From 6970fcc792c8a70d243e085a6749cb2a41be277f Mon Sep 17 00:00:00 2001 From: AnjieZheng Date: Thu, 9 Nov 2017 22:02:22 +0800 Subject: [PATCH 1/2] initial commit --- Lab3-policy-gradient.ipynb | 830 ++++++++++++++++++++++++++++--------- p3_1.png | Bin 0 -> 21005 bytes p3_2.png | Bin 0 -> 14907 bytes p4_1.png | Bin 0 -> 25460 bytes p4_2.png | Bin 0 -> 14773 bytes p5_1.png | Bin 0 -> 20890 bytes p5_2.png | Bin 0 -> 14677 bytes p6_1.png | Bin 0 -> 17818 bytes p6_2.png | Bin 0 -> 15291 bytes policy_gradient/policy.py | 4 + policy_gradient/util.py | 2 + 11 files changed, 640 insertions(+), 196 deletions(-) create mode 100644 p3_1.png create mode 100644 p3_2.png create mode 100644 p4_1.png create mode 100644 p4_2.png create mode 100644 p5_1.png create mode 100644 p5_2.png create mode 100644 p6_1.png create mode 100644 p6_2.png diff --git a/Lab3-policy-gradient.ipynb b/Lab3-policy-gradient.ipynb index 4529e50..9e14d84 100644 --- a/Lab3-policy-gradient.ipynb +++ b/Lab3-policy-gradient.ipynb @@ -28,17 +28,11 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2017-09-12 22:50:43,560] Making new env: CartPole-v0\n" - ] - } - ], + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], "source": [ "import gym\n", "import tensorflow as tf\n", @@ -103,14 +97,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/Users/andrew/miniconda2/envs/cedl/lib/python3.5/site-packages/tensorflow/python/ops/gradients_impl.py:95: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n", + "/home/anjie/anaconda3/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", " \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n" ] } @@ -214,6 +208,7 @@ " 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 +253,94 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "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 = 23.02\n", + "Iteration 2: Average Return = 23.96\n", + "Iteration 3: Average Return = 23.3\n", + "Iteration 4: Average Return = 25.42\n", + "Iteration 5: Average Return = 26.16\n", + "Iteration 6: Average Return = 30.4\n", + "Iteration 7: Average Return = 31.46\n", + "Iteration 8: Average Return = 31.78\n", + "Iteration 9: Average Return = 35.81\n", + "Iteration 10: Average Return = 34.07\n", + "Iteration 11: Average Return = 38.42\n", + "Iteration 12: Average Return = 39.99\n", + "Iteration 13: Average Return = 43.81\n", + "Iteration 14: Average Return = 41.9\n", + "Iteration 15: Average Return = 45.51\n", + "Iteration 16: Average Return = 48.5\n", + "Iteration 17: Average Return = 43.26\n", + "Iteration 18: Average Return = 47.94\n", + "Iteration 19: Average Return = 48.15\n", + "Iteration 20: Average Return = 52.89\n", + "Iteration 21: Average Return = 52.56\n", + "Iteration 22: Average Return = 50.18\n", + "Iteration 23: Average Return = 58.47\n", + "Iteration 24: Average Return = 62.32\n", + "Iteration 25: Average Return = 53.08\n", + "Iteration 26: Average Return = 56.62\n", + "Iteration 27: Average Return = 59.29\n", + "Iteration 28: Average Return = 65.93\n", + "Iteration 29: Average Return = 63.26\n", + "Iteration 30: Average Return = 67.14\n", + "Iteration 31: Average Return = 64.87\n", + "Iteration 32: Average Return = 73.27\n", + "Iteration 33: Average Return = 73.41\n", + "Iteration 34: Average Return = 73.19\n", + "Iteration 35: Average Return = 83.89\n", + "Iteration 36: Average Return = 85.32\n", + "Iteration 37: Average Return = 90.25\n", + "Iteration 38: Average Return = 92.49\n", + "Iteration 39: Average Return = 99.85\n", + "Iteration 40: Average Return = 116.93\n", + "Iteration 41: Average Return = 119.93\n", + "Iteration 42: Average Return = 118.09\n", + "Iteration 43: Average Return = 127.55\n", + "Iteration 44: Average Return = 120.04\n", + "Iteration 45: Average Return = 124.38\n", + "Iteration 46: Average Return = 123.53\n", + "Iteration 47: Average Return = 141.95\n", + "Iteration 48: Average Return = 145.59\n", + "Iteration 49: Average Return = 144.63\n", + "Iteration 50: Average Return = 144.98\n", + "Iteration 51: Average Return = 165.47\n", + "Iteration 52: Average Return = 163.97\n", + "Iteration 53: Average Return = 162.38\n", + "Iteration 54: Average Return = 166.69\n", + "Iteration 55: Average Return = 163.58\n", + "Iteration 56: Average Return = 165.28\n", + "Iteration 57: Average Return = 168.51\n", + "Iteration 58: Average Return = 174.09\n", + "Iteration 59: Average Return = 176.56\n", + "Iteration 60: Average Return = 182.93\n", + "Iteration 61: Average Return = 181.46\n", + "Iteration 62: Average Return = 177.08\n", + "Iteration 63: Average Return = 183.54\n", + "Iteration 64: Average Return = 181.86\n", + "Iteration 65: Average Return = 183.21\n", + "Iteration 66: Average Return = 184.06\n", + "Iteration 67: Average Return = 182.35\n", + "Iteration 68: Average Return = 189.33\n", + "Iteration 69: Average Return = 191.96\n", + "Iteration 70: Average Return = 189.36\n", + "Iteration 71: Average Return = 187.51\n", + "Iteration 72: Average Return = 193.92\n", + "Iteration 73: Average Return = 192.94\n", + "Iteration 74: Average Return = 190.02\n", + "Iteration 75: Average Return = 189.59\n", + "Iteration 76: Average Return = 190.69\n", + "Iteration 77: Average Return = 194.18\n", + "Iteration 78: Average Return = 190.52\n", + "Iteration 79: Average Return = 193.96\n", + "Iteration 80: Average Return = 195.58\n", + "Solve at 80 iterations, which equals 8000 episodes.\n" ] } ], @@ -371,14 +362,14 