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December 26, 2016 13:21
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{ | |
"cells": [ | |
{ | |
"cell_type": "code", | |
"execution_count": 1, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"import random" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 2, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"A = {\n", | |
" 's1': {'a1', 'a2'},\n", | |
" 's2': {'a1', 'a2'},\n", | |
" 's3': {'a1', 'a2'},\n", | |
" 's4': {'a1', 'a2'},\n", | |
"}\n", | |
"\n", | |
"R = {\n", | |
" 's1': {\n", | |
" 'a1': ('s3', 0),\n", | |
" 'a2': ('s2', 1),\n", | |
" },\n", | |
" 's2': {\n", | |
" 'a1': ('s1', -1),\n", | |
" 'a2': ('s4', 1),\n", | |
" },\n", | |
" 's3': {\n", | |
" 'a1': ('s4', 5),\n", | |
" 'a2': ('s1', -100),\n", | |
" },\n", | |
" 's4': {\n", | |
" 'a1': ('s4', 0),\n", | |
" 'a2': ('s4', 0),\n", | |
" }\n", | |
"}" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 3, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"class Environment(object):\n", | |
" def __init__(self):\n", | |
" self.state = 's1'\n", | |
" \n", | |
" def pull(self, action):\n", | |
" next_state, reward = R[self.state][action]\n", | |
" self.state = next_state\n", | |
" return next_state, reward\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 4, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"class Agent(object):\n", | |
" def __init__(self, epsilon=0.01):\n", | |
" self.alpha = 0.1\n", | |
" self.gamma = 0.9\n", | |
" self.q = dict((s, dict((a, 0) for a in A[s])) for s in A.keys())\n", | |
" self.epsilon = epsilon\n", | |
"\n", | |
" def step(self, Environment):\n", | |
" state = Environment.state\n", | |
" action = self.get_action(state)\n", | |
" next_state, reward = Environment.pull(action)\n", | |
" self.learn(state, action, reward, next_state)\n", | |
" return next_state == 's4', reward" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 5, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [], | |
"source": [ | |
"class SARSA(Agent):\n", | |
" \n", | |
" def get_action(self, state):\n", | |
