08nIyGDNmjWuocgAe/bs4eOP\nP2bOnDmYzWamT5/OggULAJgxYwZms5nS0lIeffRRTpw4gdaacePGcdFFF3UoHk9QAQEwdgJ660a0\n1j4fZNDdaK0bLrcvhPA7Ste3d/VABQUFrZ/UBHeqnM7//Rf992cxPfQMauCQdr1OV9dZVXNdVYlz\n3kzUjJsx/fgaj9/f26Spx31SVu7xh2Yxt2sutbW1rF69mnXr1rn6SM4//3ymTZtGYKDbt+kx1LjT\njaVgtn7bY5NLp6lftLIHD5YQwt+5nRVeffVV9uzZw2233Ubfvn0pKiri3XffpbKy0jVjX5ykLH2N\nVXq35sLFXf+va79St66Y9LkI4b/c7tDfsGEDv/3tb5kwYQJxcXFMmDCBe+65hy+++KIz4+vSVMIY\nOLjX12F0P66ai/S5COGv3E4uPbhrpv3iBkN5GVp2p/QoXd7EXi5CCL/idrPY5MmTeeSRR5gxY4ar\ns+jdd9/1+Yx4f6YGxKMBDh+Uv7I9qamNwoQQfsXt5DJr1izeffddVq5cic1mw2KxcM455zBjxozO\njK9ri6tbAr7gIGrUeB8H043Yj0FgIASH+DoSIUQzWkwuW7ZsafB43LhxjBs3rsHcje3btzN+vHxx\nNikqBnqHGDUX4TnlZWAOl/lDQvixFpPLn//85yaP1/+jrk8yzz77rOcj6waUUhAXjy74ztehdCva\nfqzBJmFCCP/TYnJ57rnnvBVHt6Xi4mX5fU+zH5M5LkL4Od9s2diTDBgMZTZ0RbmvI+k+ymVdMSH8\nnSSXTqbqOvWl38WDmtnLRQjhPyS5dLYBJ0eMiY7TtbVQaZfkIoSfk+TS2Sx9Iai31Fw8pbKueVHm\nDQnh1yS5dDJlMhlrjMmIMc+Q2flCdAmSXLxAxcWDNIt5Rt3sfCWjxYTwa5JcvGHAYCgtQVe2viW0\naJn+/oDxS4TFt4EIIVokycULZMSYZ2inA/3J+zB0JMT6wXbWQohmSXLxhvoRY5JcOmbjBig8jOmS\n6bL0ixB+TpKLN8T0g6Agqbl0gNYa50fvQr84OP1sX4cjhGiFJBcvUKYAiB3U7UeM6Zrqzrv59k1w\nYDfqx+lGeQoh/JrbS+53FrvdTkZGBkVFRfTt25f58+djNpsbnbd27VpWr14NwLRp05gyZUqD5x95\n5BEKCwtZtmyZN8JuMzUgHr0r39dhdBq9exvOR34HY5IwXXQ1jD/To/d3/ns1hEeiJk/16H2FEJ3D\n5zWXzMxMEhMTefrpp0lMTCQzM7PROXa7nVWrVrFkyRKWLFnCqlWrsNvtrue//PJLgoODvRl22w2I\nB2sRurrS15F0Cl1QN4rr+wM4n1mE88E7qP4syzP3/m4v5G9EpV2F6hXkkXsKITqXz5NLTk4Oqamp\nAKSmppKTk9PonNzcXJKSkjCbzZjNZpKSksjNzQWgurqaDz74gOnTp3s17rZScYONXw5/79tAOktZ\nKQCmh/+K+vlvoFcQZRkPob/b0+Fb6/+shuAQVOolHb6XEMI7fJ5cysrKiIqKAiAyMpKyssb7zVut\nVqKjo12PLRYLVqsVgDfffJMrr7ySoCA//4u2Lrnow9203+WYDcxhqN69MZ2diumexZjCInC++me0\n09nu2+rv9qJzPkOdfwkqtHFzqRDCP3mlz2XRokWUlpY2Oj5z5swGj5VSbRpiun//fo4ePcrs2bMp\nLCxs9fysrCyysoymmqVLlxITE+P2a50qMDCwzdfqqCgKAwMJOWYjrJ2v689KqyqpjYo5pVxiOH7r\nXdieeJA+uV8QevHVbb6ndjiwPvoXCI8getbtmLrprPz2fJ56Kikr9/hDOXkluSxcuLDZ5yIiIrDZ\nbERFRWGz2QgPb/wFYrFYyM8/2RlutVoZO3YsO3fuZO/evdxxxx04HA7Kysp46KGHeOihh5p8rbS0\nNNLS0lyPi4uL2/V+YmJi2ndthIWq7w9S087X9WeO4qNgDm9QLtHnpsEH71D+8nNUjByPauNik85P\n/4XelY/6+W+w1hyHmu5XbtCBz1MPJGXlns4sp7i4OLfO83mzWEpKCtnZ2QBkZ2czceLERuckJyeT\nl5eH3W7HbreTl5dHcnIyF198MStWrOC5557jj3/8I3Fxcc0mFr8QaUGXlvg6is5RZkNFRDU4pJTC\ndMMcqKlCv/tym26nS0vQq/8OY5NRZ53vyUiFEF7g8+SSnp7Opk2bmDdvHps3byY9PR2APXv2sHz5\ncgDMZjPTp09nwYIFLFiwgBkzZjQ5XNnvRVqgGyYXrbXR5xIe1eg5FTcYdVE6+vMs9G73h2I733wB\namsx3TBHZuML0QX5fJ5LWFgYDzzwQKPjCQkJJCQkuB5PnTqVqVObn+PQr18/v53jUk9FRqO3bPR1\nGJ5XXQXHj0NEZJNPqyt+aiSXNf9CjRjb6u30phz4Zj0qfRaqn3tVcCGEf/F5zaVHiYo2moiqutlc\nlzKb8bOJmguA6h0MI8eiD+xu9Vb68EGcLz4FA+JRP77Gk1EKIbxIkos3RdYNp+5uTWN1yeWHfS6n\nUoMToPBwi9sO6OKjOJ94AEwmTHfejwrs5fFQhRDeIcnFi1R9crF1r+Sij7VccwFQQ+qaOA/ubfoe\nZTacGQ/A8WpM8/8gzWFCdHGSXLwpytjgqtuNGKtvFmumzwWAwUZy0Qcaz9jXlXacTz4IpVZM8x5E\nDRrWGVEKIbxIkos3ddOaC8dsEBAILcygV+GREBUDTSWXj/8J33+H6Vf3oRLGdGakQggvkeTiRSqo\nt/EFXGr1dSieVVZqrFhsauXjNCShybXG9NaNMGwkatzpnRSgEMLbJLl4W1R0t2sW08dsEN5Ck1gd\nNTgBjn7fYGVoXWGH/btRY5M7M0QhhJdJcvG2SEv3axYrs0ELI8XqqcEJoDUc3H/y4PZNoJ2osVJr\nEaI7keTiZSoyuvs1ix0rbXEYssuQ+k79k/NddH4uBIfAsFGdFZ0QwgckuXhbVDQcK0U7HL6OxCO0\n0wHHytyruURajPNO6XfR23JhdCIq0OeLRQghPEiSi7dFRoN2nhy+29XZjxnvp4U5Lg0MTnANR9ZF\nR6DoCOo06W8RoruR5OJlqrvN0i+tn53feoc+1E2mPHwIXVNjNImBdOYL0Q1JcvG2uomU3Sa5uDE7\n/1RGp74TDu0zkktUDMQO7MQAhRC+IMnF2+pqLtrWPTr1dVndDqPudOjDyU79/btg+ybU2AmypL4Q\n3ZAkF28zhxuz2btdzcW9ZjGiYsAcjv7ff6HSDtLfIkS3JMnFy5TJ1L02DSuzQXCIsay+G5RSRu3l\n+wPG49MmdGZ0QggfkeTiC5EWdHeZSHms1P2RYnVU3SKWxA8z1hwTQnQ7klx8IdLSbSZS6jJby6sh\nN0ENGWH8lFFiQnRbklx8wJilX2LsPd/VHbOh2lhzYdQ4GDQUdVZq58QkhPA5mRbtC1HRUFMNVZUQ\n2sfX0XRMWSmMs7TpEhUWQcCDT3dSQEIIf+Dz5GK328nIyKCoqIi+ffsyf/58zObG+4KsXbuW1atX\nAzBt2jSmTJkCwEMPPYTNZiMoKAiA3//+90RERHgt/nY5dSJlF04u+ngNVFW4P1JMCNFj+Dy5ZGZm\nkpiYSHp6OpmZmWRmZjJr1qwG59jtdlatWsXSpUsBuPfee0lJSXEloXnz5pGQkOD12NtLRUajwUgu\ncYN9HU77uXagbGOzmBCi2/N5n0tOTg6pqUbbe2pqKjk5OY3Oyc3NJSkpCbPZjNlsJikpidzcXG+H\n6jn12x139YmUx4wJlG3ucxFCdHs+r7mUlZURFWV8OUVGRlJWVtboHKvVSnR0tOuxxWLBaj35xfz8\n889jMpk4++yzmT59erMzvrOyssjKygJg6dKlxMTEtCvmwMDAdl8LoMPCKARCj1dh7sB9fK16t4My\nIHLIUHo18T46Wk49hZST+6Ss3OMP5eSV5LJo0SJKS0sbHZ85c2aDx0qpNi8FMm/ePCwWC1VVVSxb\ntox169a5akI/lJaWRlpamutxcXFxm16rXkxMTLuvdQk1U1lwkOq6++hvv0Dv2orppz/v2H29yHnI\nmAhZ6lSoJsrDI+XUA0g5uU/Kyj2dWU5xcXFuneeV5LJw4cJmn4uIiMBmsxEVFYXNZiM8PLzRORaL\nhfz8fNdjq9XK2LFjXc8BhISEcO6557J79+5mk4tfiYp2TaTU1mKcLz0FVZXoK2eiQhsPaPBLZaWg\nFIT5+QAKIYTX+bzPJSUlhezsbACys7OZOHFio3OSk5PJy8vDbrdjt9vJy8sjOTkZh8PBsWPHAKit\nreWbb74hPj7eq/G3W91ESq01zlefN4YlAxzY0/J1/uSYDczhqIAAX0cihPAzPu9zSU9PJyMjgzVr\n1riGIgPs2bOHjz/+mDlz5mA2m5k+fToLFiwAYMaMGZjNZqqrq1m8eDEOhwOn00liYmKDZi9/piKj\n0Yf2o79cC5u/Rl32E/SHb6O/2+PR9bZ0dRX68yzUlMs8ngSM2fnSmS+EaMznySUsLIwHHnig0fGE\nhIQGw4unTp3K1KlTG5wTHBzMI4880ukxdor67Y7f/CskjEFdfR16w6cer7noT95HZ76KGhAPnl5u\npR3rigkhegafN4v1WJHRoDXUVGH62VyUKQCGJKAP7PbYS2iHA73u38bvhw+1/fq9O3C+8Dj6xImm\nTyizub0DpRCiZ5Hk4iMquq/x88rrjFoFdQs6Fh5GV1Z45kU25YC1bsTI4e/afLn+ap3xX87/Gj/n\ndBp9LhFtW/pFCNEzSHLxldOSMd25EHXJNNchVbdLI995pmnMufZDY3OuYaPaV3Opi0Nn/bPRIpv6\n68+gthY1dKQnQhVCdDOSXHxEBQSgJkw0msPq1S1Frz3Q76KPfA/5uajzf4waOAQOH2zb9U4nfLfP\nWDfs4D7YueWU5xzo9980lq45fVKHYxVCdD+SXPyICosASwx4oN9Fr/0QAgJR518MA+KhvAxdfsz9\nGxQehpoq1BU/BXM4zo//efLeX/0PjhzCdNX1xs6aQgjxA/LN4G8Gj+hwzUXXVKPXr0GdeQ4qPMrV\np9OW2os+uBcAlXAaKvUS2JSDLiwwBgm8/yYMGia1FiFEsyS5+Bk1JAEKCzrUqa+/zIaqCtSUy4wD\ncUZy0Ufa0DR2YA8EBkJcvHEfUwA6631jXk5hAaarr5NaixCiWfLt4GfqtwCmruYAoEsKcdx/O3p3\nfjNXNaTXfgiDhsKI04wDUTEQ1BsK2lBz+W4PxA1BBfZCRVpQZ52HXv8J+r03YHACTDjb7XsJIXoe\nSS7+pm7E2KnzXXTmq8YQ5c3ftHq5riiHg/tQZ6W6FgFVJhMMiHd7xJjWGg7uPTl6DVBpVxu7Z5YU\nGn0tbVxgVAjRs/h8hr5oSIVHGjWNun4X/d0e9Ia1rt9bdbTAuM+AQQ3vO2AQeseWpq5ozFoM9nKI\nH37y+sHDYfwZUF0NSSnu3UcI0WNJcvFHQxJcnfrOd1+GPmEwahzs3obWusVag65LLvT/wbLYA+Jh\nw1p0VSUqJLTl1z9ovLYaPLzBYdMdvwdafn0hhABpFvNLakgCHP0e/c16Y67K5T9BjUmC8jKwtbJH\nQ2EBKBPExDa8Z/2IsSOtN43pA3uNewwa1vAegYGowF5tei9CiJ5Jkosfqu/Ud/79GYjuZ6xoXN/R\n31rT2NECiO6L6vWDJFCXXLQbw5H1d3sgdiCqd+82xy6EECDJxT/Vd6RXVqDSZxmJYtAwUKZW58Do\nwsPQr4md4vrGGkOL3Rkx9l3DznwhhGgrSS5+SIVHQXQ/GDwcddb5xrHevWHAoBaTi9Yajn6P6j+g\n8T0DAqD/QHQrzWL6mA1KSxp05gshRFtJh76fMv36QQgJbTBRUQ1JQOfnNn9ReSlUV0H/gU0+rWIH\ntT7i7Lu9rtcSQoj2kpqLn1ID4lGR0Q0PDhkBZTZ0aUnTFx09bFzbVLMYGP0uxYXo4zXNvq6uSy7E\nD2v2HCGEaI0kly5EDa6rTRzY2+TzurB+GHLjZjHAWAZGO11zYZq8x3d7oG8sKtTckVCFED2cJJeu\nJH4YKNX8bpVHCyAgAKL7N/l0/cTKFkeMfbcXBkt/ixCiY3ze52K328nIyKCoqIi+ffsyf/58zObG\nfzWvXbuW1atXAzBt2jSmTJkCQG1tLStXriQ/Px+lFDNnzmTSpO65Wq8KDjE65ZvpN9FHCyC6v9F5\n35T+A435K80kF11ZAUVHUD9K81TIQogeyufJJTMzk8TERNLT08nMzCQzM5NZs2Y1OMdut7Nq1SqW\nLl0KwL333ktKSgpms5nVq1cTERHBU089hdPpxG63++JteI0aktD8Mi6FBY1n5p96ba8g6Nu/+ZrL\n/p3GebK7pBCig3zeLJaTk0NqaioAqamp5OTkNDonNzeXpKQkzGYzZrOZpKQkcnONUVOffvop6enp\nAJhMJsLDw70XvC8MGQGlJcaQ4VNoraHwMKqF5AIYnfrNzHXRu/KNmk3CaE9FK4TooXxecykrKyMq\nKgqAyMhIysrKGp1jtVqJjj45cspisWC1WqmoMPY8eeutt8jPz6d///7ccsstREZGeid4H1CDE9Bg\ndOonnnnyiVIrHK9pegLlqdfHD0dv+hpdYUf1adj8qHflQ/wwVHAra48JIUQrvJJcFi1aRGlpaaPj\nM2fObPBYKdWmRREdDgclJSWMHj2an/3sZ3zwwQe88sorzJ07t8nzs7KyyMrKAmDp0qXExMS04V2c\nFBgY2O5rO8oZOpEiIKS4AHPMj13Hjx/5DhsQMXIMvVuI7fjk87F98CZhBfsJnjzFdVzX1lK4bych\nF11FuIfemy/LqSuRcnKflJV7/KGcvJJcFi5c2OxzERER2Gw2oqKisNlsTTZrWSwW8vNPbpRltVoZ\nO3YsYWFh9O7dm7POOguASZMmsWbNmmZfKy0tjbS0k53VxcWtLALZjJiYmHZf6xH9B1KxbTPVp8Tg\n3GmUz7EQM6qF2LQlFnqHcOzLddhHjj95fN9OOF5DzaBhHntvPi+nLkLKyX1SVu7pzHKKi2ul6b2O\nz/tcUlJSyM7OBiA7O5uJEyc2Oic5OZm8vDzsdjt2u528vDySk5NRSnHmmWe6Es+WLVsYNGhQo+u7\nGzV4uGu/F5ejhyGwl7EXTEvXBgbC6PGNZvrrXXXJu373SiGE6ACfJ5f09HQ2bdrEvHnz2Lx5s6tz\nfs+ePSxfvhwAs9nM9OnTWbBgAQsWLGDGjBmu4co33HAD77zzDvfccw/r1q3jpptu8tl78ZqhI8Fa\n1GDUly4sMCY/urGvvRqbDEVH0EVHTl6/O9+4/oerAgghRDv4vEM/LCyMBx54oNHxhIQEEhJOrm81\ndepUpk6d2ui8vn378oc//KFTY/Q3avIF6PfeQP/zddSc3xkHj7Y8DLnB9WOT0YDelofqG2uMNNu9\nDTX+jM4LWgjRo/i85iLaToVFoC66Cv3N58Y2yE4HFB1ufk2xH4odBJHRUN80drTA2IhsxNjOC1oI\n0aNIcumi1EXpEGrGmfmased9ba37NReljNrL9k1opwO9a6txfOS4zgxZCNGDSHLpolRoH9Sl02Hz\n1+j1nxjH3EwuAIxNhopyYy2x3dvAHAaxTS/VL4QQbSXJpQtTF1wBEVHoD98xDrjbLAao05IAo99F\n786HEWPbNMdICCFaIsmlC1O9e6Mu/yk4HBDUGyIt7l8bHgWDhqK/zDaWjZH+FiGEB0ly6eLUeRcZ\nWyLHDmpzzUONTYbvDxi/y/wWIYQH+XwosugYFdgL0/w/Gh36bb32tGT0fzOhVxDItsZCCA+S5NIN\ntKkj/1Qjx0FgIAwbhQrs5dmghBA9miSXHkz17o2a+QtU31hfhyKE6GYkufRwptRLfB2CEKIbkg59\nIYQQHifJRQghhMdJchFCCOFxklyEEEJ4nCQXIYQQHifJRQghhMdJchFCCOFxklyEEEJ4nNJaa18H\nIYQQonuRmks73Hvvvb4OoUuQcnKPlJP7pKzc4w/lJMlFCCGEx0lyEUII4XGSXNohLS3N1yF0CVJO\n7pFycp+UlXv8oZykQ18IIYTHSc1FCCGEx8l+Lm2Qm5vLiy++iNPp5MILLyQ9Pd3XIfmN4uJinnvu\nOUpLS1FKkZaWxmWXXYbdbicjI4OioiL69u3L/PnzMZvNvg7X55xOJ/feey8Wi4V7772XwsJCnnzy\nScrLyxk+fDhz584lMLBn//OsqKhg+fLlHDx4EKUUv/zlL4mLi5PP0w988MEHrFmzBqUU8fHx/OpX\nv6K0tNTnnyepubjJ6XSycuVK7rvvPjIyMvj88885dOiQr8PyGwEBAdx4441kZGSwePFi/vOf/3Do\n0CEyMzNJTEzk6aefJjExkczMTF+H6hc+/PBDBg4c6Hr86quvcvnll/PMM8/Qp08f1qxZ48Po/MOL\nL75IcnIyTz75JI899hgDBw6Uz9MPWK1WPvroI5YuXcqyZctwOp2sX7/eLz5PklzctHv3bmJjY+nf\nvz+BgYGcc8455OTk+DosvxEVFcXw4cMBCAkJYeDAgVitVnJyckhNTQUgNTVVygwoKSnh22+/5cIL\nLwRAa83WrVuZNGkSAFOmTOnx5VRZWcm2bduYOnUqAIGBgfTp00c+T01wOp0cP34ch8PB8ePHiYyM\n9IvPU8+ud7eB1WolOjra9Tg6Oppdu3b5MCL/VVhYyL59+xgxYgRlZWVERUUBEBkZSVlZmY+j872X\nXnqJWbNmUVVVBUB5eTmhoaEEBAQAYLFYsFqtvgzR5woLCwkPD+f555/nwIEDDB8+nNmzZ8vn6Qcs\nFgtXXnklv/zlLwkKCmLChAkMHz7cLz5PUnMRHlVdXc2yZcuYPXs2oaGhDZ5TSqGU8lFk/uGbb74h\nIiLCVcsTTXM4HOzbt4+LL76YRx99lN69ezdqApPPE9jtdnJycnjuuedYsWIF1dXV5Obm+josQGou\nbrNYLJSUlLgel5SUYLFYfBiR/6mtrWXZsmWcd955nH322QBERERgs9mIiorCZrMRHh7u4yh9a8eO\nHXz99dds3LiR48ePU1VVxUsvvURlZSUOh4OAgACsVmuP/2xFR0cTHR3NyJEjAZg0aRKZmZnyefqB\nzZs3069fP1c5nH322ezYscMvPk9Sc3FTQkIChw8fprCwkNraWtavX09KSoqvw/IbWmuWL1/OwIED\nueKKK1zHU1JSyM7OBiA7O5uJEyf6KkS/cP3117N8+XKee+457rrrLsaPH8+8efMYN24cGzZsAGDt\n2rU9/rMVGRlJdHQ0BQUFgPElOmjQIPk8/UBMTAy7du2ipqYGrbWrnPzh8ySTKNvg22+/5eWXX8bp\ndHLBBRcwbdo0X4fkN7Zv384DDzzA4MGDXU0V1113HSNHjiQjI4Pi4mIZOvoDW7du5f333+fee+/l\n6NGjPPnkk9jtdoYNG8bcuXPp1auXr0P0qf3797N8+XJqa2vp168fv/rVr9Bay+fpB95++23Wr19P\nQEAAQ4cOZc6cOVitVp9/niS5CCGE8DhpFhNCCOFxklyEEEJ4nCQXIYQQHifJRQghhMdJchFCCOFx\nklyEcMPdd9/N1q1bffLaxcXF3HjjjTidTp+8vhDtIUORhWiDt99+myNHjjBv3rxOe4077riD22+/\nnaSkpE57DSE6m9RchPAih8Ph6xCE8AqpuQjhhjvuuINbbrmFxx9/HDCWgI+NjeWxxx6jsrKSl19+\nmY0bN6KU4oILLuAnP/kJJpOJtWvX8sknn5CQkMC6deu4+OKLmTJlCitWrODAgQMopZgwYQK33nor\nffr04ZlnnuGzzz4jMDAQk8nEjBkzmDx5MnfeeSdvvPGGa62oF154ge3bt2M2m7n66qtde6a//fbb\nHDp0iKCgIL766itiYmK44447SEhIACAzM5OPPvqIqqoqoqKi+PnPf05iYqLPylV0X7JwpRBu6tWr\nF33NSBIAAAMxSURBVNdcc02jZrHnnnuOiIgInn76aWpqali6dCnR0dFcdNFFAOzatYtzzjmHF154\nAYfDgdVq5ZprruG0006jqqqKZcuW8c477zB79mzmzp3L9u3bGzSLFRYWNojjqaeeIj4+nhUrVlBQ\nUMCiRYuIjY1l/PjxgLHy8m9+8xt+9atf8eabb/K3v/2NxYsXU1BQwH/+8x8efvhhLBYLhYWF0o8j\nOo00iwnRAaWlpWzcuJHZs2cTHBxMREQEl19+OevXr3edExUVxaWXXkpAQABBQUHExsaSlJREr169\nCA8P5/LLLyc/P9+t1ysuLmb79u3ccMMNBAUFMXToUC688ELXYo4AY8aM4YwzzsBkMnH++eezf/9+\nAEwmEydOnODQoUOu9bpiY2M9Wh5C1JOaixAdUFxcjMPh4Be/+IXrmNa6wcZyMTExDa4pLS3lpZde\nYtu2bVRXV+N0Ot1efNFms2E2mwkJCWlw/z179rgeR0REuH4PCgrixIkTOBwOYmNjmT17Nu+88w6H\nDh1iwoQJ3HTTTT1+eX/ROSS5CNEGP9ycKjo6msDAQFauXOna+a81b7zxBgDLli3DbDbz1Vdf8be/\n/c2ta6OiorDb7VRVVbkSTHFxsdsJ4txzz+Xcc8+lsrKSv/zlL7z22mvMnTvXrWuFaAtpFhOiDSIi\nIigqKnL1VURFRTFhwgT+/ve/U1lZidPp5MiRIy02c1VVVREcHExoaChWq5X333+/wfORkZGN+lnq\nxcTEMHr0aF5//XWOHz/OgQMH+PTTTznvvPNajb2goIAtW7Zw4sQJgoKCCAoK6vE7OYrOI8lFiDaY\nPHkyALfeeiu/+93vALjzzjupra3l7rvv5uabb+aJJ57AZrM1e49rr72Wffv28bOf/YyHH36Ys846\nq8Hz6enpvPvuu8yePZv33nuv0fW//vWvKSoq4vbbb+fxxx/n2muvdWtOzIkTJ3jttde49dZbue22\n2zh27BjXX399W96+EG6TochCCCE8TmouQgghPE6SixBCCI+T5CKEEMLjJLkIIYTwOEkuQgghPE6S\nixBCCI+T5CKEEMLjJLkIIYTwOEkuQgghPO7/AdYWXsNU6TlEAAAAAElFTkSuQmCC\n", 