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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AhoYGrFy5EgCwcuVKNDREtrZuampCRUUFysvLYTKZsGLFCnW/xYsXw2gUGSDz\n5s2DO1aQUUeoBUz64FJAFPoR2iO7VNS01jio6cihrrG2FqDLHXYulW4l/qDBt/hoK5cBX2IBfSBu\npljSqG6xKDU/PR5ha5TRyykhzenICQf0P/KRj+DBBx+E3+/H3XffDQA4ceIEqqripCQmQHd3N0pL\nxX8oq9WK7u7IrCq32w27PTiUyG634/Tp0xH7vfbaa1ixYkXMa+3atQu7du0CAGzevBkOh2NMNptM\npqSOHZ4+E24AZgSQP8ZrJkqytqWLdNnl/e3PMPD6K3Bs+3XCx0z2zyxRBpqOoRuAdc78UW0LLKyF\nE0CRtxuF8n79f/8blAHIRT6vuh0ABs+eRBcAa/VM5IzjPZtMJuSbSzAwPBxmn9M/jByLBZYY5x7u\nLYfytdRy2RXI0+hzH+6uEH/7ebkRn1n3QD+GSx1wlCUX3xmzLXPmC1sG+8PuQ6n6f5awuKxZswbX\nXHMNDAYDKiqEAtpsNtx7772jHrtx48aogf/bb7897DljLDKVL0FeeOEFGI1GXH/99TH3qa+vR319\nvfrc6XSO6VoOhyOpY3lABPJ7Wi7AO8ZrJkqytqWLdNkV2L8PuHQenR3tsWsaRjDZP7NEkd4XLvAu\nUz5y/P64tnEOIK8A3jMn0S/vF2jYC9inAF1ueM81qdsBQGoR2ZRdnIGN4z07HA4MBCTwoYEw+wL9\nfZDAYtrM+4MxkR6zdVw2hJ3XJ6r+ezrakT/iMwt0tgPFJWn7HfMc4W3qef80vDUL1e3J/j+rrIzT\nnSGEpFKRQ0969OhRGAwGLFy4MM4RgocffjjmaxaLBR6PB6WlpfB4PCgpicy4sNlscLmCvluXywWb\nzaY+f/3113HgwAE88sgjYxanlFJsFjnm5BZLKVySgPNnxJO+PsAcJXuHGDvOdqCgCCyBDr6MMaAi\n2MCS+/3AyUawa1aCnzgMdI50i2nU+gUQ2WJytb36BWMwQbdYcUlklf14GC1bLF4bHa1RWu+nyS2W\ncMzl0UcfxYkTotX0zp078cMf/hA//OEP8cILL4zLgLq6OuzevRsAsHv3bixdujRin5qaGrS2tqKj\nowN+vx/79u1DXV0dAJFF9oc//AHf/OY3kRertYPOMINR3Oh6SVxSSkdr8I+4r0dfW7IQ7uwQ9SsJ\nwspDGlieOw0M+MAWLgYcFeAjb3A9XUBhkaiRGS9KcH5YxIO5FBCpyQmkIqNqhrZfUJVGmTHqXDSJ\nMSVIuls/TsRHAAAgAElEQVTvJywuFy5cwLx58wAAr776Kh599FFs2rQJf/3rX8dlwJo1a3DkyBGs\nX78ejY2NWLNmDQARZ3nssccAAEajEWvXrsWmTZuwYcMGLF++HNXVothq+/btGBgYwMaNG/H1r38d\nP/7xj8dlT8qgFjAphyurFgDw9sbekRgbnW3BoHAiVFQB7k7woUHw9w6JmfQLrlA79IbCezza1Xuo\n0yjl5pVKt+NYHZEBVVxYpYbBfADIla85YuXC/f7xT90cC2lsvZ+wW0wpAGxrE4ZNmyYmzo23rsRs\nNuORRx6J2G6z2fDggw+qz5csWYIlS5ZE7PfUU0+N6/ppo8RKbrFU0xwiLjRWOmGkN/8K/rc/wvD/\nfT9mfQeXJMDVAXZFXeInrqgSwZeOVvDjh4HpNWBFZvCyCqCvF7zfC1You9i6NeyxpYiLUkipNq2M\n7RZjOTlga+4Cu/JabWxQzmswyDNdRrjFlHZQaajOD7OnrBz8SAO4JKV8hkzC4jJ//nz853/+Jzwe\nj+q6amtrg9lsTplx2QQrsYK3X9LbjKyGNzcJn7m3B7yvBxkYfcs4OOfgf3lBpAy3X1LHFEfQ4xHp\nvYnUuMiw8kpwALz5DHD2JNhNwivBHBXggFi9TJfFpacLLM6MmKRQ3WKyuAyO0hFZxvDx27S5/kii\nNa9Up26meeWitN7vcot2MCkkYem67777UFhYiBkzZuC228Qv4dKlS/jYxz6WMuOyCrOFWsCkEM45\ncP59sIVXig3kFkuM90+qtSi8OTK9X0WpcXEk4RaTOyfzN/4CBAJgl8m/G6V9TKhrrMejWTEhy5FF\nRHGLyTd2Fi+gn0qitd3XMoEhCdQapTQ0sEx45WI2m3HnnXeGbYvmpiJiUGIVy/RBH6BU7RLa0dkK\n+PqA+bXA/jdFJTkxKnzvLhFv4Fy4FZetir6fIgTJrFzy8oFSB3DmhHBVzblMPoe4wfHOdjAAfHBQ\n3Hy1coupKxe5mHa0EcepJj8ffGTMpTu9rV9UplaDXX8TUDh6xt94SVhc/H4/XnjhBezZs0dNHf7g\nBz+IW265BaYoE9uIEYRW6ZO4aA6X4y1s5hzwIjOJSwLwwQHwhjfA6q4Db78o3IqxUFKHk8gWAyDi\nLh4nMHchmBwLYYVFIi1W+fas1QRKBZOccTY00i2ml7gURGaL9aS59YsMsznA/vlLablWwqrwy1/+\nEmfOnMHnPvc5lJWVobOzE7///e/R39+vVuwTsWElVuFn7ukCpqQxt32y0Nwk5p5XThc3LnKLjQo/\nsE+kB39gNbB/L/i+V8NrQ0JxtgNWmyoQicLKq8CPHw66KxUc5cF0ZKX1vFbxh9zobrG0tVkZSV5B\n5Hjhbo+cep3c5zmRSDjm8vbbb+Mb3/gGFi9ejMrKSixevBj3338/3nrrrVTalz2oKxcaGpYKePMZ\noGqmmFtRVAxOK5dR4Xt3AVOmAnMXATPniG/XMZJOuLM9KZeYipwgwC5bHLZZdOiVV0Oqi0ijb/Ej\nssW4zisXFmXlomnqdYaSsLhQIHqcyOJCtS7aI4L5Z4LZRsUltHIZBd7RCpw6CrZitWi7NGOO2H4u\nhmvM2ZZcMF+GrbgBhi8+ADZ9RCZYWTng6hCNRrV2EckxF3XUcQKpyCklP0pAP13jjXUkYXFZvnw5\nvve97+HQoUNoaWnBoUOH8Pjjj2PZsmWptC97KLaInyQu2uNsF7NyZHFhRWagn8QlHnzfqwBjYMtv\nEBsqpgl3UpS4C/cPAx7XmFYuLL8QbEmUZrKOctF63+MWMRfGgn8j4yVnRCrykCwu8YooU0nUbLGu\nYLf0LCXhmMtdd92F3//+99i+fTs8Hg9sNhtWrFiBW2+9NZX2ZQ3MZBI9xqgFjPbIN0Tl2zeKKeYS\nDy4FwN96DVh4JZhc68CMRqB6VvSgvqtTZJONxS0Wg2CtS5sooCwuEX8jWjBSXAYGxAhikwatZcZC\nfj4wNBA+OK3Hk/Url7i/zaNHj4Y9X7RoERYtWgTOudp/58SJE7j88stTZ2E2YaYWMKmAN58BjCag\nUh7yVFgMDA2CDw9ldcB0zJxoBNxOsFvXhm1mM+eCv/G/kUF9dY6LduKitJHhnW3ib0LLb/G5Iyr0\n5RHHujW1zS8AOFdjP7ynS6xkrLZRDpzYxBWXH/3oR1G3K78kRWSefvpp7S3LRqgFTErgzU2i4aDS\n9FCZZe7tBUrtsQ+cpPBjBwFTDtjiEU1ip9eIPlxtF0XWnbL/GGpcRqXUIVYTznZNCygBABFFlKNM\noUw18rW5TwwM4wdFEhRbeJVuJqWDuOKybdu2dNkxKWAl1vi1BETScLn4j10d9OuzYrNwufSRuESD\nHz8M1CwIzo2XYTPmiHYt55rCGzh2tok0bw2/aTOTCbCViYyxbg/Y3NFHdyR8bqMRMBqDRZSDA/oF\n8wE1BZoP+IDcAvD9b4r6n2kz9bMpDaS2cxkRDq1ctMfZDvR7ASXeAog6F4AKKaPAe3uAC2fBFlwR\n+eLUKhHUD+0uDYjP2DYl4eFrCVNWAd7ZKuKQWge3TbnBVOQBn64rF6aIi68fvMstsvTqrs/M2VMa\nQuKSTswWYMAHrrQAJ8aPEswPTXVVxIWC+pGcagQQWXcCyHOHps8GPxfsMcYHfOBnT4p6GI1hjnLg\n0nkhAlrXfOTmhrvF9MoUA9RVE/f1i8JVzsGWXqefPWmCxCWdKN/OeqMXUvKzpyA9/0x4VgkRF2nv\nq0K0q2cGN8riwmlgWAT8+GFxswtd6YXAZswBLpwFDwTE/r/+MeBxwfCRf9DemLKKYHGh1q3nc3LD\n279khFusH3z/GyI+qPXcmAyExCWNqO0tYrjGpD/9FnzPK6IdNjEqvOUscPQA2OpPisp8hWLFLUYz\nXUbCTzQC8xbFTvudMUcO6rdAangDfO+rYB/7R7D5KcgIDSnK1LzmIzc3pOW+TzTR1AtZXPwtzUDT\ncbCl1+tnSxohcUknyh+QxxnxEu/zAo0HxJM0tMPOBvhfdooU0w99NGw7y80TN5cobjHudorPehLC\n3U6g/WJUl5gCmylX6h/YB/78NmD2fLBP3J4Se8JSm7V2i5lygxX6g4MZkS3me/VlAJgULjGAxCW9\nVE0HCoshvbMn4iV+cJ+oWIZoRU7Eh7s6wRv2gF1/k6jIH0lRCRDFLSb94FHh6pmE8BOHASB6MF+h\nvBLIywd/6VcAAMM9/65dceNIQkcma11QOGLlolvTSkC9dqDlnJjGOUka15K4pBGWmwd23Y3Au2+L\nb5Eh8L/vFn9szCBmkxBx4bv+RwRG6z8dfYeiYvARKxceCADtF8MC1qNeJ03zxtPCiSOiBqhqRsxd\nmMEIVM8Wjz/zxeBwqVRQWAwUFIp6l2hfEMaDHHPhnGdAnUtQ2CbLqgUgcUk77EMfFdW6e15Rt3GP\nS6QnLlslRo/SyiUuvM8L/sZfwK75IJi9LPpORebImIu7E5AkoL016DKJd53G/ZAe+nxSYpSpcM7B\njx8Bm1876ux0w4dvBrvlX2C4dmVKbWKMicJMs1X7ee65eWLl4vcDgUCwDb8OMKNRbUnD6khciBTB\nyiqAK5aC7/kLuFzkxRveEN/Cr/kgMGWqyP0nYsJf/xMwOAD24Ztj71QcZWCYUmnOJaD1wujXkWNg\nys+JhPT33eGrrvZLQJcLiBNvUWBXXgvDR1OQHRbtWrPmxV1JjZmcXJGYoPcsF4X8AuTMWyTSrycJ\nJC46YFj1caC3G/zAXgAAf2cPMGMOWEWV+M/npJVLLPjwEPirLwGLrgKbNivmfqyoBPCGx1x4yOfK\nW5pHv9Yp0VuPv3dojNbqA+9sA//pk5C+ez/4WbHqUuMtl8WJt+gAu/NeGNY/ov15c3IA/7D+UygV\ne/7hX1B895d1tSHdkLjowWWLgfIq8NdeBm9rAZqbwBQXRNlUITwD/framKmcPAr0dsOw+pPx9ysq\nBvq94XOInB3B7rgX44sL7+0W+xQUAWdPTqzfx4Wz4qcUgPTEQ+BHD4CfOCJcrmXaF0OOB2Y0CreR\n1uTmiToXvWe5yBg+UI/cDBP2VEPiogPMYABb9XHg7ClIv90h5mrIgT41PZPiLlHh7g7xoHIUV0qx\nWfjaQ+doONtFw8Sp08Avnot//Em5kv3DN4vznDo2dqPTDG85CzAGw7e2AOWVkJ7+DtC4H2zB4qxv\nOaKSI1foy24xpmeF/iSFxEUn2IobxLepIw3AvMvBrHKDReWbJcVdouN2ioy60ZooFimdkYOuMe4S\no3pZ1YzRVy4nG4G8ArDVnwRyckVl+wSBt5wDplSClVXA8PXHgHmXi2/xk+mbs1KhnyFusckIiYtO\nsIJCsOWrxOPQrBx55UK1LjFwOwFL6aiuFFYcpXllZ5uIaVXNALrc4HEaW/ITjcDchaLp4NyFEyvu\n0nIOTO64ywoKYfjyIzDc95BIGJks5OSKmIuyctXZLTYZIXHREfbRfwC77saw3HdWWCzy/6lKPyq8\nyyViB6NRVCx+yrUufHBQtN1xlINVzRSvxQjq8y430NYCtqAWgNzk8dJ5sT3D4b5+0SK/OpjswHJy\nwK5cpn1X40xGHhjGlZUrrVzSDomLjjBbGQz/8mWw/MLwF8oqwDtIXKLidoKVJiIuwi2mrk5c8kqw\nrEJNfeWXYoiLnCXG5inicqXYfmLsrjHOOaTf/UzEQ1KJHEuKl0k3KVAmkCriQjGXtEPikoGwsgpa\nuUSBcw54OhNbuYx0iymjeh3lYoBYYVHMlQtONorK8emiUh3Vs8T5RrjGeG83+MXziRnvbAf/ywui\n5boG8MEBdWxu2PYL58SD0C7RkxFl1LHSgZzcYmmHxCUTKasAXB1q23NCpq9XBGkTEZfCEW6xkFG9\njDGgakbMjDERb1mkxnWYwQC2YDH48cNqajP39kDa/A1Ij31duNxG48L74mdP9HELySI99x+QnvuP\nyBdazor3nsjqLptRRh33Km4x/Sr0JyskLplIWYVIf43SPXlSI/djY6UxWr6EwIxGUaMSunLJzVU7\nU7OqmSKOEloHA7kVT8clsPm14Se8bLEYhdDWAu4fhvSjzSK2MegDGhtGtYfLtSe8V6NJpBebgWMH\nIzo88wtngepZkyflOAYsR4xg4N4eICd3csWbMgQSlwxEbRaYTU0Tk0B68ZfgTe9FvqCIbSIrFyCs\nBQx3tgP28uBNt2oG4OsX/cZC4CePAIAazFdgC+W4y3uHwH/5I9EL7l+/ClhKITW8OaopirjEGhSX\nDFwKiFYukgR+9ED49ovNaqbYpEZduXRTMF8nUtRLO3G8Xi+2bNmCzs5OlJWVYcOGDSguLo7Y79Ch\nQ9ixYwckScLq1auxZs0aAMCvf/1r7N+/H4wxWCwWrFu3DjbbKDUQmY4sLryzLe7sjWyEuzrB//Qb\noMcDNmdh+GtKJ+lEXT5F5mBA3ylqXBTYtBnggIi7zA+5zolG4VYaERBnjnKRaPHSr4G+XrBP/BMM\ny1dBOnsK/M2/gg/0RyZmhKKIixZusZ4u0YATAA6/Ayip7B1top9W9SQP5gPBmIu3h8RFJ3Rfuezc\nuRO1tbXYunUramtrsXPnzoh9JEnC9u3b8dBDD2HLli3Yu3cvWlpaAACf+tSn8MQTT+Dxxx/HkiVL\n8Lvf/S7db0F7Su2A0aTpyoUHAuBnTmh2vlGv5/eP7Tiln1e0xpKeTvG5JDq1sNgMeHuF68vZDuaY\nEnxNrvAfGXfhp46KSY1RuvSyy64UwnL