" if random.random() < self.epsilon:\n", | |
" return random.choice(list(A[state]))\n", | |
" else:\n", | |
" return sorted(self.q[state].items(), key=lambda x: x[1], reverse=True)[0][0]\n", | |
" \n", | |
" def learn(self, state, action, reward, next_state):\n", | |
" next_action = self.get_action(next_state)\n", | |
" q = ((1 - self.alpha) * self.q[state][action] +\n", | |
" self.alpha * (reward + self.gamma * self.q[next_state][next_action]))\n", | |
" self.q[state][action] = q" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 6, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"class QLearn(Agent):\n", | |
" \n", | |
" def get_action(self, state):\n", | |
" if random.random() < self.epsilon:\n", | |
" return random.choice(list(A[state]))\n", | |
" else:\n", | |
" return sorted(self.q[state].items(), key=lambda x: x[1], reverse=True)[0][0]\n", | |
" \n", | |
" def learn(self, state, action, reward, next_state):\n", | |
" q = ((1 - self.alpha) * self.q[state][action] +\n", | |
" self.alpha * (reward + self.gamma * max(self.q[next_state].values())))\n", | |
" self.q[state][action] = q" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 15, | |
"metadata": { | |
"collapsed": false, | |
"scrolled": true | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"---SIM 0---\n", | |
"---SIM 10---\n", | |
"---SIM 20---\n", | |
"---SIM 30---\n", | |
"---SIM 40---\n", | |
"---SIM 50---\n", | |
"---SIM 60---\n", | |
"---SIM 70---\n", | |
"---SIM 80---\n", | |
"---SIM 90---\n", | |
"---SIM 100---\n", | |
"---SIM 110---\n", | |
"---SIM 120---\n", | |
"---SIM 130---\n", | |
"---SIM 140---\n", | |
"---SIM 150---\n", | |
"---SIM 160---\n", | |
"---SIM 170---\n", | |
"---SIM 180---\n", | |
"---SIM 190---\n", | |
"---SIM 200---\n", | |
"---SIM 210---\n", | |
"---SIM 220---\n", | |
"---SIM 230---\n", | |
"---SIM 240---\n", | |
"---SIM 250---\n", | |
"---SIM 260---\n", | |
"---SIM 270---\n", | |
"---SIM 280---\n", | |
"---SIM 290---\n", | |
"---SIM 300---\n", | |
"---SIM 310---\n", | |
"---SIM 320---\n", | |
"---SIM 330---\n", | |