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xGkEjmufCY1HxONVzkeLy4vMihKaWwgJQPZf+jJ5LvEutjqrVtrZIjJaRrxvo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\nzc1gjOH3339HdHS0qJ1Le3s7Dh8+jE2bNmHSpEnC8mvXrsH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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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r4p6gQpxnnOqz6NGjB3l5ea7OIsTZHdwDNdXm+tGVFafdbdhzIKCjOU/TkTR0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\nD3X5r1F52RjLF6NGXOzpeEII0WRSLFx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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -802,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -848,6 +951,7 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " a = util.discount(a, self.discount_rate*LAMBDA)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -868,7 +972,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, "metadata": { "scrolled": true }, @@ -877,90 +981,86 @@ "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 = 24.73\n", + "Iteration 2: Average Return = 23.87\n", + "Iteration 3: Average Return = 27.47\n", + "Iteration 4: Average Return = 33.18\n", + "Iteration 5: Average Return = 31.91\n", + "Iteration 6: Average Return = 35.31\n", + "Iteration 7: Average Return = 40.03\n", + "Iteration 8: Average Return = 39.31\n", + "Iteration 9: Average Return = 42.83\n", + "Iteration 10: Average Return = 44.94\n", + "Iteration 11: Average Return = 50.35\n", + "Iteration 12: Average Return = 51.74\n", + "Iteration 13: Average Return = 49.53\n", + "Iteration 14: Average Return = 51.19\n", + "Iteration 15: Average Return = 52.05\n", + "Iteration 16: Average Return = 54.92\n", + "Iteration 17: Average Return = 59.76\n", + "Iteration 18: Average Return = 58.6\n", + "Iteration 19: Average Return = 61.67\n", + "Iteration 20: Average Return = 64.15\n", + "Iteration 21: Average Return = 68.03\n", + "Iteration 22: Average Return = 68.49\n", + "Iteration 23: Average Return = 78.09\n", + "Iteration 24: Average Return = 74.99\n", + "Iteration 25: Average Return = 80.57\n", + "Iteration 26: Average Return = 85.57\n", + "Iteration 27: Average Return = 84.63\n", + "Iteration 28: Average Return = 88.31\n", + "Iteration 29: Average Return = 85.01\n", + "Iteration 30: Average Return = 91.76\n", + "Iteration 31: Average Return = 97.26\n", + "Iteration 32: Average Return = 96.99\n", + "Iteration 33: Average Return = 97.67\n", + "Iteration 34: Average Return = 111.77\n", + "Iteration 35: Average Return = 113.64\n", + "Iteration 36: Average Return = 113.43\n", + "Iteration 37: Average Return = 114.7\n", + "Iteration 38: Average Return = 125.34\n", + "Iteration 39: Average Return = 126.87\n", + "Iteration 40: Average Return = 125.36\n", + "Iteration 41: Average Return = 129.83\n", + "Iteration 42: Average Return = 135.94\n", + "Iteration 43: Average Return = 139.24\n", + "Iteration 44: Average Return = 147.21\n", + "Iteration 45: Average Return = 156.18\n", + "Iteration 46: Average Return = 163.94\n", + "Iteration 47: Average Return = 162.78\n", + "Iteration 48: Average Return = 159.45\n", + "Iteration 49: Average Return = 159.3\n", + "Iteration 50: Average Return = 161.98\n", + "Iteration 51: Average Return = 161.6\n", + "Iteration 52: Average Return = 170.6\n", + "Iteration 53: Average Return = 175.29\n", + "Iteration 54: Average Return = 175.56\n", + "Iteration 55: Average Return = 181.96\n", + "Iteration 56: Average Return = 178.31\n", + "Iteration 57: Average Return = 175.13\n", + "Iteration 58: Average Return = 177.73\n", + "Iteration 59: Average Return = 180.48\n", + "Iteration 60: Average Return = 181.43\n", + "Iteration 61: Average Return = 181.03\n", + "Iteration 62: Average Return = 183.87\n", + "Iteration 63: Average Return = 185.26\n", + "Iteration 64: Average Return = 190.9\n", + "Iteration 65: Average Return = 191.03\n", + "Iteration 66: Average Return = 191.6\n", + "Iteration 67: Average Return = 192.31\n", + "Iteration 68: Average Return = 193.8\n", + "Iteration 69: Average Return = 193.29\n", + "Iteration 70: Average Return = 192.18\n", + "Iteration 71: Average Return = 192.67\n", + "Iteration 72: Average Return = 192.53\n", + "Iteration 73: Average Return = 191.81\n", + "Iteration 74: Average Return = 194.7\n", + "Iteration 75: Average Return = 193.82\n", + "Iteration 76: Average Return = 193.92\n", + "Iteration 77: Average Return = 191.33\n", + "Iteration 78: Average Return = 194.29\n", + "Iteration 79: Average Return = 195.03\n", + "Solve at 79 iterations, which equals 7900 episodes.\n" ] } ], @@ -983,14 +1083,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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z2bDBnN28YcMG5syZ067MzJkz2bp1KzU1NdTU1LB161ZmzpzZo/v2lIr1JEGZ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\nbFQVbVzDf7AAAAAASUVORK5CYII=\n", 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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 4ba3272..fdb3742 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,8 +32,8 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> - b.append(0) - b_t_next = b[1:] + b[0] = 0.0 + b_t_next = np.roll(b,-1) y = x + discount_rate*b_t_next return y # <<<<<<<<<<<<<<<<<<<<<<<<<<<< From f8d3253162561653e02d5199ed553746d76aa4e2 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 14:17:59 +0800 Subject: [PATCH 07/20] Update report.md --- report.md | 97 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 97 insertions(+) diff --git a/report.md b/report.md index 1e5017e..61fd4e8 100644 --- a/report.md +++ b/report.md @@ -1,3 +1,100 @@ + +# 江愷笙 (106062568) + # Homework3-Policy-Gradient report TA: try to elaborate the algorithms that you implemented and any details worth mentioned. + +## Overview + +>Policy gradient methods are a type of reinforcement learning techniques that rely upon optimizing parametrized policies with respect to the expected return (long-term cumulative reward) by gradient descent. They do not suffer from many of the problems that have been marring traditional reinforcement learning approaches such as the lack of guarantees of a value function, the intractability problem resulting from uncertain state information and the complexity arising from continuous states & actions. +## Implementation + +In this homework we have to use policy gradient method to solve the cartpole problem. +* Problem 1 + +In problem 1 we have to use tensorflow to construct DQN for the policy gradient. Here we have to add the softmax layer to obtain the probability distribution. + +```python +fc = tf.layers.dense(self._observations, hidden_dim, tf.tanh) +probs = tf.layers.dense(fc, out_dim, tf.nn.softmax) +``` + +* Problem 2 + +In problem 2 we have to define our lost for the neural network, here the loss function is surrogate loss. However, we have to take care that for the optimizer in tensorflow, the task is to minimize the loss (gradient descent), but in policy gradient, we have to maximize the surrogate loss (gradient asscent). So here we take the negative number of the loss to minimize this negative number, which equals to maximize its positive number. + + + + + + + +```python +surr_loss = tf.reduce_mean(-log_prob * self._advantages) +``` + +the surrogate loss should take the average number over N episode and each time step, so we use tf.reduce_mean to obtain the average number. + +* Problem 3 + +Here in problem 3 we use baseline to reduce the variance. So we replace the loss function by the formula shown below. + + + + + + + + +```python +a = r - b +``` + +## Installation +* Anaconda +* Ipython notebook +* Python3.5 +* OpenAI gym +* Tensorflow +* to run the code, open Lab3-policy-gradient.ipynb by using Ipython notebook and execute each block. + +## Results +* Value Iteration + +Here we can see the action and value update for each state. + + + + + + + + + + + + + + + + + +* Policy Iteration + + + + + + + +* Tabular Q-learning + +By finishing the tabular Q-learning, we can see a crawling robot moving fast from the left to the right. + + + + + + + From c92881975434bbce37317527ec583ff8c1adab99 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 14:18:33 +0800 Subject: [PATCH 08/20] add images --- imgs/baseline.PNG | Bin 0 -> 4876 bytes imgs/baseline2.PNG | Bin 0 -> 1360 bytes imgs/surrogate_loss.PNG | Bin 0 -> 4339 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 imgs/baseline.PNG create mode 100644 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5W;nRu>_mDNJK8)Y(L*W zH?m$uYME4Dv*1`2;#XL>-;wI<_p|$TG7bG|k)1!v1rL#y^m|Gdc_His~ zkKtkgT^^uli;g6%cVC6K*fbt~*WlP6z;nW2^M`zkDk(ADKR-DmhL|H~SD1C`(ue7_ zzL5B47EW{S%^>k6`0(~Y?1EPp%S3%h@2b0zGv{!CBI))8(ICeU;cYv4r2c7##nwkF zX`DRR9<^bipnNv&+2F5q#BF&<8)PM}*01Wrke^o1k7V>bF34vGr~DXHDTs%~IvLUO zO%A>$#EMHLnXYNPd4pfCMe*US^&!7QyFZN)mK+UOUB*6nO?SoT7<8iQMmEEb15DnlS Date: Thu, 9 Nov 2017 15:09:57 +0800 Subject: [PATCH 09/20] Update report.md --- report.md | 105 +++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 85 insertions(+), 20 deletions(-) diff --git a/report.md b/report.md index 61fd4e8..75ff60e 100644 --- a/report.md +++ b/report.md @@ -24,11 +24,13 @@ probs = tf.layers.dense(fc, out_dim, tf.nn.softmax) In problem 2 we have to define our lost for the neural network, here the loss function is surrogate loss. However, we have to take care that for the optimizer in tensorflow, the task is to minimize the loss (gradient descent), but in policy gradient, we have to maximize the surrogate loss (gradient asscent). So here we take the negative number of the loss to minimize this negative number, which equals to maximize its positive number. + +
```python surr_loss = tf.reduce_mean(-log_prob * self._advantages) @@ -40,17 +42,74 @@ the surrogate loss should take the average number over N episode and each time s Here in problem 3 we use baseline to reduce the variance. So we replace the loss function by the formula shown below. + +
```python a = r - b ``` +* Problem 4 + +In problem 4 we remove the baseline to see what is the difference between problem 3 and 4, I will discuss it in Results. + +To remove the baseline, just replace + +```python +baseline = LinearFeatureBaseline(env.spec) +``` + +by + +```python +baseline = None +``` + +* Problem 5 + +In problem 5 we have to implement a simple actor critic algorithm by replacing the first formula below in problem 3 with the second formula. + + + + + +
+ + +
+ +We have to add the original reward to the discounted baseline at the next time step, so we have to left shift the baseline by 1. + +```python +b[0] = 0.0 # let the first value to 0, after we shift left, it will be the last value +b_t_next = np.roll(b,-1) # shift left by 1 +y = x + discount_rate*b_t_next # new R +return y +``` + +* Problem 6 + +Finally, we introduce generalized advantage estimation (GAE), which uses a hyperparameter lambda to comprimise two methods in problem 3 and 5. + + + + + +