1B8A+eYfYtvR6YHgI/HBs1xjv6xUrJKMR8GogLh6X+Gm1\nidYuftEAFXIm2qTPFAOC2WK93fo3rZyk6C4uDQ0NWLlSfPNauXIlGhoi/0ibmppQUVGB8vJymEwm\nrFixQt2vsDD4bXFwcDArfM3MYATsU7QVl4P7IG3+BnjHJc3OGfNaRw9A+upnwLs9yR8st11Ba0tE\nPARuJ2C1JdyenRXKbrF+r3CBha5cCosAW1lYpT4/c0IUWo6MtyjHrPoo2KqPgf3rV4M21CwArHbw\n/XFcYy3nxM9Z84H+vqAYjBUl9vTBj4j3Jbem4RfOid5pldXjO382oIjLkM5TKCcxurvFuru7UVoq\nptBZrVZ0d0d+s3O73bDb7epzu92O06eDMzZ+9atfYc+ePSgsLMSjjz4a81q7du3Crl27AACbN2+G\nwzG2jBqTyTTmYxPFU1UNqcsFe5LXiWWbt8eDPgAlQwPIS7Htfd1ueAd9KL54FgU1c+PaNRJn03sI\nMAb0e2HPNcEQMqHQ3dsNTJkKW4L295SVYeDYQVj9g3ADKJk1F/khx3pmzYHUfhEmkwmlBg73c/8B\nY3klbB+/FQZzSeQJHQ7gyqURm3uvr0f/n1+ArSAfhqJIl27fWx3wAiha+gH0Nb0HW44JRvvo7yHW\nZ9Y/5EMvAPsnboXzld8j79QRlHywHp6OiwhUzYBjauonHabjb2AsKHb5fV7I6zvkmktQmgG2Zvpn\npvl5NT9jFDZu3IiursgsmdtvD5/NzRgb08rjjjvuwB133IEXX3wRr7zyCm677bao+9XX16O+vl59\n7nSOLRvL4XCM+dhEkSx28JNHk75OLNskuf6hu/ksDNNma2FiTCS5ALT3wFvoW7gkrl2hcFcHpPZL\nQG0d0LgfrmOHweZdrr4e6GgFmz0/4c9EMuSA9/XCc+q4sCevEN6QY6WySvBDDRjq7YZz4/1Afx8M\nX3kU7sEhYDDxz50vuhp46b/hfPVPMKy4IdKOE0cBSyl8VvEH7G5+H4yP/v885u+y5TxgyoEbRuCy\nxfC9vQeDn/4spPdPgs1ZlPL/m/Fs0xvFLt7Xp24bYsaMsDXTP7NEqaxM7MtLWsTl4YcfjvmaxWKB\nx+NBaWkpPB4PSkoivzHabDa4XC71ucvlihq0v/766/HYY4/FFJcJRVm5cKH0ecGifBtOFq5kRXW5\n4u+oBUqGVpKdhPlJEW8xrPwopMb94G0tqrhwSRK2J1O/oRRSNp8RP0cOaqqaAQT86Np4P3DutOi/\nNZbBVbPnA7Yy4RqLIi78wvvAtJlAiUVsGG9Q3+MESu3iy9jia8APvyNcY24nFU8qKAF9UEdkvdA9\n5lJXV4fdu3cDAHbv3o2lSyPdDjU1NWhtbUVHRwf8fj/27duHuro6AEBra7B7cENDQ8Kqmukwrbsj\nu9IpLnLtRVsLeE8ScZdTjWI8ce0SMY+j9WLwNW+3GFmbaBoyoM5l581NQGGxiLOEwKYJIRk+fhjs\n058Bu3JZ4ucOPQ9jYnzte4ci6078w8ClC2DVswGzSETg40xH5h6nKrLsCvH3Iv3xv8VzCuYLckKK\nJinmogu6x1zWrFmDLVu24LXXXlNTkQERZ3nuuefw4IMPwmg0Yu3atdi0aRMkScKqVatQXS2Clv/1\nX/+F1tZWMMbgcDjw+c9/Xs+3ox0h3ZHZzLnjOhWXJLWeg3tSLy68r1ce1jQovlEnODecn1QytYxA\nRRV4W0jGmBLETkJcWJFZpBs3n1HTu8OomAYUFCFvyTIMf3x8q1229Drw/30R/N23wK67MfhCWwsQ\n8Iv0YLO8chlvIaXHBTZXpE8zSykwax6gjASgNGSBXEQJgMRFJ3QXF7PZjEceiRxzarPZ8OCDD6rP\nlyxZgiVLlkTsd//996fUPt1wKIWUGqxcertF+3FAVJmnmr5eYO5CoOk4+Kmj4lv9KHBXh0gXrv80\nAIBVTAtPnVZrXEavzldR3GK+vkiXGABmyoFh07OwzJgFl3ucn8uMOaIO5u3XgRBx4efl9ODqWaJf\nmckUbEkyBoLuwWCCC1t8DfjZU4DZIsSGECnrzABwicRFJ3R3ixHRYfkF4puuU4O5Li55eqPZkiZx\n8YKVWIGaBQnHXbgy+XG+HMCfOg1wd6p9u3iy1fmA6hYD5CLIKDCzJeHU5ngwxsBWfgQ42Shu9AoX\nzgr/f3mlSFYxW8e3cunpEq2BQmJP7MprxQNatagwxoJxF2paqQskLplMWQW4BrUuXIm31FwG9HSl\nviFmXy9QZBbB+IvNwZka8Th5VKw0KqcDECsXcA60y3EXt1PULhRHSRGORYi4RFu5aA1b+RGgsBjS\nH3+jbuMtZ4GqmcHeVmYL+HgC+rJbM2zsQOV04LLFQZEhBEqtC61cdIHEJYNhU6YCrReEK2Q8yHPn\n2ZwFwk0wluLGBOF+v5j+V1QcTCM+HaVP2MjjTjaKcc/KKqJimtjeJjoxhGZIJUxBoaiKR+yVi5aw\n/EKw+k8Bh98BbzkrikAvnA3v9VViGV9/MWUFF7pyYQzGr22EYdXHx37ebEQRF8oW0wUSl0zm8quF\nEMgtUcaMqwMoKAKrkCu3U5kx1i/38ioyAzPnivnzo7jGuLMdcHWEV8aXVwqfuSwu3N2ZdBt5xliw\n9X4axAUA2A2fAPILwP/0OyEEfb1AdbCuiBWPT1xU92BIzIWIgSwujNxiukDiksGwK5cBBYXg+14d\n13m4qxOwlwFWuTYoleKipOIWmUXb89nz1X5hMe2T61tCxYXl5AKOKUBrcOWSTKaYiuJGC+0rlkJY\nUTHYqo+B739TbQnDQmMhJRagtyuytU2ieJwiKUDJPCNik0tuMT0hcclgWF4eWN114AffAh/wjf1E\n7k7Rq6xUiAv3jB7U54HA2NxxcgElk+MdbN4i4MJZSCPqP8I42ShEYOqInlgV08DbWuQW8+6xDcAq\nKhb9yHJyR99XI1j9p4GcHPA//JfYMC2kMNNsEQPPokyRTAiPKCTNhh56KYdiLrpC4pLhsOU3AIMD\n4AfHMR7X1QlmKwOKLSJFs3v0lYv09HfAf/F08tfyKm4x4Y5i8y4HuITh49Hnz3NJAj9+CGx+bUTW\nFps6DWi/BHjcosX8GMSFzasFq61L+rjxwEqsYNd/WIjIlKnhrfjlQsqxusa4HHsiEiBXLqSkrsi6\nQOKS6cy5TGSN7XttTIfz/j5R52GfIm7ellJxs453jN8PnDgSve39aNcLcYsBEJ2AjSYMjZg/r/L+\nSbEquSpKdXzFNNHOXh4cNha3mOHmu2D45y8lfdx4YTfdLIR8RHowU1rAjDXu4naGZ4oRsVEKKWnl\nogskLhkOYwxsxQ2ifmIsNS9KpphdLj4stYOPFnNpvSCKLhNJIR5JX0hAH8K1h1lzMXwsurjwA/sA\nk0ltYxIKmyoyxqAI01hiLjrBbA4Y1j8Mw5rPhr9gHru4iALKMboHJyPkFtMVEpcJAFu2CgDA3/5b\n8gcrNS52OaBttY0a0OfNTeLBmMTFK2aKFARdQWzBYgw3HQ/W2yjX4Vy4+xZeBVYQZYqjko6siEsy\n1fkZAFt4FVhFVfhGpb9YzxgKKb3dopUMucUSgin9xUhcdIHEZQLAHOXA/Frwfa8lnWXElep8m7gx\nM6t9VLcYFHHp70t+omRfj2gSGRJwFr22GPjf/hi+77kmwN0JdvWKqKdixSUi0N/lEjeIEY0nJyTK\nnJixuMXG0F9tUpObK9LZ05jMQQQhcZkgsBU3iMmUTceTO9DVCZhygu4Yqw0Y9IH7+mMewpUW9UCw\nbiVR+rzBnl4yzF6GvGUrwd/4C3hIlhQ/sBcwGsEWx6ksl1cv2ZIhxXLzRDuSsYiL0nSU3GKJUVgk\ninmz4P/NRITEZYLAlqwA8vLB30oysO/qAGxlwUwsq+xSidFjjPv9oh+W8u24Nzlx4XLrl5EUfvKf\nxEpItl91iS24Iu68GjXukk3f1kssY5rpQgWUycE+fDMMX4k9mZZILSQuEwSWXyC63x76e1L1J9wt\nF1Aq51FuTLHiLnIwny2SO1AnG3fp8war4kPImX85MGMO+KsvC/svnBXz6pdEd4mpyCuXrMqQMlvA\nx9K80i0XUBZTAWUisJJSjHdcBTF2SFwmErVXC3fK+TOj76vg7gSzh1SnyyuXWHNd+LnTAAC26Cqx\nIWlx6VULKENhjIm+W20twHvvilULM4BFS0EOPS4bVy5my9ja7ntcgNWuSRdngkg19L90AsEWLQEY\nA288kND+fHhI9CYLWbkE3WIxVi7nz4iis1nzxTnGIC4jYy4KrO4DgMUGadf/iHjLvEVgo7UxqZop\nss9GVu9PYFiJdWypyF1UQElMHEhcJhDMbAFmzgVv3J/YAcqALVtw5cLy8kSgM4a48OYzYvCVctNP\nQlxCOyJHtd+UA/ahjwLH3gXaLoJd/YFRz8lsDhi+82xC+04YzBbA2518ex2PC2yCpWMTkxcSlwkG\nq60Dzp1ObA67SymgHNG00WKL2l9MCeazGTWi6WR+QXJusf4R1flRYCs/IrLXGBvVJaYeU1aRXa4g\ns0UM/PL1JXwIlyR17ABBTASy6C92csBqrwY4Bz92cNR91RoX+4hvu6V2oDtKttil86Iyf3qNeF5c\nkpy4KNX5UQL6CsxsAbvpZrBlHwJTujRPNpRVYTIZY94ewO+nNGRiwmDS2wAiSabXiJtT4wFArtyP\nibtTFJFZw7/tMqsd/FJk3zClMl/NsCkuSS7monREHmVapOHmuxI/ZxbCzBZwQIw7VhIWRkOZQJlN\niQ1EVkMrlwkGMxjALr8a/OhB0Yo+Hq4O0W7eNOI7hNUO9Hgijz9/RrRtKasQz4tLkstqUptWxl65\nEBB1LkByQX2P3DqH3GLEBIHEZSJSWyfiG++firubOiRsJKU20cJ+RH8r3nwGmF6jxjdYkm4x7g1v\nWknEQO0vlri4cKrOJyYYJC4TELbwSsBgGD1rzNUBZoucwMgUN1lIUD80mK9SXBKcz5IIfeGzXIgY\nFCfXX4z3e4HmM6KFP02gJCYIFHOZgLCiYqBmgRCXm0VLd97vBd/3GgaqZ4JXzxaZXl2uGCuX0FoX\nOb6iBPNnzAnuV2wWfciGhxKb5Kh2RM6CBpMphBmN4rONIy68+Qy6/nMLAk3HRU85AKiakV1Zc0RW\nQ+IyQWG1deAv/AK8s020hPnjb4C+XnQD4gY/Y45Idx2ZhgwAFnnccZcbSks/NZgfKi5KB19vb2K+\n/lIdu+QAABGXSURBVP7eiI7IRAzM1pgtYHjzGUhPfgtDOTlgcxcB190INr0GmD0/zUYSxNghcZmg\nsNqrwV/4BaT/f72Yx77wKhhuvguWoiJ0vfka+NEDgMEQ7uZSKLEIAZILKTnnorAxNJgPEXPhgIi7\nJCIu3uhNK4komC1RVy78wllI338YKCyC/bs/gseQo4NxBDF+SFwmKlUzgaoZgNEIwz/8C9hC0Qss\n1+GAoawSuPkucCkAZjBGHMoMRrF6kYPE/M+/Az+wF+yj/xDudlFiAwkG9Xmc1i/ECMwlwMXzYZv4\nxWYhLPn5MPz7d2CcMhVwOnUykCDGB4nLBIUxBsOjW+O6oKIJi4rVBt7lgrT3VfAXnwe7ZiXYyJG8\nsrhwbw8ScnT1eQFLaSJ7TnqY2Qre26g+560tkJ78FmAywfDv3wELWUESxESExGUCM67YRqkdOH4Y\n/GQjsPBKsH9dHxksTnLlgr5esMrpY7dpMmG2AH29IkvP4xQrFsaEsEyp1Ns6ghg3JC6TFGa1i2mU\nM+bA8MUHwExRfPtK/CTRQsq+XkpDThSlkPJiM6TnvgcMDcLw9e+CVSRYsU8QGQ6JyySFLbwSvOUc\nDF/4Blh+YfR9jEbRJyyBlYvaEZliLgnBzFZwANIPvw0MDcHwtf8DNm2mzlYRhHaQuExS2OJrYFx8\nzeg7Jlqln0BHZCIEpRhywAfDVx4FozRjIsvQXVy8Xi+2bNmCzs5OlJWVYcOGDSgujnStHDp0CDt2\n7IAkSVi9ejXWrFkT9vpLL72E559/Hj/96U9RUhK/cSKRBOYEm1cm0BGZCKFqunBJfvpOsPm1eltD\nEJqje7nvzp07UVtbi61bt6K2thY7d+6M2EeSJGzfvh0PPfQQtmzZgr1796KlpUV93el04siRI3A4\nqO+S5iS6clE6ItPKJSFYkRnGb31fzOchiCxEd3FpaGjAypUrAQArV65EQ0NDxD5NTU2oqKhAeXk5\nTCYTVqxYEbbfz3/+c3zmM5+hyvAUwIrNifUXUzoiU8yFIAhkgFusu7sbpaWiNsJqtaK7O7Jq2e12\nw24PVojb7XacPn0agBAnm82GmTNnjnqtXbt2YdeuXQCAzZs3j3mlYzKZMnaVpLVtvWUV6O97A3a7\nPa54+xjQA6B02nSYolx/Mn1mWpGpdgGZa1um2gVkrm2psist4rJx40Z0dUX2Ubr99tvDnjPGklp9\nDA4O4sUXX8S3vvWthPavr69HfX29+tw5xupnh8Mx5mNTjda2SUYTMDQE56WLYHn5sfdruwQA8AwN\ng0W5/mT6zLQiU+0CMte2TLULyFzbkrWrsjKxOqy0iMvDDz8c8zWLxQKPx4PS0lJ4PJ6owXibzQaX\ny6U+d7lcsNlsaG9vR0dHB77+9a+r27/5zW/iscceg9Vq1f6NTEZCCynjiAv6vGLqZYy0ZoIgJhe6\nx1zq6uqwe/duAMDu3buxdOnSiH1qamrQ2tqKjo4O+P1+7Nu3D3V1dZg+fTp++tOfYtu2bdi2bRvs\ndju+973vkbBoCItSpc8DAUj/9Sx4aG+sflFASS3hCYIAMkBc1qxZgyNHjmD9+vVobGxUU4zdbjce\ne+wxAIDRaMTatWuxadMmbNiwAcuXL0d1dbWeZk8e1MFWIRljzU3gr/8J0ou/CG7r81KNC0EQKroH\n9M1mMx555JGI7TabDQ8++KD6fMmSJViyZEncc23btk1z+yY9UZpX8jMnxIPD74C3XQSrqBK1MNT6\nhSAIGd1XLkSGY47iFms6DpRYAVMO+K4/iI20ciEIIgQSFyI+BUUiUC+LC+ccOHMcbMFisOWrwPe9\nBt7bIzoik7gQBCFD4kLEhRkMojBSWbm4OoBuDzDnMrD6TwHDQ+C7/0wdkQmCCIPEhRid4mB/Md50\nHADAahaI2S2XXw3+2suiIzKtXAiCkCFxIUYntAXMmRNAXgEwbQYAwHDTmuAseFq5EAQhQ+JCjE5I\n80redByYPS84QnnBFYAyh4RWLgRByJC4EKPCZHHhvn7gYjPYnMuCrzEGdqOoTWKWUr1MJAgiw9C9\nzoWYACgrl/dPAlwCq7ks7GW27ENg1lJg3uU6GUgQRKZB4kKMTnEJEAiAHz0AMAaMmJrIDAZg4VU6\nGUcQRCZCbjFidJQq/cPvAFUzwAqoOSVBEPEhcSFGhSlV+p1tYDUL9DWGIIgJAYkLMTrFIWMQ5lwW\nez+CIAgZEhdidELEZWQwnyAIIhokLsToKOJiKQUc5fraQhDEhIDEhRid/ALAaAJqFiQ1hpogiMkL\npSITo8IYA7v1brARKcgEQRCxIHEhEsJQ/ym9TSAIYgJBbjGCIAhCc0hcCIIgCM0hcSEIgiA0h8SF\nIAiC0BwSF4IgCEJzSFwIgiAIzSFxIQiCIDSHxIU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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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QLquTf4E7d+6Mn5+fS/tu376dAQMG4OXlRYsWLQgPDyc1NdXNCYVoXH6eE0pFyGOzwjWW\njvH/5JNP2LRpE1FRUYwfPx4/Pz+ys7Pp0KGDc5/g4GCys7OrPD45OZnk5GQA4uPjCQ298MVWPD09\nL+p4d7FrLrBvNrvmAvtkO5OXxVnDILRzd1STJrbJVRW7ZrNrLnBPNsuKxbBhw7j11lsBWL58OW+8\n8QZxcXE1eo/Y2FhiY2Odr7Oysi44T2ho6EUd7y52zQX2zWbXXGCfbGbqfghtyan8fMjPt02uqtg1\nm11zQc2yRUS49ui0ZR0BgYGBGIaBYRgMGTKEgwcda/kGBwdz6tQp537Z2dkEBwdbFVOIBknLk1Ci\nhiwrFjk5Oc6vt23bRmSk4y9unz592LJlC6WlpWRkZJCenk779u2tiilEg6PLyuDkcRm5LWrEpdtQ\nZ86cYfXq1Rw5coSioqIK25544olqj1+8eDEpKSnk5+czdepUxowZw549ezh8+DBKKcLCwpg8eTIA\nkZGRREdHM2vWLAzDYNKkSfIklBA1pNPTICgU5dO08sbME1BeJnNCiRpxqVg888wzlJWVER0djbe3\nd41PMmPGjEpt11577Tn3Hz16NKNHj67xeYQQYG76BL0sCTw8oUNnVNfeqI5doagAnZcNB/YAMieU\nqBmXisWBAwd4+eWX8ZJpAYSwNb3jc/SbL0DnK1GRl6K/24l+7zX0b3cMC5c+C1EjLhWLtm3bcurU\nKcLDw92dRwhxgfSeXZgvL4J2V2DEPYpq0gRunYjOzoRD30MzPwgMhsCQqm9PCXEeLhWLrl278tRT\nTzF48GACAwMrbDvf7SQhRN3QB/dhJj0Frdpg3P8XR6H4iQoOg+AwC9OJhsClYrFv3z5CQkLYvXt3\npW1SLISwljbLHYUiIAhjxhMoX9dmSxCiJqotFlprpk6dSmhoKB4eHnWRSQhRExkn4HQuasIDqIAg\nq9OIBqraZ1KVUjz88MMopeoijxCipn48DIBqc5m1OUSD5tIAhksvvZT09HR3ZxFCXACddsix4p08\nCivcyKU+iy5duvDUU08RExNTaXIq6bMQwlr62GFo2RrlVfMxUEK4yqVisX//flq0aMHevXsrbZNi\nIYTF0g6h2l9hdQrRwLlULB577DF35xBCXABdcAayM6HNcKujiAbOpWJhmuY5t8m8TUJY6NhhAFTk\npZbGEA2fS8Vi3Lhx59y2fPnyWgsjhKgZnXbY8UWbS62MIRoBl4rF888/X+F1Tk4Oq1atok+fPm4J\nJYRw0bFD4OcPAbLmi3Avl+4hhYWFVfivY8eOTJ8+nQ8++MDd+YQQ56HTDkHkZTIOSrjdBXc4FBQU\ncPr06drMIoSoAW2Ww/GjKLkFJeqAS7ehnnvuuQq/uRQXF7N3714GDRrktmBCiGqcTIfSEumvEHXC\npWLx26nJmzRpwtChQ+nevbtbQgkhqqePHQJkmg9RN1wqFj179qRDhw6V2lNTU2V9bCGsknYIPDxk\nESNRJ1zqs3jyySerbP/b3/5Wq2GEEK7Txw5DeBuUrGAp6sB5ryx+HoyntXb+97OTJ0+6PGV5UlIS\nO3fuJCAggISEBACWLVvGV199haenJy1btiQuLo5mzZqRkZHBzJkziYiIAKBDhw5Mnjz5gj6cEPWF\nzs1Gf7kRNfQmlOHiUgDHDqM6dnFvMCF+ct5i8evBeGPHjq2wzTAMbrnlFpdOMnjwYK6//nqWLFni\nbOvevTt33HEHHh4evPnmm6xcuZK77roLcPSRLFiwwOUPIUR9pz95H712DSq0JfQeUP3+Z/MhJwsi\npb9C1I3zFovnn38erTWPP/44TzzxBFprlFIopfD398fb27VZLjt37kxGRkaFth49eji/7tixI1u3\nbr2A+ELUf9osR2//HwDmf1fh4UKxIE06t0XdOm+xCAtzrNublJQEOG5L5eXlERRUu6txrVu3jgED\nfvkBycjIYPbs2fj6+jJ27FiuuEJm1BQN2L5v4XQudOoO+75FH9yHatfpvIfon+aEksdmRV1x6Wmo\ns2fP8vLLL7N161Y8PT1ZtmwZO3bsIDU1tdLtqZr697//jYeHh3PMRlBQEElJSTRv3pwffviBBQsW\nkJCQgK+vb6Vjk5OTSU5OBiA+Pr7SWhs14enpeVHHu4tdc4F9s9k1F1SdLe/rLyn2bUbon+LJirsd\nr40fE9hvoHO71pr8pHhK9nyNV5eeeHftRfGRVEoDgghrV/kpxdrKZRd2zWbXXOCebC4Vi5deeolm\nzZqRlJTErFmzAMetozfeeOOiisWGDRv46quvmDt3rnPQn5eXF14/Pd0RFRVFy5YtSU9Pp127dpWO\nj42NJTY21vk6KyvrgrOEhoZe1PHuYtdcYN9sds0FlbPpkmLML9ajel9NdkkZDBpG8acrydz7HSrM\nMb7JXPshOnkNXNaR8i3rKEpe4zi4c89a+5z16XtmF3bNBTXL9vPDRNVxqVjs3r2bF198EU/PX3b3\n9/cnLy/PpZNU5euvv+aDDz7giSeeoEmTJs7206dP4+fnh2EYnDx5kvT0dFq2bHnB5xHCzvQ326Go\nENUvBgB17Uj0f1c5OrvH3odO3Yt+9xXocRVG3KOAhrTD6O/3oDp0tja8aFRcKha+vr7k5+dX6KvI\nyspyue9i8eLFpKSkkJ+fz9SpUxkzZgwrV66krKyMefPmAb88IpuSksKKFSvw8PDAMAzuu+8+/Pz8\nLuCjCWF/+ssNEBgMl3cFQAWFoPoOQn+ejB48HPPF+RAchnHPDNTPa8dc0g51SeUrbSHcyaViMWTI\nEBISEhg7dixaaw4cOMDbb7/N0KFDXTrJjBkzKrWdaznW/v37079/f5feV4j6TJ/Nh+92ooaMrDC2\nQg0dhd66AfOph6GsFGPO31G+8guTsJZLxeLmm2/G29ubV155hfLycl544QViY2MZPlyWchTCFTr9\nGOabSZzpHY2+eiiqiQ96x2YoL3PegvqZahvlfDJKTXzQ8VoIi1VbLEzTZMOGDQwdOlSKgxAXQKfu\nxXz+SSgr5eyB7+Cj91A3jUNvXe+Y1ymycjEwfn8/+tABjL4ys7Owh2rnhjIMgzfeeMP5hJIQwnX6\n6y8xF/0VmvlhPPYsQU+/CKEt0cuSIHUvql9MlQsXqdCWUiiErbh0G6p3797s2LFDllEVogbM/33m\nKAqXtse4/6+o5gF4h4ZiPDIfdm1Fb/8fapBr/X5CWM2lYlFaWsqiRYvo2LEjISEhFX4Tmj59utvC\nCVFf6dJS9FsvQqduGNP+jGri49ymlIJe0ahe0RYmFKJmXCoWkZGRREbKnPlCuOz4UceTTL+7rkKh\nEKK+cqlY3Hbbbe7OIUSDoo+kOr5oK+MhRMPg0uJHQogaOnoQmjaDsPDq9xWiHpBiIYQb6CMHoW1U\nlU86CVEfSbEQopbpsjLHKnaXyPr0ouGQYiFEbUtPg7JSkJHXogFxqYNba83atWvZvHkz+fn5LFy4\nkJSUFHJzcyssWiSEAH30IIBM9icaFJeuLJYvX8769euJjY11zpEeEhLCBx984NZwQtRLRw5Ck6bQ\nwrV1AoSoD1wqFhs3buSRRx7h6quvdnbYtWjRotK62kKIn64s2l72y5TiQjQALv1tNk0TH5+KA4uK\niooqtQnR2GmzHNJ+QMn4CtHAuFQsrrzySt544w1KS0sBRx/G8uXL6d27t1vDCVHvpP8IJSUgT0KJ\nBsalYjF+/HhycnKYMGECBQUFjB8/nszMTO6880535xOiXnF2bsuVhWhgXF5Wdfbs2eTm5pKVlUVo\naCiBgYHuziZE/XMkFby9Iby11UmEqFUuFQvTNAHw9/fH39/f2WZIB54QFeijByEyCuXhUf3OQtQj\nLhWLcePGVdnu4eFBUFAQ/fr1Y8yYMdLhLRo1bZpw9BBqwDVWRxGi1rlULCZOnMj27dsZNWoUISEh\nZGVlsXr1anr16kVERATvvvsur7/+OlOnTq3y+KSkJHbu3ElAQAAJCQkAnDlzhsTERDIzMwkLC2Pm\nzJn4+TkWpV+5ciXr1q3DMAwmTpxIz549a+njCuFGGcehuFBmmhUNkkv3kf7zn//w0EMP0a1bNyIi\nIujevTszZ87k448/pmfPnjz00EN89dVX5zx+8ODBPProoxXaVq1aRbdu3Xj22Wfp1q0bq1atAuDY\nsWNs2bKFRYsW8ec//5lXXnnFeRtMCDvTR34euS1PQomGx6ViUVBQQHFxcYW24uJiCgoKAAgMDKSk\npOScx3fu3Nl51fCz7du3ExMTA0BMTAzbt293tg8YMAAvLy9atGhBeHg4qamprn8iIaxy9CB4ekEr\nWShMNDwu3YaKiYnhySef5IYbbiA0NJRTp07x0UcfOf+x/+abb4iIqNnUBnl5eQQFBQGOYpOXlwdA\ndnY2HTp0cO4XHBxMdnZ2jd5bCCvoQwegzaUoT5d+rISoV1z6W33XXXcRHh7Oli1byMnJITAwkOuu\nu47Y2FgAunTpwhNPPHHBIZRSFzTvf3JyMsnJyQDEx8cTGhp6wRk8PT0v6nh3sWsusG82K3KVpR3i\n1PcpNBs7Cb/znFu+ZzVn12x2zQXuyeZSsTAMg2HDhjFs2LAqt3t7e9f4xAEBAeTk5BAUFEROTo7z\nkdzg4GBOnTrl3C87O5vg4OAq3yM2NtZZsADnJIcXIjQ09KKOdxe75gL7ZrMil7n8NfDypvCqwRSd\n59zyPas5u2azay6oWTZX7wq5PFAiNzeXHTt2sH79etatW+f870L16dOHjRs3Ao6JCvv27ets37Jl\nC6WlpWRkZJCenk779tJhKOxL52ajv9yAunoIqnmA1XGEcAuXriy2bdvGc889R6tWrUhLSyMyMpK0\ntDQ6derEtddeW+3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ps1Fde1V5vLrhNvSOzY6C8uBjMjeTEKJaUizqIf3pv9EbPkINvRla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\nKIgePXrwxhtvUFBQgGmanDhx4ry3lQo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -428,6 +419,139 @@ "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": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 26.1\n", + "Iteration 2: Average Return = 28.68\n", + "Iteration 3: Average Return = 31.64\n", + "Iteration 4: Average Return = 36.36\n", + "Iteration 5: Average Return = 38.44\n", + "Iteration 6: Average Return = 38.21\n", + "Iteration 7: Average Return = 43.47\n", + "Iteration 8: Average Return = 45.46\n", + "Iteration 9: Average Return = 50.92\n", + "Iteration 10: Average Return = 55.26\n", + "Iteration 11: Average Return = 56.64\n", + "Iteration 12: Average Return = 65.01\n", + "Iteration 13: Average Return = 67.85\n", + "Iteration 14: Average Return = 67.74\n", + "Iteration 15: Average Return = 71.5\n", + "Iteration 16: Average Return = 79.61\n", + "Iteration 17: Average Return = 76.91\n", + "Iteration 18: Average Return = 80.42\n", + "Iteration 19: Average Return = 106.04\n", + "Iteration 20: Average Return = 95.93\n", + "Iteration 21: Average Return = 93.24\n", + "Iteration 22: Average Return = 110.01\n", + "Iteration 23: Average Return = 105.6\n", + "Iteration 24: Average Return = 117.98\n", + "Iteration 25: Average Return = 118.29\n", + "Iteration 26: Average Return = 125.83\n", + "Iteration 27: Average Return = 123.25\n", + "Iteration 28: Average Return = 125.55\n", + "Iteration 29: Average Return = 139.84\n", + "Iteration 30: Average Return = 141.6\n", + "Iteration 31: Average Return = 141.26\n", + "Iteration 32: Average Return = 146.04\n", + "Iteration 33: Average Return = 152.26\n", + "Iteration 34: Average Return = 150.66\n", + "Iteration 35: Average Return = 150.46\n", + "Iteration 36: Average Return = 156.69\n", + "Iteration 37: Average Return = 164.12\n", + "Iteration 38: Average Return = 168.71\n", + "Iteration 39: Average Return = 154.21\n", + "Iteration 40: Average Return = 174.28\n", + "Iteration 41: Average Return = 165.04\n", + "Iteration 42: Average Return = 171.54\n", + "Iteration 43: Average Return = 169.25\n", + "Iteration 44: Average Return = 178.1\n", + "Iteration 45: Average Return = 173.15\n", + "Iteration 46: Average Return = 175.99\n", + "Iteration 47: Average Return = 180.91\n", + "Iteration 48: Average Return = 179.32\n", + "Iteration 49: Average Return = 189.3\n", + "Iteration 50: Average Return = 179.85\n", + "Iteration 51: Average Return = 180.3\n", + "Iteration 52: Average Return = 188.91\n", + "Iteration 53: Average Return = 188.98\n", + "Iteration 54: Average Return = 187.98\n", + "Iteration 55: Average Return = 186.22\n", + "Iteration 56: Average Return = 185.88\n", + "Iteration 57: Average Return = 189.3\n", + "Iteration 58: Average Return = 191.71\n", + "Iteration 59: Average Return = 192.4\n", + "Iteration 60: Average Return = 188.38\n", + "Iteration 61: Average Return = 191.95\n", + "Iteration 62: Average Return = 190.09\n", + "Iteration 63: Average Return = 189.46\n", + "Iteration 64: Average Return = 189.36\n", + "Iteration 65: Average Return = 191.88\n", + "Iteration 66: Average Return = 188.95\n", + "Iteration 67: Average Return = 192.17\n", + "Iteration 68: Average Return = 192.79\n", + "Iteration 69: Average Return = 193.23\n", + "Iteration 70: Average Return = 193.66\n", + "Iteration 71: Average Return = 191.78\n", + "Iteration 72: Average Return = 194.28\n", + "Iteration 73: Average Return = 195.77\n", + "Solve at 73 iterations, which equals 7300 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": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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NrqTRoZ7ppTJPirNIod4ZhH7+7yBbrgJZfXFpJ5NqdWYy5uqIf5dcvVZdam50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\n9SUfz8wUPz3S7XbnXS+0tIMefQPTk5Ogp1k8flZ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PVFQU/v7+mKbp6GzCDenCAkr/MRV98Afb9j92BADV9EaUrx/G9L9BaSmcTJJb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\n4o9FCCGsXP6v0vz58wkODiY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rrWW2Dxs2jP/85z9MmDCBDz/8sNzxf/rTn0hPT+eRRx7h\nhRdeYOTIkTY9k1FcXMzbb7/NpEmTePjhh8nLy6twGU4h7EGGzgohhKiUXFkIIYSolBQLIYQQlZJi\nIYQQolJSLIQQQlRKioUQQohKSbEQQghRKSkWQgghKiXFQgghRKWkWAghhKjU/wc244ql+yXEEAAA\nAABJRU5ErkJggg==\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": {}, @@ -453,6 +577,225 @@ "If you answer is right, your will solve CartPole with roughly ~ 80 iterations." ] }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# set the hyperparameter for generalized advantage estimation (GAE)\n", + "LAMBDA = 0.98 # \\lambda\n", + "class PolicyOptimizer_actor_critic(PolicyOptimizer):\n", + " def __init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate=.99):\n", + " PolicyOptimizer.__init__(self, env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate=.99)\n", + " \n", + " def process_paths(self, paths):\n", + " for p in paths:\n", + " if self.baseline != None:\n", + " b = self.baseline.predict(p)\n", + " b[-1] = 0 # terminal state\n", + " else:\n", + " b = 0\n", + " \n", + " \"\"\"\n", + " 1. Variable `b` is the reward predicted by our baseline\n", + " 2. Calculate the advantage function via one-step bootstrap\n", + " A(s, a) = [r(s,a,s') + \\gamma*v(s')] - v(s)\n", + " 3. `target_v` specifies the target of the baseline function\n", + " \"\"\"\n", + " r = util.discount_bootstrap(p[\"rewards\"], self.discount_rate, b)\n", + " target_v = util.discount_cumsum(p[\"rewards\"], self.discount_rate)\n", + " a = r - b\n", + " \n", + " p[\"returns\"] = target_v\n", + " p[\"baselines\"] = b\n", + " p[\"advantages\"] = (a - a.mean()) / (a.std() + 1e-8) # normalize\n", + "\n", + " obs = np.concatenate([ p[\"observations\"] for p in paths ])\n", + " actions = np.concatenate([ p[\"actions\"] for p in paths ])\n", + " rewards = np.concatenate([ p[\"rewards\"] for p in paths ])\n", + " advantages = np.concatenate([ p[\"advantages\"] for p in paths ])\n", + "\n", + " return