"---SIM 340---\n", | |
"---SIM 350---\n", | |
"---SIM 360---\n", | |
"---SIM 370---\n", | |
"---SIM 380---\n", | |
"---SIM 390---\n", | |
"---SIM 400---\n", | |
"---SIM 410---\n", | |
"---SIM 420---\n", | |
"---SIM 430---\n", | |
"---SIM 440---\n", | |
"---SIM 450---\n", | |
"---SIM 460---\n", | |
"---SIM 470---\n", | |
"---SIM 480---\n", | |
"---SIM 490---\n" | |
] | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"n_sim = 500\n", | |
"n_episodes = 3000\n", | |
"data = np.zeros(shape=(2, n_sim, n_episodes))\n", | |
"for sim in range(n_sim):\n", | |
" if sim%10==0: print '---SIM %d---' % (sim)\n", | |
" sarsa = SARSA(epsilon=0.01)\n", | |
" qlearn = QLearn(epsilon=0.01)\n", | |
" for i in range(n_episodes):\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = sarsa.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[0][sim][i] = revenue\n", | |
"\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = qlearn.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[1][sim][i] = revenue\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 16, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
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NioQdCm2kLBJGx+A9CHLf3JjpgNX2yWmLRIgfYsHS+5XJ4D3zKc/g+xasnuhaJLyGNowq\nZmbRKvR612OnxSgUQpr+6TiKrwnbHx6KhOYBX8wReeeFNb1JazpUcxbWLBIAcTpiKAqu5b282LNb\ngaK+4W5J9F7OWLBIeL9sAmHoRVAUxacBF0IYbtQ9n2V4hjb0jo1gpINwn/O5J14+FD5f6zrKl0/e\nIdyfcA5teF+rGm6iQTcqr7aORvodcqPWaYP3cvfu3Rz94yjkupBs+3UbZU6UUX9r2b91Pzn7cuAi\nHC56WI3zT+o/rFmzxn8hufFOcEI9zonPcaXx47py6twpNc9dm3b5jedGm5fPMbBn856Aebjjndx1\nEva7wk6fO+1zXRf3XgSv5uWfwv9w9u+zcMqVz5EdR9SyDhU95JmHl1wAp3adgoOwevVq15ReLznX\nrV0H+1z3b+/5vbAPdvy6gzWnXHmULFmSihUreqSJGR+JsKMd2lCUgJqZB9qhDY8KEOLQBsJTd/Cx\nQNhvkcg+56lITJ0Kd94J3AZU1U9jyCJhcydqNj/LQxvoDG0Y6BTV9BqLhFNWGX9DG2a/GMyYrLVe\n41oZQvGRCOX+hNMi4ZbTjEXCu65ZaYQjbdXzxog8u3fvpnbt2pw+ned4OWLyCL/xpzJVPZ5LntPz\n/Nx/wVhFntPtOc6R+br/GS3b2U7ms4FnvGjJnJwZ9NhoerMyfEOeQ793PnNz/xkpr+nkprr5XzX5\nKgBW5/4DGDk5z9kuJSWFTZs2RY2PRHQrEl4mbb1jNWaQoQ2XRcLajfY0s+vLZwY9i0SA0j1+vfuu\n51m9xtLI17mRRsdMQ2nE+1iLnc6WofpIeKNnLTGDXRYJf/J4nPM3tOEls