+ + +
+ +```python +a = util.discount(a, self.discount_rate*LAMBDA) +``` + ## Installation * Anaconda * Ipython notebook @@ -60,41 +119,47 @@ a = r - b * to run the code, open Lab3-policy-gradient.ipynb by using Ipython notebook and execute each block. ## Results -* Value Iteration - -Here we can see the action and value update for each state. +* Problem 1~3 + +
- - - - - - - - - - - + +
-* Policy Iteration +* Problem 4 + +
- + +
-* Tabular Q-learning +* Problem 5 -By finishing the tabular Q-learning, we can see a crawling robot moving fast from the left to the right. + + + + +
+ + +
+* Problem 6 + + +
- - + +
+ From 80213eda6324b0ad731875eacf5ed5debd7ceccd Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 15:10:20 +0800 Subject: [PATCH 10/20] add images --- imgs/GAE1.PNG | Bin 0 -> 2355 bytes imgs/GAE2.PNG | Bin 0 -> 1720 bytes imgs/actor.PNG | Bin 0 -> 1869 bytes 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 imgs/GAE1.PNG create mode 100644 imgs/GAE2.PNG create mode 100644 imgs/actor.PNG diff --git a/imgs/GAE1.PNG b/imgs/GAE1.PNG new file mode 100644 index 0000000000000000000000000000000000000000..d665a4d5221fa6c787965c4403096a8049e9f08d GIT binary patch literal 2355 zcmV-33C#A1P)Px#1ZP1_K>z@;j|==^1poj532;bRa{vGqB>(^xB>_oNB=7(L2*^o9K~#8N?VO8I zBRdd9$pA8d3?Ku@05X6KAOpw%GJp&q1IU072!Rm%IFxT~KcP=+JRXnjK2<8PMx#%w z`|9Im4;DcX>^&g_K@fxx1VM}U_xHu+<>lh^^mLiCdxQ`Kfdho#`uckLY4->r2m%L( zhlh*j=jY|8-D9mn@csS0`1tr(JUl!sUS3`nUteFp`JbPkzdu{NzP>Jh`-|$XCCUWB zs^G5&2M0UtPiqkZ3`X#upP&D#C}%94vyP6Aet)*$J>IXO36fgV#^dAT;`a7-=O-?O 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z*R|>~-dMH~!tW5GO%#37o%0BfhTXbiJjEN-nL@E&WNxF1LA%N`UDx^34TiWsT^A=g z%wgFfhV+H67ha)8$(4hKu^Rit5|D;fPe`i)kxD-+SWrOoeVK_7at6FL4iwV7GKpft zvYrkbB-beR!0b94yBCfw9lE@=&z?zh1OxvNOX+w+s~5%iIJ3nn{zZ>CYS5|bl^ScF zctO$D($ea@vU&q@@!-00SPs+dV(i3}-*#ixtYkVgdTZ`}eXpCm%;n`zls>K|N1I zqGcC2LAx3HsMuBFa-R0^7VOArDmsLn44L%$XxA)xCR45& zGAr!ys=G}t)Wvf|lk>QSWdS@m^Bw<9UXfC*FEILOrZWQ@3$zVzVn;9;P#_r*@(9 Date: Thu, 9 Nov 2017 17:17:11 +0800 Subject: [PATCH 11/20] Update report.md --- Lab3-policy-gradient.ipynb | 918 +++++++++++++++++++------------------ report.md | 21 +- 2 files changed, 490 insertions(+), 449 deletions(-) diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index d3ede73..3ddadb5 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -3,7 +3,9 @@ { "cell_type": "code", "execution_count": 1, - "metadata": {}, + "metadata": { + "collapsed": true + }, "outputs": [], "source": [ "# Automatically reload changes to external code\n", @@ -258,65 +260,91 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 36.3\n", - "Iteration 2: Average Return = 35.62\n", - "Iteration 3: Average Return = 41.83\n", - "Iteration 4: Average Return = 41.59\n", - "Iteration 5: Average Return = 45.35\n", - "Iteration 6: Average Return = 47.75\n", - "Iteration 7: Average Return = 43.01\n", - "Iteration 8: Average Return = 48.09\n", - "Iteration 9: Average Return = 46.28\n", - "Iteration 10: Average Return = 49.92\n", - "Iteration 11: Average Return = 50.93\n", - "Iteration 12: Average Return = 51.42\n", - "Iteration 13: Average Return = 51.7\n", - "Iteration 14: Average Return = 50.86\n", - "Iteration 15: Average Return = 51.94\n", - "Iteration 16: Average Return = 56.21\n", - "Iteration 17: Average Return = 54.27\n", - "Iteration 18: Average Return = 56.5\n", - "Iteration 19: Average Return = 62.7\n", - "Iteration 20: Average Return = 62.08\n", - "Iteration 21: Average Return = 65.68\n", - "Iteration 22: Average Return = 67.45\n", - "Iteration 23: Average Return = 68.25\n", - "Iteration 24: Average Return = 65.85\n", - "Iteration 25: Average Return = 68.73\n", - "Iteration 26: Average Return = 73.22\n", - "Iteration 27: Average Return = 74.65\n", - "Iteration 28: Average Return = 82.6\n", - "Iteration 29: Average Return = 88.59\n", - "Iteration 30: Average Return = 93.62\n", - "Iteration 31: Average Return = 94.23\n", - "Iteration 32: Average Return = 107.5\n", - "Iteration 33: Average Return = 117.67\n", - "Iteration 34: Average Return = 130.97\n", - "Iteration 35: Average Return = 144.9\n", - "Iteration 36: Average Return = 150.8\n", - "Iteration 37: Average Return = 146.76\n", - "Iteration 38: Average Return = 143.07\n", - "Iteration 39: Average Return = 151.75\n", - "Iteration 40: Average Return = 143.04\n", - "Iteration 41: Average Return = 152.31\n", - "Iteration 42: Average Return = 145.89\n", - "Iteration 43: Average Return = 145.5\n", - "Iteration 44: Average Return = 150.78\n", - "Iteration 45: Average Return = 151.63\n", - "Iteration 46: Average Return = 154.9\n", - "Iteration 47: Average Return = 160.54\n", - "Iteration 48: Average Return = 167.68\n", - "Iteration 49: Average Return = 171.19\n", - "Iteration 50: Average Return = 180.17\n", - "Iteration 51: Average Return = 182.87\n", - "Iteration 52: Average Return = 190.01\n", - "Iteration 53: Average Return = 188.21\n", - "Iteration 54: Average Return = 191.13\n", - "Iteration 55: Average Return = 193.14\n", - "Iteration 56: Average Return = 191.94\n", - "Iteration 57: Average Return = 193.99\n", - "Iteration 58: Average Return = 195.12\n", - "Solve at 58 iterations, which equals 5800 episodes.\n" + "Iteration 1: Average Return = 20.45\n", + "Iteration 2: Average Return = 18.67\n", + "Iteration 3: Average Return = 19.21\n", + "Iteration 4: Average Return = 20.59\n", + "Iteration 5: Average Return = 23.19\n", + "Iteration 6: Average Return = 21.18\n", + "Iteration 7: Average Return = 24.79\n", + "Iteration 8: Average Return = 23.09\n", + "Iteration 9: Average Return = 25.96\n", + "Iteration 10: Average Return = 27.52\n", + "Iteration 11: Average Return = 27.33\n", + "Iteration 12: Average Return = 31.51\n", + "Iteration 13: Average Return = 33.2\n", + "Iteration 14: Average Return = 33.66\n", + "Iteration 15: Average Return = 33.37\n", + "Iteration 16: Average Return = 42.08\n", + "Iteration 17: Average Return = 37.26\n", + "Iteration 18: Average Return = 43.06\n", + "Iteration 19: Average Return = 40.63\n", + "Iteration 20: Average Return = 40.84\n", + "Iteration 21: Average Return = 40.37\n", + "Iteration 22: Average Return = 45.1\n", + "Iteration 23: Average Return = 43.67\n", + "Iteration 24: Average Return = 46.74\n", + "Iteration 25: Average Return = 46.12\n", + "Iteration 26: Average Return = 44.35\n", + "Iteration 27: Average Return = 49.1\n", + "Iteration 28: Average Return = 48.62\n", + "Iteration 29: Average Return = 51.33\n", + "Iteration 30: Average Return = 51.07\n", + "Iteration 31: Average Return = 50.5\n", + "Iteration 32: Average Return = 54.02\n", + "Iteration 33: Average Return = 52.56\n", + "Iteration 34: Average Return = 57.02\n", + "Iteration 35: Average Return = 55.55\n", + "Iteration 36: Average Return = 59.29\n", + "Iteration 37: Average Return = 57.34\n", + "Iteration 38: Average Return = 62.87\n", + "Iteration 39: Average Return = 63.65\n", + "Iteration 40: Average Return = 64.12\n", + "Iteration 41: Average Return = 60.31\n", + "Iteration 42: Average Return = 63.77\n", + "Iteration 43: Average Return = 65.99\n", + "Iteration 44: Average Return = 63.3\n", + "Iteration 45: Average Return = 64.73\n", + "Iteration 46: Average Return = 65.91\n", + "Iteration 47: Average Return = 67.49\n", + "Iteration 48: Average Return = 68.8\n", + "Iteration 49: Average Return = 67.64\n", + "Iteration 50: Average Return = 68.82\n", + "Iteration 51: Average Return = 70.26\n", + "Iteration 52: Average Return = 78.53\n", + "Iteration 53: Average Return = 73.5\n", + "Iteration 54: Average Return = 80.04\n", + "Iteration 55: Average Return = 77.22\n", + "Iteration 56: Average Return = 84.89\n", + "Iteration 57: Average Return = 82.58\n", + "Iteration 58: Average Return = 87.67\n", + "Iteration 59: Average Return = 88.29\n", + "Iteration 60: Average Return = 90.95\n", + "Iteration 61: Average Return = 99.53\n", + "Iteration 62: Average Return = 94.45\n", + "Iteration 63: Average Return = 107.04\n", + "Iteration 64: Average Return = 112.56\n", + "Iteration 65: Average Return = 123.9\n", + "Iteration 66: Average Return = 132.01\n", + "Iteration 67: Average Return = 146.82\n", + "Iteration 68: Average Return = 146.05\n", + "Iteration 69: Average Return = 152.7\n", + "Iteration 70: Average Return = 152.27\n", + "Iteration 71: Average Return = 157.93\n", + "Iteration 72: Average Return = 157.28\n", + "Iteration 73: Average Return = 160.44\n", + "Iteration 74: Average Return = 161.47\n", + "Iteration 75: Average Return = 167.08\n", + "Iteration 76: Average Return = 163.13\n", + "Iteration 77: Average Return = 167.54\n", + "Iteration 78: Average Return = 171.92\n", + "Iteration 79: Average Return = 175.66\n", + "Iteration 80: Average Return = 184.96\n", + "Iteration 81: Average Return = 182.74\n", + "Iteration 82: Average Return = 184.35\n", + "Iteration 83: Average Return = 185.37\n", + "Iteration 84: Average Return = 196.11\n", + "Solve at 84 iterations, which equals 8400 episodes.\n" ] } ], @@ -343,9 +371,9 @@ "outputs": [ { "data": { - "image/png": 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vaQKDASy+w5tF/mKVLHrwP5AD/ZNOF2zjNn+IlFRYVDxlv46UEs6fRlxZpkK3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\n", 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uKwnjiEzBSj8JLhcYlT3JWEzZKIspo4n2T+9Nz4SMLHSr6ci6sQHKzqAmG7un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\n250r69xGkcxeBJe+ap3UVza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LA8Df359jx45x4cKFkjWjytKhQwcaNSq9tk1cXFzJ1NvQ0FDi4uIAiI+PZ8CA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\nrkJggg==\n", 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x5MiRC75/WFgYOTk5F3y9J5Aycsxdy0hnZmA8Go+6+U70lvVQWoLl8YXg7Y3x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\np6N6xaK8fWzva9sOEjIDWwj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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -404,92 +432,92 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 20.1\n", - "Iteration 2: Average Return = 23.47\n", - "Iteration 3: Average Return = 23.32\n", - "Iteration 4: Average Return = 26.65\n", - "Iteration 5: Average Return = 27.08\n", - "Iteration 6: Average Return = 31.7\n", - "Iteration 7: Average Return = 31.25\n", - "Iteration 8: Average Return = 30.92\n", - "Iteration 9: Average Return = 33.59\n", - "Iteration 10: Average Return = 36.52\n", - "Iteration 11: Average Return = 40.5\n", - "Iteration 12: Average Return = 39.73\n", - "Iteration 13: Average Return = 39.07\n", - "Iteration 14: Average Return = 39.96\n", - "Iteration 15: Average Return = 41.08\n", - "Iteration 16: Average Return = 41.64\n", - "Iteration 17: Average Return = 40.74\n", - "Iteration 18: Average Return = 40.6\n", - "Iteration 19: Average Return = 45.97\n", - "Iteration 20: Average Return = 46.91\n", - "Iteration 21: Average Return = 43.62\n", - "Iteration 22: Average Return = 47.65\n", - "Iteration 23: Average Return = 48.4\n", - "Iteration 24: Average Return = 46.54\n", - "Iteration 25: Average Return = 52.47\n", - "Iteration 26: Average Return = 49.33\n", - "Iteration 27: Average Return = 49.14\n", - "Iteration 28: Average Return = 52.58\n", - "Iteration 29: Average Return = 48.55\n", - "Iteration 30: Average Return = 51.14\n", - "Iteration 31: Average Return = 52.14\n", - "Iteration 32: Average Return = 56.55\n", - "Iteration 33: Average Return = 53.23\n", - "Iteration 34: Average Return = 54.4\n", - "Iteration 35: Average Return = 54.32\n", - "Iteration 36: Average Return = 52.81\n", - "Iteration 37: Average Return = 55.96\n", - "Iteration 38: Average Return = 56.55\n", - "Iteration 39: Average Return = 54.79\n", - "Iteration 40: Average Return = 54.97\n", - "Iteration 41: Average Return = 54.94\n", - "Iteration 42: Average Return = 55.18\n", - "Iteration 43: Average Return = 64.06\n", - "Iteration 44: Average Return = 60.15\n", - "Iteration 45: Average Return = 60.81\n", - "Iteration 46: Average Return = 66.59\n", - "Iteration 47: Average Return = 64.09\n", - "Iteration 48: Average Return = 60.87\n", - "Iteration 49: Average Return = 64.64\n", - "Iteration 50: Average Return = 65.73\n", - "Iteration 51: Average Return = 67.45\n", - "Iteration 52: Average Return = 64.71\n", - "Iteration 53: Average Return = 72.9\n", - "Iteration 54: Average Return = 75.25\n", - "Iteration 55: Average Return = 80.11\n", - "Iteration 56: Average Return = 72.7\n", - "Iteration 57: Average Return = 80.52\n", - "Iteration 58: Average Return = 83.91\n", - "Iteration 59: Average Return = 84.67\n", - "Iteration 60: Average Return = 87.1\n", - "Iteration 61: Average Return = 96.67\n", - "Iteration 62: Average Return = 95.72\n", - "Iteration 63: Average Return = 105.09\n", - "Iteration 64: Average Return = 112.31\n", - "Iteration 65: Average Return = 135.78\n", - "Iteration 66: Average Return = 137.28\n", - "Iteration 67: Average Return = 146.69\n", - "Iteration 68: Average Return = 149.38\n", - "Iteration 69: Average Return = 156.18\n", - "Iteration 70: Average Return = 166.69\n", - "Iteration 71: Average Return = 160.88\n", - "Iteration 72: Average Return = 164.94\n", - "Iteration 73: Average Return = 162.63\n", - "Iteration 74: Average Return = 158.43\n", - "Iteration 75: Average Return = 158.95\n", - "Iteration 76: Average Return = 169.29\n", - "Iteration 77: Average Return = 174.37\n", - "Iteration 78: Average Return = 168.45\n", - "Iteration 79: Average Return = 175.18\n", - "Iteration 80: Average Return = 179.8\n", - "Iteration 81: Average Return = 185.64\n", - "Iteration 82: Average Return = 186.79\n", - "Iteration 83: Average Return = 189.48\n", - "Iteration 84: Average Return = 194.17\n", - "Iteration 85: Average Return = 190.49\n", - "Iteration 86: Average Return = 195.75\n", + "Iteration 1: Average Return = 30.5\n", + "Iteration 2: Average Return = 30.97\n", + "Iteration 3: Average Return = 34.39\n", + "Iteration 4: Average Return = 34.49\n", + "Iteration 5: Average Return = 39.38\n", + "Iteration 6: Average Return = 39.56\n", + "Iteration 7: Average Return = 38.52\n", + "Iteration 8: Average Return = 47.56\n", + "Iteration 9: Average Return = 45.63\n", + "Iteration 10: Average Return = 47.82\n", + "Iteration 11: Average Return = 43.86\n", + "Iteration 12: Average Return = 50.8\n", + "Iteration 13: Average Return = 49.57\n", + "Iteration 14: Average Return = 52.1\n", + "Iteration 15: Average Return = 53.25\n", + "Iteration 16: Average Return = 56.55\n", + "Iteration 17: Average Return = 57.8\n", + "Iteration 18: Average Return = 55.13\n", + "Iteration 19: Average Return = 54.48\n", + "Iteration 20: Average Return = 55.56\n", + "Iteration 21: Average Return = 60.33\n", + "Iteration 22: Average Return = 61.28\n", + "Iteration 23: Average Return = 65.61\n", + "Iteration 24: Average Return = 66.02\n", + "Iteration 25: Average Return = 71.55\n", + "Iteration 26: Average Return = 66.51\n", + "Iteration 27: Average Return = 70.45\n", + "Iteration 28: Average Return = 66.59\n", + "Iteration 29: Average Return = 76.95\n", + "Iteration 30: Average Return = 74.85\n", + "Iteration 31: Average Return = 76.25\n", + "Iteration 32: Average Return = 82.77\n", + "Iteration 33: Average Return = 82.44\n", + "Iteration 34: Average Return = 90.91\n", + "Iteration 35: Average Return = 93.65\n", + "Iteration 36: Average Return = 100.23\n", + "Iteration 37: Average Return = 104.6\n", + "Iteration 38: Average Return = 111.54\n", + "Iteration 39: Average Return = 109.77\n", + "Iteration 40: Average Return = 128.62\n", + "Iteration 41: Average Return = 129.4\n", + "Iteration 42: Average Return = 150.79\n", + "Iteration 43: Average Return = 160.3\n", + "Iteration 44: Average Return = 153.7\n", + "Iteration 45: Average Return = 158.81\n", + "Iteration 46: Average Return = 158.52\n", + "Iteration 47: Average Return = 160.79\n", + "Iteration 48: Average Return = 159.55\n", + "Iteration 49: Average Return = 168.21\n", + "Iteration 50: Average Return = 171.06\n", + "Iteration 51: Average Return = 167.63\n", + "Iteration 52: Average Return = 168.45\n", + "Iteration 53: Average Return = 175.41\n", + "Iteration 54: Average Return = 173.7\n", + "Iteration 55: Average Return = 174.66\n", + "Iteration 56: Average Return = 186.83\n", + "Iteration 57: Average Return = 182.37\n", + "Iteration 58: Average Return = 188.43\n", + "Iteration 59: Average Return = 188.83\n", + "Iteration 60: Average Return = 188.65\n", + "Iteration 61: Average Return = 190.42\n", + "Iteration 62: Average Return = 188.67\n", + "Iteration 63: Average Return = 185.52\n", + "Iteration 64: Average Return = 190.54\n", + "Iteration 65: Average Return = 184.61\n", + "Iteration 66: Average Return = 186.75\n", + "Iteration 67: Average Return = 191.3\n", + "Iteration 68: Average Return = 186.89\n", + "Iteration 69: Average Return = 189.24\n", + "Iteration 70: Average Return = 186.92\n", + "Iteration 71: Average Return = 189.76\n", + "Iteration 72: Average Return = 191.05\n", + "Iteration 73: Average Return = 191.01\n", + "Iteration 74: Average Return = 190.58\n", + "Iteration 75: Average Return = 186.59\n", + "Iteration 76: Average Return = 188.17\n", + "Iteration 77: Average Return = 194.91\n", + "Iteration 78: Average Return = 192.14\n", + "Iteration 79: Average Return = 192.46\n", + "Iteration 80: Average Return = 194.36\n", + "Iteration 81: Average Return = 194.98\n", + "Iteration 82: Average Return = 190.75\n", + "Iteration 83: Average Return = 193.96\n", + "Iteration 84: Average Return = 194.34\n", + "Iteration 85: Average Return = 194.81\n", + "Iteration 86: Average Return = 195.32\n", "Solve at 86 iterations, which equals 8600 episodes.\n" ] } @@ -517,9 +545,9 @@ "outputs": [ { "data": { - "image/png": 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wLoIaqMMMkZpkdeA4GhEw8DI1ZlW7OTk5XUwIwEvdaCp26+CuEqw3ax/B4XQC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\ntB+uzsRdcapPfnfu706CLTnosdzxY+DsGVrOalUanM1uFIe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ULaNOr+QFtHJJhnVbe7xI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fUz3bkN5YYySiPSOIMkYWOSOwblTt8alenLEULxgIIRArViNPvoqUEtmkyp6T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\n++Zg3nrrLQA2btxIdHQ0n332Ga+99ppP5xqNRux2Oy0tLWpAqq2t9Tm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XWb9+Pd7e3gCMHz+ebt26Vch7CiF+p7euh4O7UbdMRH/xEXrD56h7fn8cXsd9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\n/VhCuQAAAABJRU5ErkJggg==\n", 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3x6D8ZTcCIari9HkWMTExzJ8/H29vb+bPn090dDQXX3xxjW6ek5NDUJC9nzgo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\nPrTvXPnxXzGfeQi9+m3MF+e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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -619,7 +647,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": { "scrolled": true }, @@ -628,206 +656,206 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 15.58\n", - "Iteration 2: Average Return = 15.27\n", - "Iteration 3: Average Return = 14.87\n", - "Iteration 4: Average Return = 15.79\n", - "Iteration 5: Average Return = 15.16\n", - "Iteration 6: Average Return = 17.15\n", - "Iteration 7: Average Return = 16.77\n", - "Iteration 8: Average Return = 19.63\n", - "Iteration 9: Average Return = 18.92\n", - "Iteration 10: Average Return = 18.62\n", - "Iteration 11: Average Return = 20.91\n", - "Iteration 12: Average Return = 20.77\n", - "Iteration 13: Average Return = 21.79\n", - "Iteration 14: Average Return = 21.58\n", - "Iteration 15: Average Return = 25.72\n", - "Iteration 16: Average Return = 25.73\n", - "Iteration 17: Average Return = 28.32\n", - "Iteration 18: Average Return = 25.81\n", - "Iteration 19: Average Return = 29.66\n", - "Iteration 20: Average Return = 31.96\n", - "Iteration 21: Average Return = 31.97\n", - "Iteration 22: Average Return = 37.81\n", - "Iteration 23: Average Return = 34.28\n", - "Iteration 24: Average Return = 32.66\n", - "Iteration 25: Average Return = 40.06\n", - "Iteration 26: Average Return = 41.08\n", - "Iteration 27: Average Return = 44.96\n", - "Iteration 28: Average Return = 42.21\n", - "Iteration 29: Average Return = 40.91\n", - "Iteration 30: Average Return = 44.04\n", - "Iteration 31: Average Return = 46.89\n", - "Iteration 32: Average Return = 46.89\n", - "Iteration 33: Average Return = 48.9\n", - "Iteration 34: Average Return = 50.14\n", - "Iteration 35: Average Return = 46.95\n", - "Iteration 36: Average Return = 54.99\n", - "Iteration 37: Average Return = 52.45\n", - "Iteration 38: Average Return = 54.82\n", - "Iteration 39: Average Return = 59.08\n", - "Iteration 40: Average Return = 58.38\n", - "Iteration 41: Average Return = 54.59\n", - "Iteration 42: Average Return = 61.3\n", - "Iteration 43: Average Return = 62.26\n", - "Iteration 44: Average Return = 61.51\n", - "Iteration 45: Average Return = 59.4\n", - "Iteration 46: Average Return = 62.32\n", - "Iteration 47: Average Return = 61.84\n", - "Iteration 48: Average Return = 62.1\n", - "Iteration 49: Average Return = 64.87\n", - "Iteration 50: Average Return = 58.23\n", - "Iteration 51: Average Return = 71.57\n", - "Iteration 52: Average Return = 65.5\n", - "Iteration 53: Average Return = 69.07\n", - "Iteration 54: Average Return = 66.68\n", - "Iteration 55: Average Return = 73.35\n", - "Iteration 56: Average Return = 69.2\n", - "Iteration 57: Average Return = 75.52\n", - "Iteration 58: Average Return = 78.99\n", - "Iteration 59: Average Return = 75.31\n", - "Iteration 60: Average Return = 87.01\n", - "Iteration 61: Average Return = 85.15\n", - "Iteration 62: Average Return = 88.56\n", - "Iteration 63: Average Return = 90.55\n", - "Iteration 64: Average Return = 92.69\n", - "Iteration 65: Average Return = 90.0\n", - "Iteration 66: Average Return = 95.58\n", - "Iteration 67: Average Return = 106.97\n", - "Iteration 68: Average Return = 108.86\n", - "Iteration 69: Average Return = 112.72\n", - "Iteration 70: Average Return = 116.06\n", - "Iteration 71: Average Return = 122.27\n", - "Iteration 72: Average Return = 127.2\n", - "Iteration 73: Average Return = 117.92\n", - "Iteration 74: Average Return = 120.04\n", - "Iteration 75: Average Return = 121.56\n", - "Iteration 76: Average Return = 117.68\n", - "Iteration 77: Average Return = 129.27\n", - "Iteration 78: Average Return = 125.99\n", - "Iteration 79: Average Return = 119.74\n", - "Iteration 80: Average Return = 122.39\n", - "Iteration 81: Average Return = 128.95\n", - "Iteration 82: Average Return = 134.99\n", - "Iteration 83: Average Return = 133.44\n", - "Iteration 84: Average Return = 136.84\n", - "Iteration 85: Average Return = 139.35\n", - "Iteration 86: Average Return = 145.28\n", - "Iteration 87: Average Return = 141.45\n", - "Iteration 88: Average Return = 141.47\n", - "Iteration 89: Average Return = 134.68\n", - "Iteration 90: Average Return = 136.97\n", - "Iteration 91: Average Return = 134.16\n", - "Iteration 92: Average Return = 144.93\n", - "Iteration 93: Average Return = 139.9\n", - "Iteration 94: Average Return = 137.18\n", - "Iteration 95: Average Return = 138.02\n", - "Iteration 96: Average Return = 138.85\n", - "Iteration 97: Average Return = 135.37\n", - "Iteration 98: Average Return = 131.78\n", - "Iteration 99: Average Return = 136.19\n", - "Iteration 100: Average Return = 138.71\n", - "Iteration 101: Average Return = 130.51\n", - "Iteration 102: Average Return = 128.65\n", - "Iteration 103: Average Return = 135.1\n", - "Iteration 104: Average Return = 138.46\n", - "Iteration 105: Average Return = 145.99\n", - "Iteration 106: Average Return = 146.95\n", - "Iteration 107: Average Return = 146.54\n", - "Iteration 108: Average Return = 149.18\n", - "Iteration 109: Average Return = 148.54\n", - "Iteration 110: Average Return = 149.14\n", - "Iteration 111: Average Return = 143.38\n", - "Iteration 112: Average Return = 151.95\n", - "Iteration 113: Average Return = 152.59\n", - "Iteration 114: Average Return = 154.05\n", - "Iteration 115: Average Return = 158.63\n", - "Iteration 116: Average Return = 156.96\n", - "Iteration 117: Average Return = 153.63\n", - "Iteration 118: Average Return = 155.61\n", - "Iteration 119: Average Return = 151.14\n", - "Iteration 120: Average Return = 155.31\n", - "Iteration 121: Average Return = 151.17\n", - "Iteration 122: Average Return = 151.2\n", - "Iteration 123: Average Return = 147.83\n", - "Iteration 124: Average Return = 145.58\n", - "Iteration 125: Average Return = 137.98\n", - "Iteration 126: Average Return = 140.12\n", - "Iteration 127: Average Return = 143.54\n", - "Iteration 128: Average Return = 138.92\n", - "Iteration 129: Average Return = 144.35\n", - "Iteration 130: Average Return = 140.67\n", - "Iteration 131: Average Return = 139.8\n", - "Iteration 132: Average Return = 147.92\n", - "Iteration 133: Average Return = 142.12\n", - "Iteration 134: Average Return = 147.82\n", - "Iteration 135: Average Return = 151.31\n", - "Iteration 136: Average Return = 158.1\n", - "Iteration 137: Average Return = 154.29\n", - "Iteration 138: Average Return = 156.21\n", - "Iteration 139: Average Return = 159.23\n", - "Iteration 140: Average Return = 154.62\n", - "Iteration 141: Average Return = 156.02\n", - "Iteration 142: Average Return = 159.6\n", - "Iteration 143: Average Return = 159.07\n", - "Iteration 144: Average Return = 160.45\n", - "Iteration 145: Average Return = 162.08\n", - "Iteration 146: Average Return = 170.15\n", - "Iteration 147: Average Return = 163.68\n", - "Iteration 148: Average Return = 160.86\n", - "Iteration 149: Average Return = 165.91\n", - "Iteration 150: Average Return = 160.33\n", - "Iteration 151: Average Return = 156.86\n", - "Iteration 152: Average Return = 159.02\n", - "Iteration 153: Average Return = 156.21\n", - "Iteration 154: Average Return = 160.18\n", - "Iteration 155: Average Return = 151.82\n", - "Iteration 156: Average Return = 158.0\n", - "Iteration 157: Average Return = 155.82\n", - "Iteration 158: Average Return = 158.78\n", - "Iteration 159: Average Return = 158.07\n", - "Iteration 160: Average Return = 153.29\n", - "Iteration 161: Average Return = 149.6\n", - "Iteration 162: Average Return = 151.32\n", - "Iteration 163: Average Return = 156.6\n", - "Iteration 164: Average Return = 161.83\n", - "Iteration 165: Average Return = 158.11\n", - "Iteration 166: Average Return = 161.3\n", - "Iteration 167: Average Return = 155.03\n", - "Iteration 168: Average Return = 162.12\n", - "Iteration 169: Average Return = 159.91\n", - "Iteration 170: Average Return = 161.16\n", - "Iteration 171: Average Return = 164.55\n", - "Iteration 172: Average Return = 168.07\n", - "Iteration 173: Average Return = 166.54\n", - "Iteration 174: Average Return = 160.41\n", - "Iteration 175: Average Return = 160.93\n", - "Iteration 176: Average Return = 157.23\n", - "Iteration 177: Average Return = 159.94\n", - "Iteration 178: Average Return = 155.53\n", - "Iteration 179: Average Return = 157.9\n", - "Iteration 180: Average Return = 163.03\n", - "Iteration 181: Average Return = 167.13\n", - "Iteration 182: Average Return = 168.55\n", - "Iteration 183: Average Return = 157.28\n", - "Iteration 184: Average Return = 159.7\n", - "Iteration 185: Average Return = 164.65\n", - "Iteration 186: Average Return = 153.78\n", - "Iteration 187: Average Return = 161.19\n", - "Iteration 188: Average Return = 148.61\n", - "Iteration 189: Average Return = 157.44\n", - "Iteration 190: Average Return = 161.3\n", - "Iteration 191: Average Return = 158.44\n", - "Iteration 192: Average Return = 160.78\n", - "Iteration 193: Average Return = 170.6\n", - "Iteration 194: Average Return = 162.48\n", - "Iteration 195: Average Return = 172.45\n", - "Iteration 196: Average Return = 165.99\n", - "Iteration 197: Average Return = 161.56\n", - "Iteration 198: Average Return = 170.44\n", - "Iteration 199: Average Return = 173.01\n", - "Iteration 200: Average Return = 162.66\n" + "Iteration 1: Average Return = 18.37\n", + "Iteration 2: Average Return = 18.64\n", + "Iteration 3: Average Return = 18.61\n", + "Iteration 4: Average Return = 20.39\n", + "Iteration 5: Average Return = 21.79\n", + "Iteration 6: Average Return = 23.28\n", + "Iteration 7: Average Return = 26.5\n", + "Iteration 8: Average Return = 26.41\n", + "Iteration 9: Average Return = 38.14\n", + "Iteration 10: Average Return = 39.46\n", + "Iteration 11: Average Return = 43.6\n", + "Iteration 12: Average Return = 56.31\n", + "Iteration 13: Average Return = 60.37\n", + "Iteration 14: Average Return = 74.8\n", + "Iteration 15: Average Return = 77.94\n", + "Iteration 16: Average Return = 88.85\n", + "Iteration 17: Average Return = 85.66\n", + "Iteration 18: Average Return = 89.34\n", + "Iteration 19: Average Return = 97.0\n", + "Iteration 20: Average Return = 94.19\n", + "Iteration 21: Average Return = 86.07\n", + "Iteration 22: Average Return = 97.74\n", + "Iteration 23: Average Return = 91.69\n", + "Iteration 24: Average Return = 90.21\n", + "Iteration 25: Average Return = 92.39\n", + "Iteration 26: Average Return = 89.37\n", + "Iteration 27: Average Return = 87.73\n", + "Iteration 28: Average Return = 81.14\n", + "Iteration 29: Average Return = 95.13\n", + "Iteration 30: Average Return = 88.92\n", + "Iteration 31: Average Return = 90.05\n", + "Iteration 32: Average Return = 94.35\n", + "Iteration 33: Average Return = 95.83\n", + "Iteration 34: Average Return = 94.58\n", + "Iteration 35: Average Return = 97.66\n", + "Iteration 36: Average Return = 107.71\n", + "Iteration 37: Average Return = 94.41\n", + "Iteration 38: Average Return = 85.35\n", + "Iteration 39: Average Return = 95.1\n", + "Iteration 40: Average Return = 96.84\n", + "Iteration 41: Average Return = 104.97\n", + "Iteration 42: Average Return = 97.64\n", + "Iteration 43: Average Return = 102.49\n", + "Iteration 44: Average Return = 113.19\n", + "Iteration 45: Average Return = 110.21\n", + "Iteration 46: Average Return = 110.11\n", + "Iteration 47: Average Return = 114.36\n", + "Iteration 48: Average Return = 130.16\n", + "Iteration 49: Average Return = 132.27\n", + "Iteration 50: Average Return = 135.63\n", + "Iteration 51: Average Return = 133.3\n", + "Iteration 52: Average Return = 138.79\n", + "Iteration 53: Average Return = 141.04\n", + "Iteration 54: Average Return = 144.74\n", + "Iteration 55: Average Return = 146.45\n", + "Iteration 56: Average Return = 140.39\n", + "Iteration 57: Average Return = 147.39\n", + "Iteration 58: Average Return = 153.11\n", + "Iteration 59: Average Return = 141.86\n", + "Iteration 60: Average Return = 148.21\n", + "Iteration 61: Average Return = 141.57\n", + "Iteration 62: Average Return = 142.29\n", + "Iteration 63: Average Return = 148.51\n", + "Iteration 64: Average Return = 140.74\n", + "Iteration 65: Average Return = 149.14\n", + "Iteration 66: Average Return = 152.47\n", + "Iteration 67: Average Return = 149.24\n", + "Iteration 68: Average Return = 138.4\n", + "Iteration 69: Average Return = 140.74\n", + "Iteration 70: Average Return = 141.23\n", + "Iteration 71: Average Return = 133.87\n", + "Iteration 72: Average Return = 141.9\n", + "Iteration 73: Average Return = 136.04\n", + "Iteration 74: Average Return = 145.07\n", + "Iteration 75: Average Return = 137.5\n", + "Iteration 76: Average Return = 137.77\n", + "Iteration 77: Average Return = 146.0\n", + "Iteration 78: Average Return = 145.29\n", + "Iteration 79: Average Return = 145.52\n", + "Iteration 80: Average Return = 145.19\n", + "Iteration 81: Average Return = 140.74\n", + "Iteration 82: Average Return = 141.58\n", + "Iteration 83: Average Return = 155.41\n", + "Iteration 84: Average Return = 149.82\n", + "Iteration 85: Average Return = 149.35\n", + "Iteration 86: Average Return = 151.23\n", + "Iteration 87: Average Return = 151.5\n", + "Iteration 88: Average Return = 154.62\n", + "Iteration 89: Average Return = 152.32\n", + "Iteration 90: Average Return = 163.26\n", + "Iteration 91: Average Return = 161.27\n", + "Iteration 92: Average Return = 153.37\n", + "Iteration 93: Average Return = 146.51\n", + "Iteration 94: Average Return = 155.09\n", + "Iteration 95: Average Return = 153.72\n", + "Iteration 96: Average Return = 150.84\n", + "Iteration 97: Average Return = 151.8\n", + "Iteration 98: Average Return = 157.12\n", + "Iteration 99: Average Return = 151.62\n", + "Iteration 100: Average Return = 152.22\n", + "Iteration 101: Average Return = 149.63\n", + "Iteration 102: Average Return = 147.91\n", + "Iteration 103: Average Return = 152.14\n", + "Iteration 104: Average Return = 142.41\n", + "Iteration 105: Average Return = 146.01\n", + "Iteration 106: Average Return = 146.08\n", + "Iteration 107: Average Return = 140.91\n", + "Iteration 108: Average Return = 152.31\n", + "Iteration 109: Average Return = 150.13\n", + "Iteration 110: Average Return = 153.66\n", + "Iteration 111: Average Return = 159.03\n", + "Iteration 112: Average Return = 161.19\n", + "Iteration 113: Average Return = 159.77\n", + "Iteration 114: Average Return = 155.77\n", + "Iteration 115: Average Return = 163.07\n", + "Iteration 116: Average Return = 159.13\n", + "Iteration 117: Average Return = 160.18\n", + "Iteration 