dict(\n", + " observations=obs,\n", + " actions=actions,\n", + " rewards=rewards,\n", + " advantages=advantages,\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Iteration 1: Average Return = 19.26\n", + "Iteration 2: Average Return = 19.0\n", + "Iteration 3: Average Return = 20.61\n", + "Iteration 4: Average Return = 21.16\n", + "Iteration 5: Average Return = 23.18\n", + "Iteration 6: Average Return = 26.47\n", + "Iteration 7: Average Return = 25.54\n", + "Iteration 8: Average Return = 27.3\n", + "Iteration 9: Average Return = 28.79\n", + "Iteration 10: Average Return = 31.26\n", + "Iteration 11: Average Return = 35.2\n", + "Iteration 12: Average Return = 34.03\n", + "Iteration 13: Average Return = 34.78\n", + "Iteration 14: Average Return = 36.76\n", + "Iteration 15: Average Return = 36.06\n", + "Iteration 16: Average Return = 39.78\n", + "Iteration 17: Average Return = 39.89\n", + "Iteration 18: Average Return = 46.0\n", + "Iteration 19: Average Return = 40.1\n", + "Iteration 20: Average Return = 42.5\n", + "Iteration 21: Average Return = 44.58\n", + "Iteration 22: Average Return = 46.97\n", + "Iteration 23: Average Return = 43.54\n", + "Iteration 24: Average Return = 46.47\n", + "Iteration 25: Average Return = 47.68\n", + "Iteration 26: Average Return = 50.25\n", + "Iteration 27: Average Return = 49.23\n", + "Iteration 28: Average Return = 48.78\n", + "Iteration 29: Average Return = 47.92\n", + "Iteration 30: Average Return = 49.72\n", + "Iteration 31: Average Return = 49.3\n", + "Iteration 32: Average Return = 51.64\n", + "Iteration 33: Average Return = 49.14\n", + "Iteration 34: Average Return = 52.92\n", + "Iteration 35: Average Return = 51.14\n", + "Iteration 36: Average Return = 54.7\n", + "Iteration 37: Average Return = 57.42\n", + "Iteration 38: Average Return = 50.94\n", + "Iteration 39: Average Return = 52.51\n", + "Iteration 40: Average Return = 54.94\n", + "Iteration 41: Average Return = 58.1\n", + "Iteration 42: Average Return = 51.95\n", + "Iteration 43: Average Return = 58.8\n", + "Iteration 44: Average Return = 57.96\n", + "Iteration 45: Average Return = 57.5\n", + "Iteration 46: Average Return = 58.54\n", + "Iteration 47: Average Return = 54.82\n", + "Iteration 48: Average Return = 54.24\n", + "Iteration 49: Average Return = 59.35\n", + "Iteration 50: Average Return = 58.65\n", + "Iteration 51: Average Return = 59.99\n", + "Iteration 52: Average Return = 61.51\n", + "Iteration 53: Average Return = 58.73\n", + "Iteration 54: Average Return = 62.7\n", + "Iteration 55: Average Return = 57.76\n", + "Iteration 56: Average Return = 64.79\n", + "Iteration 57: Average Return = 60.5\n", + "Iteration 58: Average Return = 64.41\n", + "Iteration 59: Average Return = 68.62\n", + "Iteration 60: Average Return = 63.43\n", + "Iteration 61: Average Return = 63.57\n", + "Iteration 62: Average Return = 69.89\n", + "Iteration 63: Average Return = 67.95\n", + "Iteration 64: Average Return = 68.19\n", + "Iteration 65: Average Return = 69.92\n", + "Iteration 66: Average Return = 67.62\n", + "Iteration 67: Average Return = 72.27\n", + "Iteration 68: Average Return = 74.44\n", + "Iteration 69: Average Return = 76.3\n", + "Iteration 70: Average Return = 77.04\n", + "Iteration 71: Average Return = 70.98\n", + "Iteration 72: Average Return = 74.9\n", + "Iteration 73: Average Return = 74.99\n", + "Iteration 74: Average Return = 79.64\n", + "Iteration 75: Average Return = 84.0\n", + "Iteration 76: Average Return = 84.78\n", + "Iteration 77: Average Return = 80.9\n", + "Iteration 78: Average Return = 87.47\n", + "Iteration 79: Average Return = 92.22\n", + "Iteration 80: Average Return = 100.7\n", + "Iteration 81: Average Return = 104.11\n", + "Iteration 82: Average Return = 107.5\n", + "Iteration 83: Average Return = 112.73\n", + "Iteration 84: Average Return = 118.82\n", + "Iteration 85: Average Return = 132.46\n", + "Iteration 86: Average Return = 133.6\n", + "Iteration 87: Average Return = 148.75\n", + "Iteration 88: Average Return = 149.98\n", + "Iteration 89: Average Return = 154.15\n", + "Iteration 90: Average Return = 157.76\n", + "Iteration 91: Average Return = 154.33\n", + "Iteration 92: Average Return = 154.39\n", + "Iteration 93: Average Return = 157.81\n", + "Iteration 94: Average Return = 156.77\n", + "Iteration 95: Average Return = 159.61\n", + "Iteration 96: Average Return = 157.34\n", + "Iteration 97: Average Return = 168.61\n", + "Iteration 98: Average Return = 161.92\n", + "Iteration 99: Average Return = 172.29\n", + "Iteration 100: Average Return = 174.62\n", + "Iteration 101: Average Return = 173.58\n", + "Iteration 102: Average Return = 178.55\n", + "Iteration 103: Average Return = 180.57\n", + "Iteration 104: Average Return = 189.68\n", + "Iteration 105: Average Return = 188.96\n", + "Iteration 106: Average Return = 191.17\n", + "Iteration 107: Average Return = 193.55\n", + "Iteration 108: Average Return = 195.91\n", + "Solve at 108 iterations, which equals 10800 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 = LinearFeatureBaseline(env.spec) \n", + "\n", + "po = PolicyOptimizer(env, policy, baseline, n_iter, n_episode, path_length,\n", + " discount_rate)\n", + "\n", + "# Train the policy optimizer\n", + "loss_list, avg_return_list = po.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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rgyZt3nzzzbj55pvjOseYoMsTGcqDg2DK08V4Qy77wsqEcGEaDWAweWpfNcvC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\n6E94jAXiqf9CE3CbZzMfHBRPHormkp0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/tRKO7IdW7W3HaQ2Hk+CsFV1YYNvprlsEqmVbJ2cgaorDy32cl5OTQ3Z2Nrm5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\noUOaQnAoetMHmHln0W+vhOs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IioUQQogKSbEQQghRISkWQgghKiTFQgghRIWkWAghhKiQ\nFAshhBAV+n9u4m85J1dyrAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "util.plot_curve(loss_list, \"loss\")\n", + "util.plot_curve(avg_return_list, \"average return\")" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -477,7 +820,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 14, "metadata": { "collapsed": true }, @@ -523,6 +866,7 @@ " Sample solution should be only 1 line. (you can use `util.discount` in policy_gradient/util.py)\n", " \"\"\"\n", " # YOUR CODE HERE >>>>>>>>\n", + " a = util.discount(a, self.discount_rate * LAMBDA)\n", " # <<<<<<<\n", " p[\"returns\"] = target_v\n", " p[\"baselines\"] = b\n", @@ -543,7 +887,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "metadata": { "scrolled": true }, @@ -552,90 +896,184 @@ "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 = 18.82\n", + "Iteration 2: Average Return = 20.46\n", + "Iteration 3: Average Return = 20.94\n", + "Iteration 4: Average Return = 20.68\n", + "Iteration 5: Average Return = 24.44\n", + "Iteration 6: Average Return = 24.7\n", + "Iteration 7: Average Return = 26.52\n", + "Iteration 8: Average Return = 26.62\n", + "Iteration 9: Average Return = 30.28\n", + "Iteration 10: Average Return = 32.26\n", + "Iteration 11: Average Return = 34.24\n", + "Iteration 12: Average Return = 33.56\n", + "Iteration 13: Average Return = 36.47\n", + "Iteration 14: Average Return = 39.82\n", + "Iteration 15: Average Return = 38.08\n", + "Iteration 16: Average Return = 40.75\n", + "Iteration 17: Average Return = 45.29\n", + "Iteration 18: Average Return = 46.37\n", + "Iteration 19: Average Return = 46.97\n", + "Iteration 20: Average Return = 48.76\n", + "Iteration 21: Average Return = 48.9\n", + "Iteration 22: Average Return = 48.46\n", + "Iteration 23: Average Return = 47.52\n", + "Iteration 24: Average Return = 48.14\n", + "Iteration 25: Average Return = 52.07\n", + "Iteration 26: Average Return = 50.37\n", + "Iteration 27: Average Return = 51.86\n", + "Iteration 28: Average Return = 52.77\n", + "Iteration 29: Average Return = 54.83\n", + "Iteration 30: Average Return = 55.18\n", + "Iteration 31: Average Return = 56.71\n", + "Iteration 32: Average Return = 60.94\n", + "Iteration 33: Average Return = 60.89\n", + "Iteration 34: Average Return = 59.71\n", + "Iteration 35: Average Return = 68.57\n", + "Iteration 36: Average Return = 65.16\n", + "Iteration 37: Average Return = 70.96\n", + "Iteration 38: Average Return = 69.16\n", + "Iteration 39: Average Return = 70.81\n", + "Iteration 40: Average Return = 79.71\n", + "Iteration 41: Average Return = 84.33\n", + "Iteration 42: Average Return = 90.04\n", + "Iteration 43: Average Return = 84.57\n", + "Iteration 44: Average Return = 83.18\n", + "Iteration 45: Average Return = 102.23\n", + "Iteration 46: Average Return = 98.87\n", + "Iteration 47: Average Return = 102.66\n", + "Iteration 48: Average Return = 107.39\n", + "Iteration 49: Average Return = 105.16\n", + "Iteration 50: Average Return = 111.83\n", + "Iteration 51: Average Return = 108.32\n", + "Iteration 52: Average Return = 114.33\n", + "Iteration 53: Average Return = 115.77\n", + "Iteration 54: Average Return = 119.42\n", + "Iteration 55: Average Return = 116.63\n", + "Iteration 56: Average Return = 122.64\n", + "Iteration 57: Average Return = 123.8\n", + "Iteration 58: Average Return = 132.13\n", + "Iteration 59: Average Return = 132.66\n", + "Iteration 60: Average Return = 136.09\n", + "Iteration 61: Average Return = 138.67\n", + "Iteration 62: Average Return = 139.61\n", + "Iteration 63: Average Return = 142.17\n", + "Iteration 64: Average Return = 142.72\n", + "Iteration 65: Average Return = 147.83\n", + "Iteration 66: Average Return = 154.65\n", + "Iteration 67: Average Return = 160.01\n", + "Iteration 68: Average Return = 161.56\n", + "Iteration 69: Average Return = 171.01\n", + "Iteration 70: Average Return = 167.06\n", + "Iteration 71: Average Return = 168.64\n", + "Iteration 72: Average Return = 175.58\n", + "Iteration 73: Average Return = 175.31\n", + "Iteration 74: Average Return = 178.66\n", + "Iteration 75: Average Return = 173.94\n", + "Iteration 76: Average Return = 172.89\n", + "Iteration 77: Average Return = 179.22\n", + "Iteration 78: Average Return = 176.39\n", + "Iteration 79: Average Return = 174.61\n", + "Iteration 80: Average Return = 180.04\n", + "Iteration 81: Average Return = 171.26\n", + "Iteration 82: Average Return = 183.48\n", + "Iteration 83: Average Return = 174.01\n", + "Iteration 84: Average Return = 178.26\n", + "Iteration 85: Average Return = 182.07\n", + "Iteration 86: Average Return = 180.75\n", + "Iteration 87: Average Return = 187.27\n", + "Iteration 88: Average Return = 177.33\n", + "Iteration 89: Average Return = 189.05\n", + "Iteration 90: Average Return = 184.37\n", + "Iteration 91: Average Return = 178.13\n", + "Iteration 92: Average Return = 186.29\n", + "Iteration 93: Average Return = 180.3\n", + "Iteration 94: Average Return = 187.12\n", + "Iteration 95: Average Return = 190.54\n", + "Iteration 96: Average Return = 186.13\n", + "Iteration 97: Average Return = 184.18\n", + "Iteration 98: Average Return = 185.83\n", + "Iteration 99: Average Return = 188.27\n", + "Iteration 100: Average Return = 188.96\n", + "Iteration 101: Average Return = 186.21\n", + "Iteration 102: Average Return = 191.24\n", + "Iteration 103: Average Return = 191.43\n", + "Iteration 104: Average Return = 191.1\n", + "Iteration 105: Average Return = 189.13\n", + "Iteration 106: Average Return = 190.17\n", + "Iteration 107: Average Return = 189.91\n", + "Iteration 108: Average Return = 190.11\n", + "Iteration 109: Average Return = 192.92\n", + "Iteration 110: Average Return = 187.13\n", + "Iteration 111: Average Return = 192.68\n", + "Iteration 112: Average Return = 194.33\n", + "Iteration 113: Average Return = 189.54\n", + "Iteration 114: Average Return = 192.39\n", + "Iteration 115: Average Return = 193.98\n", + "Iteration 116: Average Return = 187.77\n", + "Iteration 117: Average Return = 192.66\n", + "Iteration 118: Average Return = 192.91\n", + "Iteration 119: Average Return = 192.93\n", + "Iteration 120: Average Return = 193.57\n", + "Iteration 121: Average Return = 189.61\n", + "Iteration 122: Average Return = 192.21\n", + "Iteration 123: Average Return = 189.11\n", + "Iteration 124: Average Return = 191.71\n", + "Iteration 125: Average Return = 190.65\n", + "Iteration 126: Average Return = 190.37\n", + "Iteration 127: Average Return = 187.43\n", + "Iteration 128: Average Return = 185.5\n", + "Iteration 129: Average Return = 189.3\n", + "Iteration 130: Average Return = 190.72\n", + "Iteration 131: Average Return = 190.52\n", + "Iteration 132: Average Return = 191.24\n", + "Iteration 133: Average Return = 187.7\n", + "Iteration 134: Average Return = 188.06\n", + "Iteration 135: Average Return = 187.14\n", + "Iteration 136: Average Return = 191.17\n", + "Iteration 137: Average Return = 