/4741xHaPRabfGRQF9p\nMpO3lWcTNRYJEwrnoUOHOH36tFzsL4ZxLzZ16NChSIuiEt2KhGL8C8Nfm2G/s2UAHwmDGPkwDdoI\nBih7++/2DG04Zfr3l7eRxlxRjPlI+E1vwMM9FBO5Wx47MGOy1hvuCUYwZcrK2LEbo1/4hoekArw0\nVnwkvM+Fc9aGEcxYr9yyezvc+uSpeb9q165N48aGNnGWRDGR9IvQEt3OlorxL4xgikRIFgltByfy\nQp20SKgNnQWZGzW2R0mwo6H0pxxY/RJVUHxnKJiYyqj9evN3D0KeFqm1pNkxtGHAR8LbIuHPN0Ir\no5MdoWFFIkjnZwS/ioQTPhKa+hw1FgnNlGZJwUJbH5388AtGdCsSWPfCVtN4JAndR0JtnIRiaB0J\nPYz0J3mNgmfkvOsJMAZuQC4jjY6ZRj5UHwkFJaR1JIzKaqTRdWp9BSeHEvwtYGTG8mImbTDstkgE\nwoqPhDdWvuwctUiYaKsCLUglKThE0mcnuhUJxfiXncc91HzF51y0cfqn8GqaLA5tGLJICGMWCd3G\n0iZFwhaLhAkfCQ8nxQDTN0OZ/ulvPF2LvxkQRvGermo1HyNDG8F8JAKldXJBKqMds1EF0IifiBmL\nhC3OlhH8AtQiLRIFF3/T5MNNzCgSwcjx8w7l2GCR8DCHe2osFvMLHsdoo6AbT7HH2dLJhklvOqIR\ndH0kTDhb+lu8SEuoPhIezpYhTBe0Y2XLQITiAxGMcFok3FitU2BxHQkbhmX8YclHwsZ7KZGYIcoV\nCRsWq/HwkbDzcpU8RScnN1+Dio8pH4kgyo/VoY2tWyOsSOjIbaThV9BZR8LEeL/269XIinVW8GdZ\nMdvxGLJIBPORiPLpn4aHpAx0rOHwkQhUXqSQQxsFFw+Lpxza8IOmQww0jc61xoR+Fh7vVojOlj4P\nyi2fqqDodeo6sxPMWCT8yawE+LI2oEicyzbgI2HiC8fIsIQWq42e0emfRsoPh7NlKPkacrb0M7Th\nXaaeTNGwIJXZhcQCEcqsDaM+CdFiStYihzYkIIc2DGHKR0LDRa1FwhZnS8+QXIly/9MTwjfMlI+E\nQxYJI9YTMw2T32Wh/chvNW9/0z+9O6Rgs0VMTf8MwZE0lI7H1PRPC2b2gCvFhtgohXPWhhszFgnv\ncwXBIhEtUwUlziAtEn4xdmNcO7H5OZcTurOl7guou4eHAxaJIFi1SAS6t926wcCB1htKqxvR+FsR\nUkuoFgkjToihdm7+rDNWLRJGyvJWpIxYG6JhQSpbfSRCWEjMbkVi+z/b+W7nd+rvPcf20OvTXurv\nmz6+iYOnDrL/xH7d9CMXj8zbaThAuY9+9Sg//fkTAJN+msSdc+7k71N/czL7JMt2L2Pt/rVq3DX7\n1+huHS8xjnZF0EiT+VYmT3z7hPq74ssVUZ5SUJ5SWLVvVYCU9hPdC1LFn1MPNx/arB5fzLnI7M2z\n1d9ztszh30eucO1V8dpGaPy2em79qUW8vDKbk3E9fH0kRmbA2RKw+Ub441o1uPLLlfn3tf9Wfy/Z\nslSVYe+JP12B1/4LLtmeK2e26/8r3oLtHaDEjrwymr/oWWbVxZwpWoHvdx72DK+2SD388vcvWXdg\nnetHmQ1q+Hc7v+PIJeeBtlDUtTj7hxs+xIeRlX3DvCm1WTf4t4O/MW/NCTifwoCsI8HzAb7f+T0b\n/97okcezS58F4ET2Cd1KvWj7Io/fS3ctZVvqNo+wjX9vpHLxyvy892c1bNq6aWz/Z7tHvClrptCw\nbEOPsCNnjzB43mCmrJnCv6/9N/Fx8ZRKKcXuY7sBmLslbwlbbWMLMGPjjKDXrMdnmz4jrXAavx/5\nXQ3bcSSvLngrKO4OQI8vtn3B//b9D4Bvd36rG2fN/jXsOrpL99z6v9YDqHl88usnHueFEMzaNMtv\n+eOXjef4ueN+zwOcOn/KJ2zdgXXULV2XhdsWBkzrxsdJ9imFJuWbqHK70dsWXnlK0T12c/TsUY/w\nVtNasWz3Ml05Rn05KmD+px87TctpLTl46qAaNmzhMN28ADp/2Jmth7fSsVpHFvZdSMWXPfdFmL15\nNrM3zyatcJpu+t3HdvvN/73176nHzy1/Tj2esmYKAFPXTaVXnV58/OvHPmn/9e2//MqcXzh58iRP\nPPEEc+bMYf/+/RQrVowGDRowYcIEGjZsyLJly3j11VdZuXIlf/31F6VLl6Znz56MGzeOpKQkNZ8B\nAwYwa9Ys1q9fz/Dhw1m2bBlt27bls88+Y9u2bTz66KOsWLGCo0ePUrJkSVq2bMnkyZMpWtS1p8G0\nadOYPn06Gzdu5NixY1StWpV7772Xe+65x/F7oO0fw4ESbevFAyiK0hhYzWCgfKSliUI+ex+63xZp\nKRyl2iXVfBSGSDGuzTge++axkPNpnt6cH//80QaJQufaytf6VVBC5bv+33HNu9c4knek2H3/bh9l\nQI+p3aZyR6M7PJQQkSV0FZ1gtKrYiqW7l3qEiSzBhOUTeOSrRwKmvaL8FfpfpfuAya7NovLrypZ9\n+/bls88+495776V27docPnyY5cuXc8stt9C7d29GjBjBjh07aNmyJZdeeik///wz06ZNo3v37nz8\ncZ7ydccddzBjxgzS09Np1aoVzZs3JyUlhVtuuYWaNWty/vx5hg4dStmyZdm7dy/z589n5syZZGRk\nANCsWTPq1q1LgwYNiI+PZ968eSxevJjXXnuNIUOGWL6+NWvWkJmZSaD+sWORjnwx6guATCFEgN3V\n7CG6LRISfdL2RFoCiQWixTkP9K0JdnH2wlnH8o4UkfCHkNM5rbFw4UIGDRrEhAkT1LCHHnpIPZ4w\nYQKFCxdWf991111UrVqVxx9/nD///JP09HT1XHZ2Nr169eLpp59Ww9avX8/OnTuZNWsWN910kxr+\nxBN5wwwAP/zwg0c5Q4cOpWPHjkycODEkRcII4W5rpCIhiUoiuZOdN3a9lNHinAfOOmZFk8JkF3bs\nCWK6zBB8dex6f06fhs36o6C2UasWpKTYl1/x4sX5+eef2b9/P+XKlfM5r+3cT58+zZkzZ2jevDk5\nOTmsXbvWQ5EAfIYiihUrBsCiRYvo0KEDycnJunJoyzl+/Djnz5+ndevWLFmyhBMnTqhDIPkBqUhI\nJGEiqhQJBzv7aBwuDRXDzrw2Xnso9cUuhWbzZsg0vrO3JVavBjtHWSZMmMCAAQPIyMggMzOTTp06\ncfvtt3PZZZcBsGfPHp588knmzZvHkSN5fmCKonDs2DGPvOLj430Ui8qVK/Pggw8yceJEpk+fTqtW\nrejWrRv9+vUjLS3P52X58uVkZWWxcuVKDydNdzlOKhLhfgelIiGRhIloUiSclCWartMuInFN/soM\np7WuVi1XR+90GXZy880307p1az7//HOWLFnCCy+8wHPPPcfnn3/O9ddfT9u2bTl69CijR4+mZs2a\nFClShL1799K/f39yvJZI1loVtDz//PMMGDCAOXPmsGTJEu677z6effZZfvrpJ8qXL8+OHTto27Yt\ntWvX5qWXXiIjI4PExEQWLFjAyy+/7FOO3cihDYkkyrBLu4+mL3U5tGGOSCgS/hYZCycpKfZaC8JF\nmTJluOeee7jnnns4dOgQjRo14plnnqFs2bJs27aN999/n759+6rxv/rqK9Nl1KlThzp16vDYY4+x\ncuVKWrRowZtvvsnYsWOZO3cu2dnZzJs3jwoVKqhpvv76a1uuL9qI8nUkJJLIky99JOTQhinMblNv\nB/78MuTCUv7Jycnh+HHPacslS5akfPnynDt3jkKFCqnxtLz88suG7+uJEye4eNHz2dSpU4e4uDjO\nnXMtWRAfH+9TzrFjx/jvf/9r6nqsIoc2JBLy51dtNHnhh3NDtvyAHbuUmiUkH4koclYOJydOnCA9\nPZ2ePXvSoEEDUlNT+fLLL1m1ahUTJ06kVq1aVK1alQcffJA///yTtLQ0Zs2axdGj+ot/6fHNN98w\nfPhwbr75ZmrUqMGFCxd47733iI+Pp0ePHgC0a9eOhIQEunTpwt13382JEyd4++23KVOmDAcOHHDq\n8iOGVCRikvzfSETT17tdRNM1hXtDtlinoPpIxBopKSkMGzaMJUuW8Pnnn5OTk0O1atV44403GDx4\nMADz58/nvvvuY/z48SQlJdG9e3eGDRtGgwYNfPLTs1I0aNCADh06MH/+fPbu3UtKSgoNGjRg0aJF\nNG3aFIAaNWowa9YsnnjiCUaNGkXZsmUZOnQol156KXfeeaezNyECSEUiJsl/DbU30dQZ5UcfCWmR\nMEdE1pFwcJvy/EpCQgLjx49n/PjxfuPUrFmTxYsX+4R7D1dMmzaNadOm+cSrXLkyU6ZMCSpL586d\n6dy5s0/4gAEDgqaNNaSPhCQqiaavd7uIpqENR50to0hhsotY85GQfhQFm3C/g1KRkEQl0dTp2kU0\nKUeOOlsWYIuE9JGQFESkIiGJSvJjpxtV1yQtEqaIhGIbTfVFEluEW5mXikRMkv+/NvJjZxRN1yQX\npDJHQV1HQhKbyKENiQGip0Nyimga2rDN2TKKnpuT9zeartMuIuIj4cfZUg5bSKINqUhIopL8+FUb\nTV74cvqnOWLOR0I6WxZovLefdxqpSEiikvyoSBw9a3zRG6eR0z/N8dfJvwzFGzh3IC2ntvQIKzS2\nkKUyD54+6BO27fA2HljyQNC0K/assFSmJH9w/uL5sJanROPXg6IojYHVDAbKR1qaKCQ7BRJPB48n\nkUgkWvYBk2H16tU0jsVNNCSsWbOGzMxMAvaPuc8ZyBRCrHFappAsEoqijFYUJUdRlIkB4vTPjXMx\n9/8cRVFkLxgKUomQSAo8KQkpkRZBIgFCUCQURWkCDALWG4h+DCir+atktVyJRCI5/ujx4JEkEklY\nsKRIKIqSCkwH7gKMDPwKIcRBIcTfuX++g38SiURiEOlMKGdvSKIHqxaJ14B5QohvDMZPVRRlp6Io\nuxVFma0oyuUWy5VIJBLZiUpCpnLlygwcODDSYuQLTG/apSjKrUBD4AqDSbYAA4FfgGLAKGCFoih1\nhBB7zZYvkUgk0iIh70GoyPtnH6YUCUVR0oGXgeuFEIbmlwghVgIrNXn8CGzC5XOaZaZ8iUQiAYhT\n5Mx1iSRaMGuRyARKAauVPHWuENBaUZThQGERZD6pEOKCoihrgWpBS1sEJHmF1cv9k0gkBRY5tCGV\nqVhECEF2djaFCxe2L9MNuX9aztqXvRHM1sSvcHXjDYEGuX+rcDleNgimRAAoihIH1AX2By2tA9DH\n608qERINgB0OAAAgAElEQVRJgUeapaUyFYhly5bRpEkTkpOTqV69OpMnT2bMmDHExQXu8o4dO8b9\n999PxYoVSUpKonr16kyYMMFnxdIXXniBq666ipIlS5KSksIVV1zBrFmzfPKLi4vjvvvu48MPP6Ru\n3bokJSWxePFidu3aRVxcHBMnTmTKlClUq1aNpKQkmjZtyqpVq8xdbD18+8kO5rIIFVMWCSHEKeA3\nbZiiKKeAw0KITbm/3wX2CiEey/39JK6hje1AceBhXNM/3w5ZeolEUiCRnahUpvyxceNG2rdvT+nS\npRk7diznz59nzJgxlC5dOuA9O3PmDK1bt2bfvn0MGTKEjIwMVqxYwejRozlw4AATJ+YtlzRp0iRu\nuOEG+vXrR3Z2NjNmzOCWW25h/vz5dOzY0SPfr7/+mpkzZzJs2DBKlixJ5cqV1XMffPABJ0+e5J57\n7kFRFJ577jl69OjBjh07KFTI2oqokcC0s6UO3laIDEC7qUAJXGtslQWOAKuB5kKIzTaULZFICiCy\nE5X448knnwRcVokKFSoA0KNHD+rWrRsw3Ysvvsgff/zBunXrqFKlCgCDBg2iXLlyvPDCCzz44INq\nftu2bfMYnhg+fDiNGjVi4sSJPorE1q1b2bhxIzVr1lTDdu3aBcCePXvYvn07aWlpANSoUYMbb7yR\nxYsX06lTp1BuQ1gJWZEQQrQJ8vsBIPji8BKJRGIQaZEIzz04ff40mw85+81Xq2Qt21bpzMnJ4csv\nv+TGG29UO32AmjVr0r59e7744gu/aT/99FNatWpFsWLFOHz4sBp+3XXXMX78eH744Qd69+4N4KFE\nHD16lAsXLtCqVStmzJjhk+8111zjoURoufXWW1UlAqBVq1YIIdixY4fxi44C7LBISCQSSViRFonw\nsPnQZjInZzpaxurBq2lczp59Pw4ePMjp06epXr26z7maNWsGVCS2bdvGhg0bKFWqlM85RVH4+++/\n1d/z58/nmWeeYd26dZw7d04N1/PB0A5leJORkeHxu3jx4gAcOXLEb5poRCoSEokk5pAWifAoU7VK\n1mL14NWOl2EXbqdIvXsTbC5ATk4O119/PY888ohu3Bo1agCwdOlSbrjhBq655hreeOMNypUrR0JC\nAlOnTuWjjz7ySZecnOy3TH9+ENG4mWYgpCIhkUhiDmmRCA8pCSm2WQvCQenSpUlOTmbr1q0+57Zs\n2RIwbdWqVTl58iTXXnttwHifffYZycnJLF68mPj4vC70nXfesSZ0PkBORJZIJJIYRK4j4UtcXBzt\n27dn9uzZ/Pnnn2r4pk2bWLJkScC0t9xyCz/++KNuvGPHjpGTkwO4rAiKonDhwgX1/M6dO5kzZ45N\nVxF7yJookUgkMYgc3tHnqaeeQghBy5YtmTBhAs888wxt2rShTp06AdONGjWKRo0a0aVLFwYPHsxb\nb73FxIkTGTBgABkZGRw96tqfskuXLpw6dYr27dvz1ltvMXbsWK688kpdv4yCghzakEgkEkm+oV69\neixZsoQHHniArKws0tPTGTt2LPv27WPDhrwlIBVF8RgiS05O5ocffmDcuHHMnDmT999/n7S0NGrU\nqMHYsWMpVqwY4JqFMXXqVMaPH8/IkSO57LLLmDBhAn/88Qe//PKLhyzeZRg5FyhNtKJEo1OHoiiN\ngdUMBspHWhqJRBJtiCyB8lRsNbZmKJlSkkOnDwWMU7pIaf4+9XfAOD7sAybD6tWradw4dnwf7OCp\np55i7NixXLx4MXjkKGbNmjVkZmYSqH98qd5LjOwxEiBTCLHGaZnk0EYwtnSJXNn78vmLfrJMpCWI\nGYZcMcRSunKp5QzFu6vRXdzb9F5LZUSKpHjvjXiim2sqX2M4bsuKLYPG0X4EdqgW5jWRJVGN8Fkn\n0lliR5F4y9kpSLrMngpfPedM3tvbB48z9x3YamJ1s0nb8o7/CryKW1Tw8WeOZNutZjdH8m2e3txy\n2oeaPxRS2SOajQga5/5m9/uEXVflOkP5T+k2hUkdJ0kHPgcxc2/NWorlc5NEktipfSISZkwFhEO3\nyEi+QgHFRIOivUdKjnmZwo1D97aQ4swa9aE01qHOlTdSdkaxDJ+w/OyQF43DsoEw8yxyRPD3V/vV\nKRUJY8Sa70GsEEO1L0IVwDFFwsj1KPhuZRIsvvswFhQJZ55pfJwzPsShNEKF4kJTbqyWbTZdflY8\nIo2ZZ2FEkfDIWz63oGRlZXlM2ZTYRwwpEhFARIFFwipmLBkRw5nGL9RO2wlCVW6MdBR6cfJzBxPu\nceBQMfMszF6btEhItITbWhdDtS9CjUaOQ52SIQXFZCfgoXjEQCPrkJLmmEUihE7ZKZmCYdoiIU2/\njiF9JCT5ldipfREx1TtpkTCgoJi2SGiHNqQiYTchDW2E6LdheWjDpPITSxaMmPORcHBoQyoSkkgS\nO7UvEh2jo0MbBn0kTF238HMcpTjkI+GUs2UoyIZeYsoiYeD91SpS0pIkiSQxtLJlhDrGiPpImCxb\na7WJCWfL2Jq1EQohO1sa8ZHQWyXPrEVCdkiOYfesDat5g2vvCUlsEo3PLnYUiYiY6mPM2VJ7j+TQ\nhu2EYvYPh3KjZ+rPz5aQmHO2tHlow9L0zxSIS4yjX79+hmWRRB9xiXHkpETPx2IMKRIRumlGfBks\n5WvU2dLi0EYBtkhEpY+EnP5Z4IkKZ8vikDM0B06byt5W6pWpx4a/NgSPqOGLvl9QOrU0mW9lqmGJ\n8YlkX8gmNTGVk9kn7RbTL11rdmXelnmW0zco24D1B9aHJENOSg4UDykLW4kdRSISXx9O+kgYabBD\nsUjExNeaM51