118: Average Return = 157.42\n", + "Iteration 119: Average Return = 157.94\n", + "Iteration 120: Average Return = 160.62\n", + "Iteration 121: Average Return = 165.97\n", + "Iteration 122: Average Return = 169.01\n", + "Iteration 123: Average Return = 162.06\n", + "Iteration 124: Average Return = 158.46\n", + "Iteration 125: Average Return = 157.93\n", + "Iteration 126: Average Return = 154.82\n", + "Iteration 127: Average Return = 158.02\n", + "Iteration 128: Average Return = 158.43\n", + "Iteration 129: Average Return = 158.97\n", + "Iteration 130: Average Return = 155.2\n", + "Iteration 131: Average Return = 156.32\n", + "Iteration 132: Average Return = 157.74\n", + "Iteration 133: Average Return = 152.66\n", + "Iteration 134: Average Return = 151.86\n", + "Iteration 135: Average Return = 159.88\n", + "Iteration 136: Average Return = 160.81\n", + "Iteration 137: Average Return = 160.78\n", + "Iteration 138: Average Return = 162.45\n", + "Iteration 139: Average Return = 168.18\n", + "Iteration 140: Average Return = 154.28\n", + "Iteration 141: Average Return = 154.41\n", + "Iteration 142: Average Return = 158.33\n", + "Iteration 143: Average Return = 157.41\n", + "Iteration 144: Average Return = 155.62\n", + "Iteration 145: Average Return = 160.26\n", + "Iteration 146: Average Return = 161.08\n", + "Iteration 147: Average Return = 159.65\n", + "Iteration 148: Average Return = 159.3\n", + "Iteration 149: Average Return = 163.87\n", + "Iteration 150: Average Return = 157.24\n", + "Iteration 151: Average Return = 156.31\n", + "Iteration 152: Average Return = 154.99\n", + "Iteration 153: Average Return = 157.0\n", + "Iteration 154: Average Return = 157.87\n", + "Iteration 155: Average Return = 161.16\n", + "Iteration 156: Average Return = 162.85\n", + "Iteration 157: Average Return = 159.98\n", + "Iteration 158: Average Return = 165.98\n", + "Iteration 159: Average Return = 160.04\n", + "Iteration 160: Average Return = 164.62\n", + "Iteration 161: Average Return = 161.65\n", + "Iteration 162: Average Return = 163.17\n", + "Iteration 163: Average Return = 167.49\n", + "Iteration 164: Average Return = 167.75\n", + "Iteration 165: Average Return = 172.5\n", + "Iteration 166: Average Return = 163.51\n", + "Iteration 167: Average Return = 166.19\n", + "Iteration 168: Average Return = 164.78\n", + "Iteration 169: Average Return = 172.07\n", + "Iteration 170: Average Return = 165.18\n", + "Iteration 171: Average Return = 163.92\n", + "Iteration 172: Average Return = 163.87\n", + "Iteration 173: Average Return = 159.09\n", + "Iteration 174: Average Return = 159.76\n", + "Iteration 175: Average Return = 158.48\n", + "Iteration 176: Average Return = 158.18\n", + "Iteration 177: Average Return = 156.48\n", + "Iteration 178: Average Return = 162.21\n", + "Iteration 179: Average Return = 161.7\n", + "Iteration 180: Average Return = 162.1\n", + "Iteration 181: Average Return = 172.95\n", + "Iteration 182: Average Return = 172.49\n", + "Iteration 183: Average Return = 168.28\n", + "Iteration 184: Average Return = 178.22\n", + "Iteration 185: Average Return = 174.47\n", + "Iteration 186: Average Return = 175.37\n", + "Iteration 187: Average Return = 172.74\n", + "Iteration 188: Average Return = 170.63\n", + "Iteration 189: Average Return = 171.42\n", + "Iteration 190: Average Return = 170.09\n", + "Iteration 191: Average Return = 162.79\n", + "Iteration 192: Average Return = 171.33\n", + "Iteration 193: Average Return = 163.03\n", + "Iteration 194: Average Return = 167.17\n", + "Iteration 195: Average Return = 165.31\n", + "Iteration 196: Average Return = 164.29\n", + "Iteration 197: Average Return = 158.58\n", + "Iteration 198: Average Return = 161.52\n", + "Iteration 199: Average Return = 161.87\n", + "Iteration 200: Average Return = 154.18\n" ] } ], @@ -850,16 +878,16 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "scrolled": true }, "outputs": [ { "data": { - "image/png": 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xGkEjmufCY1HxONVzkeLy4vMihKaWwgJQPZf+jJ5LvEutjqrVtrZIjJaRrxvo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\nzc1gjOH3339HdHS0qJ1Le3s7Dh8+jE2bNmHSpEnC8mvXrsH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Y7+9D+9oJ3HUL+sSRwbdvb4X2VvQ7uwGwNv0A/cbOuE5ttbWYiemxHjwdz7GrE+rrUGXT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\nvD2CIAgJQMRlvBlBWExrDT09kCHiIgjC5ETEZby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r4p6gQpxnnOqz6NGjB3l5ea7OIsTZHdwDNdXm+tGVFafdbdhzIKCjOU/TkTR0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\neicjkUgwMDCAz+fj8uXLfPnyhYaGhl/5+SJp06O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UyjB0kNWsPSVEAPLb1Nn169cTGxtLz5496x13OBz1WhpxcXE4HA5JFgFIf/J3\nqDZQP63byUylDIXU81GDRzZ4jeoY5t4XAsxFbpaHX3AvxHOLq/kb2LgWevZDndPHrErrtENNyRCd\n+TWEhEByChw+eNotP1WHMCx/nAuRMfW2EA0UKqkHlpf+YU4IECIA+SVZlJeX89FHH/GnP/3plMca\nGtBsrLBbRkYGGRkZAMyZM6dekmkOq9Xa4mu9LVBj07k56LVfEnblNXQacHIZbRv88bnmPVkDv5+O\njSXPGgxVlURPu4vqY0dwrfqC8AO7cY9iHD5AUPdedOg7gJLN6wgvtOMCYvqnYG3oNfPj6xio/44Q\nuLFJXM3j7bj8kiyOHTtGXl4eDzzwAAD5+fk8+OCDPPPMM8TFxWG3102ZzM/Pb7RVkZaWRlpamvvn\nk69rDpvN1uJrvS1QY+v47UowDMomXEG5t+LrnARRsbiSeqJPmFNjXauXm49FdIJiF9Wx8ZRGx4Fh\n4Prvp2AJwhkUigqw1yxQ/x0hcGOTuJqnpXElJXlW48wvyaJHjx71Bq7vuOMOnnnmGSIjI0lNTeWL\nL75g3Lhx7N69m7CwMOmCCkCVO7dAQtIpfe9tyXLvE+ZUW6gbCN/xA3ToiDp3BPq7laj4LqhuPc0V\n29nbCB44BMMqhQmEaGs++b9q/vz5bNu2DZfLxcyZM5k6dSoXXXRRg+cOGzaMjRs3MmvWLEJCQkhP\nT/dFiKIZtNZU7MhCpQz36n1OXjinwsLNmUJOO3TrCr2S4buVkJBo9vff/xRERBFz7hDynU6vxiXE\n2cgnyeLuu+8+7eMLFy50f6+UYvr06d4OSbSA3rcLY9nfsFz9K3PWUSuK8bVIUndw2lFduqF+Mhht\nsaBq1nWonwwyvwYFzs5yQrQn0l4XHtP//Ri2bcKoqdnk62ShknqYq7A7d0V17YFl/t/rr9QWQniN\nlPsQHtGlxehN35kb/xw9ZL5JJ3X3bRBJ5pTb2lXfkiiE8B1JFsIjesNaqKpETbsbgoIITk6pX57D\nB1TKcBg4DJLPbfpkIUSb8qgbqri4mE8++YQDBw5QVlZW77HHH3/cK4GJwKLXfglduptVUoNDiOjd\nl0Ifx6Bi4gi6R/7ehPAHj5LFSy+9RFVVFWPGjCEkpHm7g4kzn/4hE7K3o677rblActhogm02CMC5\n5kII7/AoWezatYs33niD4OBgb8cjAoyurMRY8gYkdkNNuMzf4Qgh/MSjMYsePXqQn5/v7VhEANL/\nWwl5uVhGNzU7AAAgAElEQVSu/Q3KKh8WhDhbedSyOPfcc3n66aeZOHEi0dH19wFobHGdaCeOHzP3\nohg4zN+RCCH8yKNksWPHDuLi4sjKyjrlMUkW7VxxEUR0Mjc2EkKctZpMFlprZs6cic1mI0hWx551\ndHGRe8tPIcTZq8mPi0op7r///kbLhIt2rrgIAnD/ByGEb3nUt9CzZ09yc3O9HYsIRNKyEELg4ZhF\nSkoKTz/9NBMmTDhlcw0Zs2jniotQEQP8HYUQws88ShY7d+4kISGB7du3n/KYJIv2S2stLQshBOBh\nsnj00Ue9HYcIRCdKwDAkWQghPEsWhmE0+phFplS2X64i86skCyHOeh4li+uvv77Rx5YsWdLk9YsW\nLWLjxo1ERUUxd+5cAN599102bNiA1Wqlc+fOpKenEx4eDsDSpUtZsWIFFouFadOmMXToUE/CFG2t\n2EwWSpKFEGc9j5LFK6+8Uu9np9PJsmXLSE1N9egmEydO5LLLLqu3I97gwYO54YYbCAoK4r333mPp\n0qXcdNNNHDp0iLVr1/Liiy/idDp54okneOmll6QF4w/F0rIQQpg8egeOj4+v919ycjJ33nknH3/8\nsUc3GThwIBEREfWODRkyxL3ILzk5GYfDAUBmZiZjx44lODiYhIQEEhMTyc7Obs7vJNqIdieLTv4N\nRAjhdy3eVrW0tJSioqI2CWLFihWMHTsWAIfDQb9+/dyPxcbGuhPJj2VkZJCRkQHAnDlzTpnW6ymr\n1dria73Nn7GVGNUUA3E9e2HpGB4wcZ2OxNV8gRqbxNU83o7Lo2Tx8ssv11vBXV5ezvbt27ngggta\nHcBHH31EUFCQ+7m01h5fm5aWRlpamvtnewv3V7DZbC2+1tv8GZtxLBesVvKLS1ElJwImrtORuJov\nUGOTuJqnpXElJSV5dJ5HySIxMbHez6GhoVx88cUMHjy42YGdbOXKlWzYsIHZs2e7k1FcXFy9cugO\nh4PY2NhW3Ue0UHERRERJqRchhGfJYujQofW6hmplZ2fTt2/fFt34+++/5+OPP+bxxx8nNDTUfTw1\nNZUFCxYwefJknE4nubm5Lb6HaB0pIiiEqOVRsnjyySd5++23Tzn+1FNPsXjx4iavnz9/Ptu2bcPl\ncjFz5kymTp3K0qVLqaqq4oknngCgX79+zJgxg+7duzNmzBjuvfdeLBYLv/nNb2QmlL9IEUEhRI3T\nJovaxXhaa/d/tY4dO+ZxyfK77777lGOnKxNy9dVXc/XVV3v03MKLil2oHvH+jkIIEQBOmyxOXox3\n3XXX1XvMYrHw85//3DtRicBQs/GREEKcNlm88soraK157LHHePzxx9Fao5RCKUVkZCQhISG+ilP4\ngK6uRtW0FnWBA0pcEJfg56iEEIHgtMkiPt7sgli0aBFgdksVFhYSExPj/ciETxmff4j++j9Y/rwQ\nZQ1GZ60HQKXI3ttCCA9XcJeUlPDSSy9x4403MmvWLADWr1/P+++/79XghO/oA7vh+FH0xm/Nn7ds\ngBgbdO3p38CEEAHBo2Tx+uuvExYWxqJFi7BazcZIcnIya9eu9Wpwwocc5mIevfIzdFUlbPseNWiE\nrLEQQgAeTp3NysritddecycKgMjISAoLC70WmPAxhx1CQmD3NvRH70DZCdQgzwpFCiHaP49aFmFh\nYbhcrnrH7Ha7jF20E7qqEoqcqAsuhY7h6P9+DKEdoX/rVugLIdoPj1oWkyZNYu7cuVx33XVordm1\naxf/+Mc/uPjii70dn/AFZz5oDd17YXnuTbOVEdoR1aGjvyMTQgQIj5LFVVddRXBwMG+++SbV1dX8\n5S9/IS0tjSuuuMLb8YkW0g47WK2oyOimT64Zr1AxNlSHMEjq4eXohBBnmiaThWEYrFy5kksuuYQr\nr7zSFzGJNmC8OgdibATd/lCT52rHcfObWFmtLYRoWJNjFhaLhXfeeYfg4GBfxCPaytHDYD/m2bm1\nySIm8Gr0CyECg0cD3CNGjGD9+vXejkW0EV1aDCdKoKjAswscdoiIRJ1U/VcIIU7m0ZhFZWUlL774\nIsnJycTFxdWbe3/nnXd6LTjRQvk1LQVXAdowUD+q2qtLijHmzcZy852oHr3RTrt0QQkhTsujZNG9\ne3e6d+/u7VhEW8mv6X6qroaS4lPLjB/YDQey0Vs2oHr0Nruh4hNPfR4hhKjhUbK45pprvB2HaEO6\ntmUBUOQ8JVnoo4fNb44cNL867KifDPJRdEKIM5FHyaK1Fi1axMaNG4mKimLu3LkAFBcXM2/ePI4f\nP058fDz33HMPERERaK1ZvHgxmzZtIjQ0lPT0dHr37u2LMNsPe17d94VO6HpO/cdrkoU+chBdWmKO\nb8TK4LYQonE+2YJu4sSJPPzww/WOLVu2jEGDBrFgwQIGDRrEsmXLANi0aRNHjx5lwYIFzJgxgzfe\neMMXIbYr2pEHwWb5eN3AILc+VtOyyD0Ee3YAoKRgoBDiNHySLAYOHEhERES9Y5mZmUyYMAGACRMm\nkJmZCZjVbMePH49SiuTkZEpKSnA6nb4Is/2w50GPmtZYUQOv3dHDYA2GqkqMb5ZDkBX6DfRtjEKI\nM4rfNrc+eV+MmJgYioqKAHA4HNhsdV0icXFxOBwOv8R4xsrPQ3XrabYuCuu3LHR5uTmgXbtPxab/\nQa9kVGgH38cphDhjeDRmobXmyy+/ZM2aNbhcLl544QW2bdtGQUEBY8eObdOATt7nu1ZjZbIzMjLI\nyMgAYM6cOfWSTHNYrdYWX+ttzY3NOFHC8RIX4T16Ubo9jpDyUqJOur5y324cQOT5aRRtXgfaIHzY\nKCKa+fsH6msmcTVfoMYmcTWPt+PyKFksWbKErKwsrrjiCl5//XXA/MT/9ttvtzhZREVF4XQ6iYmJ\nwel0EhkZ6X5eu93uPi8/P7/R6rZpaWmkpaW5fz75uuaw2WwtvtbbmhubPnwAgJIO4eiISMryjlJ5\n0vXGjq0AFMcmmFum5udxokcfypr5+wfqayZxNV+gxiZxNU9L40pKSvLoPI+6oVatWsWDDz7IuHHj\n3J/yExISyMvLa+LKxqWmprJq1Sr3848cOdJ9fPXq1e7qtmFhYe2qFLrWGm0YdT8786n+w2/Rhw+2\nzQ2O5wKg4hIgMubUVdzHDplfOyeZBQOtwdCnf9vcWwjRbnnUsjAMgw4d6vdpl5WVnXKsMfPnz2fb\ntm24XC5mzpzJ1KlTmTJlCvPmzWPFihXYbDbuvfdeAIYNG8bGjRuZNWsWISEhpKenN/NXCmzGK0+i\nOoahpt9nHji4F+zHzAVyXVtf7VVnroGO4dCtFyoqGr1ne/3HD+yFWBsqtAOWy36BHj4GVTNzSggh\nGuNRshg2bBjvvPMOv/71rwHz0/GSJUsYMWKERze5++67Gzw+e/bsU44ppZg+fbpHz3um0VrDri3o\n8jL0z65HJSShC/LNBw9kt/x5846gv12JOj8NvXENavxlqNBQdGQ0FBehq6pQVit68zr4/jvUZb8A\nQCWnoJJT2uJXE0K0cx51Q9188804HA5uueUWSktLufnmmzl+/Dg33nijt+NrX5z5UHYCtEav+Ld5\nrCZZ6P27m7xcZ2/HWPJGvUkAurgIY/5j6E/fx3jyHqiqQo2/1HwwMsbc1Ki40KwH9dYC6N4L9bMb\n2vxXE0K0bx61LMLCwvj9739PQUEBdrsdm81GdLQHm+qI+o7mmF/jE9HfZKB/doOZQACOH0WXFKPC\nIxq9XG9Yg874BHXxFPeKa+ONueDMR11xDfrzD6FPf1TNim0VFYMGcxW3qxCKi7DMeAAl5eaFEM3k\n8ZgFQGRkpHvWkmEYWCx+W6ZxRtJHzGShptyEfv0F2LMd7cwHiwUMwyzwN3BY409QVGh+PZgNsTZz\n06Ktm1BX3Yhl8rXoIedBVGzd+bWVZPPz0LXXJnbzwm8mhGjvPEoW119/fYPHg4KCiImJYdSoUUyd\nOtXjAe+zVu4hCItADRiCpqagX0E+9EuBnVno/dmogcPMBFJShOrWq97l2mXObNIH9qCGjkZv2QiA\nGjbG/Nr7J/XvV1NJVh8/Cq4isFohqv3MLBNC+I5HyWLatGlkZmYyZcoU9zqITz75hOHDh5OUlMQH\nH3zAW2+9xcyZM70d7xlNH82BLt0gIhLCIsyyGwX5qJ+ci3ba0ft3ow0D45Un4XgulucW13+Corpk\nAaCzNpith6SGy8ersHAI7wR5R6G0GGITTtnbQgghPOFRsvj3v//Ns88+S1hYGGAu4ujTpw8PPfQQ\nL7/8Mj169ODBBx/0aqDtwpEc1LDR5lqVxK7onL1QWgLRcaiBQ9Gr/4N+bxEcrEkG61ZDt5MmEdSu\nmTiQja6qhO2bUaMmNLrCHTDHR47nwolSsCV48ZcTQrRnHn3MLC0tpby8vN6x8vJySktLAYiOjqai\noqLto2tHtKsIiovcYwaqc1fYXzNdNjoO9YtfQ5fu6K+XQ69k6HoOetXn7plP2qiGYpfZUigqQK/7\nGspPoAadfvqyik809+LOz0PZOnv1dxRCtF8etSwmTJjAk08+yeWXX47NZiM/P5/PPvvMXTV28+bN\nHi8ZP2vl1gxud6npMkrsCtqcOKBi4lAdwrDc9QjG31/DctUN6D070X9/lcqdW8DWxUw02kClDEev\nW4V+dyFEdIL+g09/3/gusH6Nea84aVkIIVrGo2Rx0003kZiYyNq1a3E6nURHR3PppZe66zKlpKTw\n+OOPezXQM5He8QPGuwuxPPQcemcWKOUuHa4Su+JeLRETZx6LSyDorkfMY/Fd0J++T9ErT6Mfeq5u\nJlTKUNiwBsI7Ybn7MVSHjqcPIiHRnZQkWQghWsqjZGGxWLjkkku45JJLGnw8JETKRTREZ2+HvFz0\ntyvQ67+BvgNQtbOROp80hTU67pRrVccwLNPvo3r+o6i/v4YaPdE8HtcZ9cDTEBePauC6U54nPtGd\nlKQbSgjRUh5vq1pQUEB