190.14\n", + "Iteration 138: Average Return = 190.59\n", + "Iteration 139: Average Return = 185.24\n", + "Iteration 140: Average Return = 190.52\n", + "Iteration 141: Average Return = 190.59\n", + "Iteration 142: Average Return = 188.41\n", + "Iteration 143: Average Return = 189.54\n", + "Iteration 144: Average Return = 190.87\n", + "Iteration 145: Average Return = 191.51\n", + "Iteration 146: Average Return = 188.65\n", + "Iteration 147: Average Return = 187.7\n", + "Iteration 148: Average Return = 192.18\n", + "Iteration 149: Average Return = 188.55\n", + "Iteration 150: Average Return = 191.15\n", + "Iteration 151: Average Return = 188.9\n", + "Iteration 152: Average Return = 188.65\n", + "Iteration 153: Average Return = 189.25\n", + "Iteration 154: Average Return = 188.08\n", + "Iteration 155: Average Return = 181.44\n", + "Iteration 156: Average Return = 190.69\n", + "Iteration 157: Average Return = 188.88\n", + "Iteration 158: Average Return = 189.24\n", + "Iteration 159: Average Return = 189.28\n", + "Iteration 160: Average Return = 190.74\n", + "Iteration 161: Average Return = 188.79\n", + "Iteration 162: Average Return = 187.41\n", + "Iteration 163: Average Return = 191.7\n", + "Iteration 164: Average Return = 188.06\n", + "Iteration 165: Average Return = 187.52\n", + "Iteration 166: Average Return = 184.26\n", + "Iteration 167: Average Return = 185.29\n", + "Iteration 168: Average Return = 187.25\n", + "Iteration 169: Average Return = 189.05\n", + "Iteration 170: Average Return = 186.43\n", + "Iteration 171: Average Return = 189.73\n", + "Iteration 172: Average Return = 187.74\n", + "Iteration 173: Average Return = 187.6\n", + "Iteration 174: Average Return = 192.48\n", + "Iteration 175: Average Return = 192.57\n", + "Iteration 176: Average Return = 189.65\n", + "Iteration 177: Average Return = 195.5\n", + "Solve at 177 iterations, which equals 17700 episodes.\n" ] } ], @@ -658,14 +1096,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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GAw880Oh4QkICCQkJrsdTp05l6tSGXwzR0dG8/fbbnR5ja1RAgDF3QpKL99mP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\n", 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EdtgTxgCNPlSOC5WZbZLaf/0fc7wPz0VNvdhM1q05AdXHUNf9jQlzHXwfdcU1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\nOCkuQgghIk6KixBCiIj7H4rG3zqDrIK9AAAAAElFTkSuQmCC\n", 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DUS1DoFsfq0sWos7USVgsWrSI/fv3k5eXx8yZM5k0aRIjRoy4oAsKYP/+/axZswY3Nzds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\nyo892QAAAABJRU5ErkJggg==\n", 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7u1/V9jWpvmUt2ZdMlmni37ELXjZ+rvp2XGuTupRXslayr0u9GBZmTTSzaNEi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\nZr7buRnzhcfh8D5r8EAfuep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z;NpT@X9gw(I@tsnm3|m}5Zv6{2gAs-7H|hatRA;(qy2gD8vAV{XMD0OuD9xNkh|U| z2akel-fu#Aad%nLcM<@0wwhDXRa-$4&|0E2QT9(fYf)FhGSxh*H?vJp99jWxAef?V*! zqML#VaRDM>Qy}@6gK4maLZ*Y>;|s`|1f5r2LE(MgO`R0T4D+o!((Zr{jjmNV6u?6! z05$r7LC(g5Ca=gYvsu{uU+?1i2LvpS*YH4@HJf|_=dxy_c2ziSoRgjX*#d;KQMFE+8D_zNknmn3?RS{LIgEq zXbBrY|9G#RJ?6#H|aUVu&1hBRT~QG8jiMxmF#`@RhWl>*dt%F(3;xB+OJ zcRGk%`Pi``y(&HB7%hAm8ymw2{D$1Xb-JSMTpPY6U_XZqH*JH(;@!J|+7(XdxJY+yMQ_d??2EuI6;1xOkJxtgK-6CN6x0T1%kpKNykFUBk4h1wUMEgghX z!N$jDx_9sG_O|_`*Zze2rmU2d?bg&+%$}&o$eZBY4;%WVLK7`@*)$M6oFgu-UHMqv z+6XvQWrKRvzkKu7t?wf3oKgxgNV_a0p%GoEU>>>>> # <<<<<<<< + layer1 = tf.contrib.layers.fully_connected(self._observations, hidden_dim, activation_fn=tf.tanh) + layer2 = tf.contrib.layers.fully_connected(layer1, out_dim, activation_fn=tf.nn.softmax) + probs = layer2 # -------------------------------------------------- # This operation (variable) is used when choosing action during data sampling phase @@ -72,6 +75,7 @@ def __init__(self, in_dim, out_dim, hidden_dim, optimizer, session): Sample solution is about 1~3 lines. """ # YOUR CODE HERE >>>>>> + surr_loss = tf.reduce_mean(-log_prob * self._advantages) # <<<<<<<< grads_and_vars = self._opt.compute_gradients(surr_loss) diff --git a/policy_gradient/util.py b/policy_gradient/util.py index 61ef302..3560a48 100644 --- a/policy_gradient/util.py +++ b/policy_gradient/util.py @@ -32,6 +32,8 @@ def discount_bootstrap(x, discount_rate, b): Sample code should be about 3 lines """ # YOUR CODE >>>>>>>>>>>>>>>>>>> + b = np.append(b[1:], 0) + return x + discount_rate * b # <<<<<<<<<<<<<<<<<<<<<<<<<<<< def plot_curve(data, key, filename=None): From 447cb2fd904c6dd12ffc9b4c04dd273f18f36752 Mon Sep 17 00:00:00 2001 From: Anjie Zheng Date: Thu, 9 Nov 2017 22:17:11 +0800 Subject: [PATCH 2/2] Update report --- report.md | 49 ++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 48 insertions(+), 1 deletion(-) diff --git a/report.md b/report.md index 1e5017e..db3c8b6 100644 --- a/report.md +++ b/report.md @@ -1,3 +1,50 @@ # Homework3-Policy-Gradient report +## Problem 1 +Following instructions: Use `tanh` as the activation function of the first hidden layer, and append softmax layer after the output of the neural network to get the probability of each possible action. +```python +layer1 = tf.contrib.layers.fully_connected(self._observations, hidden_dim, activation_fn=tf.tanh) +layer2 = tf.contrib.layers.fully_connected(layer1, out_dim, activation_fn=tf.nn.softmax) +probs = layer2 +``` +## Problem 2 +Compute surrogate loss and assign it to variable `surr_loss`. Since we need to maximize it not minimize it, so we turn it into negative. +```python +surr_loss = tf.reduce_mean(-log_prob * self._advantages) +``` + +## Problem 3 +Simply subtract the variance and then assign the result to the variable `a`. +```python + a = r - b +``` +![](https://i.imgur.com/78ARcKp.png) +![](https://i.imgur.com/TfxMgpZ.png) + +## Problem 4 +Curve without baseline substraction. + +![](https://i.imgur.com/hAIowgF.png) +![](https://i.imgur.com/jxAnLjv.png) + +## Problem 5 +Following `y = r(s_t,a,s_{t+1}) + \gamma*V_t` +```python +b = np.append(b[1:], 0) +y = x + discount_rate * b +``` + +![](https://i.imgur.com/fonXELu.png) +![](https://i.imgur.com/20uIuUT.png) + + +## Problem 6 +Generalized Advantage Estimation +```python +a = util.discount(a, self.discount_rate * LAMBDA) +``` + +![](https://i.imgur.com/HnAgfXD.png) +![](https://i.imgur.com/8vN4nNC.png) + + -TA: try to elaborate the algorithms that you implemented and any details worth mentioned.