2djYul6veCuKLLrrIK4G1C06zAqT+4iNwFaKun1H3WEIXUBbo0KHR1oEa\nMISOl13NiS8+qtucKDIa1aUZayXiu9R9L8lCCNFCHiWLdevW8fLLL9OlSxdycnLo3r07OTk59O/f\nX5LFaWhHTblgVyEohRpeV85dBQebs5Osp19NHXLuME589v9gZ5Z5ILKZK+ejYtxbrDb7WiGEqOHx\nfhbp6emMGTOGadOm8dxzz/HVV1+Rk5Pj7fjObE67OQC9bzec0wcVHVvvYTVinLly+zSsPfsBoLd9\nb25/GhberBCUxWK2KLQ+/RRbIYQ4DY+Shd1uZ8yYMfWOTZgwgRkzZnDzzTd7JbB2wWE3K7v+7Abo\nFHXKw5Zf/LrJpwjqnAQdOpqtk+i4Fr3hq/MvrhvkFkKIFvAoWURGRlJQUEB0dDTx8fHs2rWLTp06\nuWtGiVPpslI4UQIx8aja8YYWUBYLdOsF2dta3I1kuWRKi+8vhBDgYbKYNGkSO3bsYPTo0Vx55ZU8\n/vjjKKWYPHmyt+M7c9WOV8S2fk9c1b0XOnsbRJ7aOhFCCF/wKFn87Gc/c1eYnTBhAikpKZSVldGt\nW+srmH766aesWLECpRTdu3cnPT2dgoIC5s+fT3FxMb169eKuu+7CavV44lZgqEkWKqYNNlDvbhYU\nVJ1kgFoI4R9NlvswDINf/epXVFZWuo/ZbLY2SRQOh4PPP/+cOXPmMHfuXAzDYO3atbz33ntceeWV\nLFiwgPDwcFasWNHqe/marpk2S1x8q59L1Szkk9lMQgh/aTJZWCwWkpKScLlcXgnAMAwqKiqorq6m\noqKC6Ohotm7dyujRowGYOHEimZmZXrm3Vzns5ortqNimz21KUg+wdUb17Nv65xJCiBbwqG/n/PPP\n59lnn+Xyyy8nLq7+jJxzzz23xTePjY3lpz/9KbfffjshISEMGTKE3r17ExYWRlBQkPsch8PR4nv4\njfM4RMWg2qD7TAWHEPTM620QlBBCtIxH72TLly8H4IMPPqh3XCnFK6+80uKbFxcXk5mZycKFCwkL\nC+PFF1/k+++/9/j6jIwMMjIyAJgzZw42W8vGB6xWa4uvbYyzuAid0IXYVj6vN2JrCxJX8wRqXBC4\nsUlczePtuDxKFgsXLvTKzbOyskhISHBv1Tpq1Ch27txJaWkp1dXVBAUF4XA4iI1tuCsnLS3NXcwQ\nzPUgLWGz2Vp8bWOqj+VC1x6tfl5vxNYWJK7mCdS4IHBjk7iap6VxeVox3ONt06qqqti+fTtr164F\noKysjLKysmYHdjKbzcbu3bspLy9Ha01WVhbdunUjJSWF7777DoCVK1eSmpraqvv4mi50Qt4RlOx3\nLYRoJzxqWRw8eJBnn32W4OBg8vPzGTt2LNu2bWPVqlXcc889Lb55v379GD16NA8++CBBQUH07NmT\ntLQ0hg8fzvz583n//ffp1avXGVd/Sq/9EgwDNfpCf4cihBBtwqNk8frrr3Pttdcyfvx4pk2bBsDA\ngQN57bXXWh3A1KlTmTp1ar1jnTt35plnnmn1c/uDNgxzt7vkc1GJXf0djhBCtAmPuqEOHTrEBRdc\nUO9Yhw4dZCvVhuzMguNHURc0vPeHEEKciTxKFvHx8ezdu7fesezsbBITE70S1JlM79kBgBo22s+R\nCCFE2/GoG+raa69lzpw5XHzxxVRVVbF06VL++9//ctttt3k7vjOPqxDCwlGhHfwdiRBCtBmPWhYj\nRozgD3/4A0VFRQwcOJDjx49z//33M2TIEG/Hd+ZxFYLUcBJCtDMetSyKioro3bs3vXv39nY8Zzxd\nVACdIv0dhhBCtCmPkkV6ejopKSmcf/75jBw5kg4dpIulUa5C6OzZIhchhDhTeNQNtWjRIoYPH87y\n5cuZMWMG8+fPZ/369VRXV3s7vjOPq1BKiQsh2h2Pd8q79NJLufTSS7Hb7XzzzTe8//77/OUvf+HN\nN9/0doxnDG1UQ3GRbFIkhGh3PC73UaugoICCggJcLhfh4eHeiOnMVewCrRvcb1sIIc5kHrUsDh06\nxDfffMOaNWuoqKhgzJgxPPDAA/TtK/sr1OMqNL9KN5QQop3xKFk88sgjjBo1ihkzZnDuuefW289C\nnKSoAAAl3VBCiHbG49pQZ9we2H6gi4vMb6QbSgjRzniUAaxWKwUFBWRnZ+NyudBaux870yrCelWR\ndEMJIdonj5LFunXrePnll+nSpQs5OTl0796dnJwc+vfvL8niZK4CUBYIj/B3JEII0aY8ShZLliwh\nPT2dMWPGMG3aNJ577jm++uorcnJyvB3fmcVVCJ0iUZZmTzITQoiA5tG7mt1uZ8yYMfWOTZgwgdWr\nV3slqDOVLiqU8QohRLvk8aK8goICoqOjiY+PZ9euXXTq1AnDMFodQElJCa+++io5OTkopbj99ttJ\nSkpi3rx5HD9+nPj4eO655x4iIs6Arh1XAUTKeIUQov3xKFlMmjSJHTt2MHr0aK688koef/xxlFJM\nnjy51QEsXryYoUOHct9991FVVUV5eTlLly5l0KBBTJkyhWXLlrFs2TJuuummVt/L61yFqJ79/B2F\nEEK0OY+SxZQpU9zfT5gwgZSUFMrKyujWrVurbl5aWsr27du54447zGCsVqxWK5mZmTz22GPu+z32\n2GMBnyy01uY6C+mGEkK0Qy1aPGGz2drk5nl5eURGRrJo0SIOHDhA7969ueWWWygsLCQmJgaAmJgY\nioqKGrw+IyODjIwMAObMmdPiuKxWa6t/p6oDe8gvO0FE8kDC2uj1gbaJzRskruYJ1LggcGOTuJrH\n23H5daVddXU1+/bt49Zbb6Vfv34sXryYZcuWeXx9WloaaWlp7p/tdnuL4rDZbC2+tpax8j8AlPQe\nQGkrn+tkbRGbN0hczROocUHgxiZxNU9L40pK8mxLBb/O8YyLiyMuLo5+/cx+/tGjR7Nv3z6ioqJw\nOp0AOJ1OIiMDfzMhvXkd9EpGRcf6OxQhhGhzfk0W0dHRxMXFceTIEQCysrLo1q0bqamprFq1CoBV\nq1YxcuRIf4bZJF3ggH27UEPO83coQgjhFX4v+HTrrbeyYMECqqqqSEhIID09Ha018+bNY8WKFdhs\nNu69915/h3laOms9gCQLIUS75fdk0bNnT+bMmXPK8dmzZ/shmhY6fABCO0LXc/wdiRBCeIXUpWgL\nhU6IjpXS7UKIdkuSRRvQhQ6IivF3GEII4TWSLNpCoRMlyUII0Y5JsmgLhU5pWQgh2jVJFq2ky0qh\nvEyShRCiXZNk0VoF5uJBomQxnhCi/ZJk0VqFZrKQMQshRHsmyaKVdKHD/EZaFkKIdkySRWvVtCyI\nlpaFEKL9kmTRWoUOsFoh7AzYyU8IIVpIkkVrFRZAZIys3hZCtGuSLFpJVm8LIc4Gkixaq9Apg9tC\niHZPkkVrFTpRMrgthGjnJFm0gi47ASUu6YYSQrR7kixaY8dmAFSfAX4ORAghvMvvmx8BGIbBQw89\nRGxsLA899BB5eXnMnz+f4uJievXqxV1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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -905,7 +933,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 23, "metadata": { "collapsed": true }, @@ -972,7 +1000,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 24, "metadata": { "scrolled": true }, @@ -981,86 +1009,82 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration 1: Average Return = 24.73\n", - "Iteration 2: Average Return = 23.87\n", - "Iteration 3: Average Return = 27.47\n", - "Iteration 4: Average Return = 33.18\n", - "Iteration 5: Average Return = 31.91\n", - "Iteration 6: Average Return = 35.31\n", - "Iteration 7: Average Return = 40.03\n", - "Iteration 8: Average Return = 39.31\n", - "Iteration 9: Average Return = 42.83\n", - "Iteration 10: Average Return = 44.94\n", - "Iteration 11: Average Return = 50.35\n", - "Iteration 12: Average Return = 51.74\n", - "Iteration 13: Average Return = 49.53\n", - "Iteration 14: Average Return = 51.19\n", - "Iteration 15: Average Return = 52.05\n", - "Iteration 16: Average Return = 54.92\n", - "Iteration 17: Average Return = 59.76\n", - "Iteration 18: Average Return = 58.6\n", - "Iteration 19: Average Return = 61.67\n", - "Iteration 20: Average Return = 64.15\n", - "Iteration 21: Average Return = 68.03\n", - "Iteration 22: Average Return = 68.49\n", - "Iteration 23: Average Return = 78.09\n", - "Iteration 24: Average Return = 74.99\n", - "Iteration 25: Average Return = 80.57\n", - "Iteration 26: Average Return = 85.57\n", - "Iteration 27: Average Return = 84.63\n", - "Iteration 28: Average Return = 88.31\n", - "Iteration 29: Average Return = 85.01\n", - "Iteration 30: Average Return = 91.76\n", - "Iteration 31: Average Return = 97.26\n", - "Iteration 32: Average Return = 96.99\n", - "Iteration 33: Average Return = 97.67\n", - "Iteration 34: Average Return = 111.77\n", - "Iteration 35: Average Return = 113.64\n", - "Iteration 36: Average Return = 113.43\n", - "Iteration 37: Average Return = 114.7\n", - "Iteration 38: Average Return = 125.34\n", - "Iteration 39: Average Return = 126.87\n", - "Iteration 40: Average Return = 125.36\n", - "Iteration 41: Average Return = 129.83\n", - "Iteration 42: Average Return = 135.94\n", - "Iteration 43: Average Return = 139.24\n", - "Iteration 44: Average Return = 147.21\n", - "Iteration 45: Average Return = 156.18\n", - "Iteration 46: Average Return = 163.94\n", - "Iteration 47: Average Return = 162.78\n", - "Iteration 48: Average Return = 159.45\n", - "Iteration 49: Average Return = 159.3\n", - "Iteration 50: Average Return = 161.98\n", - "Iteration 51: Average Return = 161.6\n", - "Iteration 52: Average Return = 170.6\n", - "Iteration 53: Average Return = 175.29\n", - "Iteration 54: Average Return = 175.56\n", - "Iteration 55: Average Return = 181.96\n", - "Iteration 56: Average Return = 178.31\n", - "Iteration 57: Average Return = 175.13\n", - "Iteration 58: Average Return = 177.73\n", - "Iteration 59: Average Return = 180.48\n", - "Iteration 60: Average Return = 181.43\n", - "Iteration 61: Average Return = 181.03\n", - "Iteration 62: Average Return = 183.87\n", - "Iteration 63: Average Return = 185.26\n", - "Iteration 64: Average Return = 190.9\n", - "Iteration 65: Average Return = 191.03\n", - "Iteration 66: Average Return = 191.6\n", - "Iteration 67: Average Return = 192.31\n", - "Iteration 68: Average Return = 193.8\n", - "Iteration 69: Average Return = 193.29\n", - "Iteration 70: Average Return = 192.18\n", - "Iteration 71: Average Return = 192.67\n", - "Iteration 72: Average Return = 192.53\n", - "Iteration 73: Average Return = 191.81\n", - "Iteration 74: Average Return = 194.7\n", - "Iteration 75: Average Return = 193.82\n", - "Iteration 76: Average Return = 193.92\n", - "Iteration 77: Average Return = 191.33\n", - "Iteration 78: Average Return = 194.29\n", - "Iteration 79: Average Return = 195.03\n", - "Solve at 79 iterations, which equals 7900 episodes.\n" + "Iteration 1: Average Return = 19.91\n", + "Iteration 2: Average Return = 18.4\n", + "Iteration 3: Average Return = 19.53\n", + "Iteration 4: Average Return = 22.78\n", + "Iteration 5: Average Return = 23.65\n", + "Iteration 6: Average Return = 25.54\n", + "Iteration 7: Average Return = 27.71\n", + "Iteration 8: Average Return = 32.29\n", + "Iteration 9: Average Return = 28.77\n", + "Iteration 10: Average Return = 33.15\n", + "Iteration 11: Average Return = 34.87\n", + "Iteration 12: Average Return = 40.8\n", + "Iteration 13: Average Return = 41.48\n", + "Iteration 14: Average Return = 43.07\n", + "Iteration 15: Average Return = 47.6\n", + "Iteration 16: Average Return = 47.86\n", + "Iteration 17: Average Return = 46.81\n", + "Iteration 18: Average Return = 46.03\n", + "Iteration 19: Average Return = 53.02\n", + "Iteration 20: Average Return = 50.85\n", + "Iteration 21: Average Return = 56.04\n", + "Iteration 22: Average Return = 57.77\n", + "Iteration 23: Average Return = 54.57\n", + "Iteration 24: Average Return = 62.82\n", + "Iteration 25: Average Return = 65.64\n", + "Iteration 26: Average Return = 64.68\n", + "Iteration 27: Average Return = 64.74\n", + "Iteration 28: Average Return = 72.81\n", + "Iteration 29: Average Return = 74.57\n", + "Iteration 30: Average Return = 86.19\n", + "Iteration 31: Average Return = 85.78\n", + "Iteration 32: Average Return = 93.65\n", + "Iteration 33: Average Return = 97.91\n", + "Iteration 34: Average Return = 125.64\n", + "Iteration 35: Average Return = 113.59\n", + "Iteration 36: Average Return = 122.85\n", + "Iteration 37: Average Return = 131.33\n", + "Iteration 38: Average Return = 124.59\n", + "Iteration 39: Average Return = 122.64\n", + "Iteration 40: Average Return = 136.92\n", + "Iteration 41: Average Return = 141.61\n", + "Iteration 42: Average Return = 146.41\n", + "Iteration 43: Average Return = 157.23\n", + "Iteration 44: Average Return = 165.37\n", + "Iteration 45: Average Return = 163.88\n", + "Iteration 46: Average Return = 160.78\n", + "Iteration 47: Average Return = 166.6\n", + "Iteration 48: Average Return = 178.25\n", + "Iteration 49: Average Return = 166.47\n", + "Iteration 50: Average Return = 168.98\n", + "Iteration 51: Average Return = 166.51\n", + "Iteration 52: Average Return = 168.31\n", + "Iteration 53: Average Return = 164.27\n", + "Iteration 54: Average Return = 174.25\n", + "Iteration 55: Average Return = 175.09\n", + "Iteration 56: Average Return = 175.08\n", + "Iteration 57: Average Return = 171.07\n", + "Iteration 58: Average Return = 179.65\n", + "Iteration 59: Average Return = 176.71\n", + "Iteration 60: Average Return = 184.62\n", + "Iteration 61: Average Return = 187.19\n", + "Iteration 62: Average Return = 185.63\n", + "Iteration 63: Average Return = 184.89\n", + "Iteration 64: Average Return = 182.38\n", + "Iteration 65: Average Return = 187.64\n", + "Iteration 66: Average Return = 193.47\n", + "Iteration 67: Average Return = 189.34\n", + "Iteration 68: Average Return = 187.74\n", + "Iteration 69: Average Return = 186.09\n", + "Iteration 70: Average Return = 189.64\n", + "Iteration 71: Average Return = 192.96\n", + "Iteration 72: Average Return = 191.75\n", + "Iteration 73: Average Return = 188.43\n", + "Iteration 74: Average Return = 192.56\n", + "Iteration 75: Average Return = 195.96\n", + "Solve at 75 iterations, which equals 7500 episodes.\n" ] } ], @@ -1083,14 +1107,14 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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z2bDBnN28YcMG5syZ067MzJkz2bp1KzU1NdTU1LB161ZmzpzZo/v2lIr1JEGZ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\nbFQVbVzDf7AAAAAASUVORK5CYII=\n", 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j0WCWVU+6sMvX6YygaLkYhsGrr77KAw88wPLly/nwww/Jz89vdsyGDRsYOHAgK1as4Oqr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\nRYsAyM3N5b333uOOO+7AZrPx9a9/nfv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j9OnTzjWjhPAKWzfA/7ahbrtHlhwXooq51Aw1cOBA9uzZQ8+ePRkyZAjTpk1D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\nAcUAxUajlK0rAAAAAElFTkSuQmCC\n", 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o71agevTH6Na7zP1VYC1o2QHVUpr8hBDFeex9FuL66MP70Ivn2Tuv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\nSnVl+fTTTwF47bXXqFmzJlu2bOGDDz5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"text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/report.md b/report.md index 75ff60e..f049578 100644 --- a/report.md +++ b/report.md @@ -141,11 +141,25 @@ a = util.discount(a, self.discount_rate*LAMBDA) -* Problem 5 +Here we prove why baseline won't produce bias. + +
+ +
+ +We add baseline by subtracting the baseline function B(s). Since the operation is linear, we can only consider the term in the figure above which is related to B(s). Since that the baseline function B(s) only depend on s, we can take it outside the sigma which sum over all the actions. And the summation over all the policies is equal to 1, so the gradient on a constant is 0, thus the baseline won't introduce bias. + +By comparing problem 3 and 4, we can find that without baseline, the result will have bigger variance. + +* Problem 5 + + + + @@ -154,7 +168,7 @@ a = util.discount(a, self.discount_rate*LAMBDA) * Problem 6 -
+
@@ -163,3 +177,6 @@ a = util.discount(a, self.discount_rate*LAMBDA)
+In problem 6 we introduce a hyperparameter lambda, which value is between [0,1]. If lambda is 0, then this GAE algorithm will reduce to actor critic algorithm as we implemented in problem, who has lower variance but introduce bias. On the other hand, if lambda is 1, it will have higher variance. + + From 9c622262db5d3b2c4787e4b892883c9620a23356 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:22:19 +0800 Subject: [PATCH 12/20] add images --- imgs/bias.PNG | Bin 0 -> 20311 bytes imgs/problem3_1.PNG | Bin 0 -> 47942 bytes imgs/problem3_2.PNG | Bin 0 -> 34116 bytes imgs/problem4_1.PNG | Bin 0 -> 71033 bytes imgs/problem4_2.PNG | Bin 0 -> 34620 bytes imgs/problem5_1.PNG | Bin 0 -> 60574 bytes imgs/problem5_2.PNG | Bin 0 -> 47575 bytes imgs/problem6_1.PNG | Bin 0 -> 45594 bytes imgs/problem6_2.PNG | Bin 0 -> 38968 bytes 9 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 imgs/bias.PNG create mode 100644 imgs/problem3_1.PNG create mode 100644 imgs/problem3_2.PNG create mode 100644 imgs/problem4_1.PNG create mode 100644 imgs/problem4_2.PNG create mode 100644 imgs/problem5_1.PNG create mode 100644 imgs/problem5_2.PNG create mode 100644 imgs/problem6_1.PNG create mode 100644 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zl$WR1cu<6n4V}Ya*-hZO0pBTr^BidUs4`yn$CEq|^C#gut~)tgrbHulY?{xhAkIpf zp5-?R|AYoF9l|{N^Rk1>GnFty%4-^~V*zUgXJj{nY08c#Wh_8ltzfH$ZWY6X9#(^! z+@?159d{$9wdsf=aoWQG9Pu#<%L!X_GXZVs=JQ!}Eu=nfdQ2fqjbjit9_AsJg!($sJ;N)TiKY>`Rvc5fZ^Yxj;ZS8x#%S#bsh=O%8dNS z5y0GThJ|F9lIc}^Bo+(Di-ui?o4_zPeSkfA6MogrWS?M`VK|PYe#T=n6-H<=wNcIE zk60j(lKUjgw-MWrfE&R3JuAp*GtHkR+;qZ${;{d)sgGQ?G?>>UXF!^Tzy9=zxe1a) z{hYiLF1)Z!ts(TO^aldR(RNFeiP-1R!uRW)mAe)Uw+kt74jqDFgnSul87E&4#7D|B z1z*}h;K$@L8=mLo&l-%~o4Z>g{snXDl((s+F7CcOVngza+U6;FqUl1^-~rbDdOh(p z`94*fv0+VA);BtGMb7rFDL+&lgi|^a|1{K8{-lL%uZqymWmSmv7}{2V0{{O$JjjDS lyF~x5qK_Y6f1UW?6lKrh@#O19!$cVPD9Eb93$Ggm{vW0uiCh2x literal 0 HcmV?d00001 From 10269d5cef7e24c7619c450b030574e0a319aaa0 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:27:02 +0800 Subject: [PATCH 13/20] Update report.md --- report.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/report.md b/report.md index f049578..16b77fa 100644 --- a/report.md +++ b/report.md @@ -124,8 +124,8 @@ a = util.discount(a, self.discount_rate*LAMBDA)
- - + +
@@ -135,8 +135,8 @@ a = util.discount(a, self.discount_rate*LAMBDA)
- - + +
@@ -160,8 +160,8 @@ By comparing problem 3 and 4, we can find that without baseline, the result will
- - + +
@@ -171,8 +171,8 @@ By comparing problem 3 and 4, we can find that without baseline, the result will
- - + +
From 539edcda15399b4ea44cdfb172781ee6360f8cea Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:28:40 +0800 Subject: [PATCH 14/20] Update report.md --- report.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/report.md b/report.md index 16b77fa..04cc890 100644 --- a/report.md +++ b/report.md @@ -124,8 +124,8 @@ a = util.discount(a, self.discount_rate*LAMBDA)
- - + +
@@ -135,8 +135,8 @@ a = util.discount(a, self.discount_rate*LAMBDA)
- - + +
@@ -160,8 +160,8 @@ By comparing problem 3 and 4, we can find that without baseline, the result will
- - + +
@@ -171,8 +171,8 @@ By comparing problem 3 and 4, we can find that without baseline, the result will
- - + +
From 31690baf21a0bd372934ee45a33ffdd140195086 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:35:02 +0800 Subject: [PATCH 15/20] Update report.md --- report.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/report.md b/report.md index 04cc890..d135907 100644 --- a/report.md +++ b/report.md @@ -177,6 +177,6 @@ By comparing problem 3 and 4, we can find that without baseline, the result will -In problem 6 we introduce a hyperparameter lambda, which value is between [0,1]. If lambda is 0, then this GAE algorithm will reduce to actor critic algorithm as we implemented in problem, who has lower variance but introduce bias. On the other hand, if lambda is 1, it will have higher variance. +In problem 6 we introduce a hyperparameter lambda, which value is between [0,1]. If lambda is 0, then this GAE algorithm will reduce to actor critic algorithm as we implemented in problem, who has lower variance but introduce bias. On the other hand, if lambda is 1, it will have higher variance, but it is more accurate on the value function (less bias). Thus lambda makes a compromise between bias and variance. From 996707867268ce61465aced157aee61cf1000dcf Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:39:30 +0800 Subject: [PATCH 16/20] Update report.md --- report.md | 1 + 1 file changed, 1 insertion(+) diff --git a/report.md b/report.md index d135907..dcfe4c6 100644 --- a/report.md +++ b/report.md @@ -79,6 +79,7 @@ In problem 5 we have to implement a simple actor critic algorithm by replacing t + From 7aaf94c3a4f3c0e9cbaccc692f3cae5c3848f01e Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:41:12 +0800 Subject: [PATCH 17/20] Update report.md --- report.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/report.md b/report.md index dcfe4c6..e6a9458 100644 --- a/report.md +++ b/report.md @@ -79,7 +79,10 @@ In problem 5 we have to implement a simple actor critic algorithm by replacing t - + + + + From 80898513baee622cab57cdd71c5724732a6ec888 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 17:43:37 +0800 Subject: [PATCH 18/20] Update report.md --- report.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/report.md b/report.md index e6a9458..6f393f9 100644 --- a/report.md +++ b/report.md @@ -46,6 +46,10 @@ Here in problem 3 we use baseline to reduce the variance. So we replace the loss + + + + @@ -105,6 +109,10 @@ Finally, we introduce generalized advantage estimation (GAE), which uses a hyper + + + + From b0a15e507ecc3d2398492623779e4af186f303d5 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 20:29:16 +0800 Subject: [PATCH 19/20] Update report.md --- report.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/report.md b/report.md index 6f393f9..a90973e 100644 --- a/report.md +++ b/report.md @@ -165,7 +165,7 @@ Here we prove why baseline won't produce bias. We add baseline by subtracting the baseline function B(s). Since the operation is linear, we can only consider the term in the figure above which is related to B(s). Since that the baseline function B(s) only depend on s, we can take it outside the sigma which sum over all the actions. And the summation over all the policies is equal to 1, so the gradient on a constant is 0, thus the baseline won't introduce bias. -By comparing problem 3 and 4, we can find that without baseline, the result will have bigger variance. +By comparing problem 3 and 4, we can find that without baseline, the result will have bigger variance. Since we subtract the value function of the current state from the reward, we obtain the advantage which shows how much the current action is better than we usually do at this state. The baseline compensate the variance introduced from different states. * Problem 5 From baae265e099286c227e3e43edc61e21a15072e93 Mon Sep 17 00:00:00 2001 From: petersci Date: Thu, 9 Nov 2017 21:40:10 +0800 Subject: [PATCH 20/20] Update report.md --- report.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/report.md b/report.md index a90973e..b914621 100644 --- a/report.md +++ b/report.md @@ -1,6 +1,8 @@ # 江愷笙 (106062568) +Here is the [github page](https://petersci.github.io/homework3-policy-gradient/) of my report. + # Homework3-Policy-Gradient report TA: try to elaborate the algorithms that you implemented and any details worth mentioned. @@ -155,13 +157,11 @@ a = util.discount(a, self.discount_rate*LAMBDA) Here we prove why baseline won't produce bias. - -
We add baseline by subtracting the baseline function B(s). Since the operation is linear, we can only consider the term in the figure above which is related to B(s). Since that the baseline function B(s) only depend on s, we can take it outside the sigma which sum over all the actions. And the summation over all the policies is equal to 1, so the gradient on a constant is 0, thus the baseline won't introduce bias. @@ -169,7 +169,7 @@ By comparing problem 3 and 4, we can find that without baseline, the result will * Problem 5 - +
@@ -180,7 +180,7 @@ By comparing problem 3 and 4, we can find that without baseline, the result will * Problem 6 - +
@@ -189,6 +189,6 @@ By comparing problem 3 and 4, we can find that without baseline, the result will
-In problem 6 we introduce a hyperparameter lambda, which value is between [0,1]. If lambda is 0, then this GAE algorithm will reduce to actor critic algorithm as we implemented in problem, who has lower variance but introduce bias. On the other hand, if lambda is 1, it will have higher variance, but it is more accurate on the value function (less bias). Thus lambda makes a compromise between bias and variance. +In problem 6 we introduce a hyperparameter lambda, which value is between [0,1]. If lambda is 0, then this GAE algorithm will reduce to actor critic algorithm as we implemented in problem 5, who has lower variance but introduce bias. On the other hand, if lambda is 1, it will have higher variance, but it is more accurate on the value function (less bias). Thus lambda makes a compromise between bias and variance.