WNJrnw2EZiMbrdpKYc7a0efqn9vpN1a/iRLQTKpJeBEzq1XUb1iU9Ld1jb4mEhASy\nz2cTnxQPZ+2VMRAlq5aEE9bTp2akOu6dKJfI9kekTPVOrahp1CJh5rpjzUfCIaLRnB8pvw3pIxE9\nOLogVQF8bu73PNzve6hWu/z4rKKvxfVLhHwknDL1OuIjoVUkYuBrzSElzSnzfCj5htrYWS07PzZa\nbmLOR8JBZ8toVJ79YcWSFMj/J9zDcfn5nbJK7NS+iE3/dEqRcGKJbDm0AdH5okdqtc38vI5ErBEV\nPhL5iFi1SOTHZxVDVxSpjtGphtVhH4kCbJFw6kUNRUEJ2SJhoGzdJbLz8cqWMecjYeLeZl/MDhrH\nY9ZGDDXlKQkpptPo3Tu3ch7uOhuqJSyxUKJNkkQPsVP7Ijb9M4IWify+joRDRONXdTi+QvTKiMZ7\nUVAx8yw2/B18VkO/enlTOKNdARx55Uj1+OUOL1M8yZy3Z3pauk+YnkWiYdmG6vFDzR8yK2ZAbq17\nKwBVS1S1nMejVz3KvU3vtUukqCF2FInjFcJfZvI/cCHJmbzPGnmRTDQOp0q5/txs6WZaJFOcTw49\nj+yioeehw/Fs4y7VVUpUcUQGb4oVLhZS+mCdUJ96fbir8V0+4U0rNDVVTqhyWuHD7h+aip/9hOfX\n+oI+CwBITUwNWZaPe35M1tVZhuK+e+O7Ac9vGb6FLjW6qL/dnf3bXd8Omrf7q/XUY6d8zjUp34Rz\nT5xjUsdJalggRXX8deN9wj7u+bHH7zFXj1GPS6aU5OTok7x343ss7LMwqKxa6pepz+nHTvvkP7H9\nRGpcWgOAS5Iv4cgjR+hTr496PlCn379Bf/W4Y7WO6rGej8TdmXerSsrz7Z5Xw19q/5JHnhf/dVE9\n1j7vwoUK68owuPFgPurxEWceP2PonSqRVEI93v/gfuqXqQ/AIy0foWuNrnzU46OgeQC80uEVQ/Ei\nTUwoErVWfwFHgmuBg6o8A88fgGeP5gUeaGCusHlvwqLcSpdwGi4WptDpcubyMMLRy2DiHnj+L3jx\nz7zwZ07Blq6uY6Eza+MZ34aFj2bDS7vgZDmYuBv+fRYWvO55H7Rs7awf/slM1/+HagaXf9aH8Me1\nnmHPH4A/NS/ZvkxY+Kp++qWPwoncuVynLw1c1gVPU+CMK3fAwkl+IkO8Es/hhw/zz8P/cHDUQY48\ncoTJXSZ7xNkzcg8nR5/k16G/cmjUIcD3q+eLvl94/NY2WN5fVHNunQNA+aLl2XbvNo9z83rPo1iS\n8Q66UrFKhuNuGLKBww8f5oPuH5Cc4Kvc3VDrBg95nmz9JC0rtvSb3+e9PjdUrlsBS4r3VLQ7V/dT\nt/ywcchGetfr7RHWqmIral7qvw4mFEoA8kzMbS5rw+GHDzOjxww1jtW1RNpXbc+Ya8aQHO97L91f\npG56Xt4zYF41Lq3BvN7z1Ofp7vj0vq69cQ/b6HVsyQnJJBZK9LBCBFIkSiSX8AnznkXkbW4vkliE\n2xrcRsfqHTFD0cSiJCckB5TH/R5p36dASqD2OqtfUl091rNI+Bvu8r4+bZrSRUqrx2efCDyP1Lu+\n+0Mrc2KhRFWuOCUORVF86pI/EuISDMXzRk7/9KJKiSpsmtuBon4+XhOUvAfbLrMGnCoD5zSNtllL\nRnZROJbhEVSr0iXm8jDK8XQ4VRpOaGQ8n6Kxguh8hZ7XGV88nwIXchu+4xlwsTDkxHveB4/4fqwJ\nZ0rAmBz4j8ElWM94NVCnyrjKdXO2OGQX0U97wsRz2XOVx8+kuNSAlqJ58wWXJF9CieQSlEwpSfGk\n4pRJLeMRp1jhYhRJLEJSfBJphdMAz68IcH2Z+cO7Ia52STXA1Th6dxSlUkqZMmtfkuxb3/yZrlMS\nUnTj68kGMPbasbSq2Mpv3Gbpzbip1k3q7+urXK/bmPnzmL+81OUBZfGmTuk6puLroaBwSfIlHo3n\nkCuGWMorkAOf2ZkU3qgdaJBhiCIJRdSyjA5XBapfZn1J7PA90ZPHna9dwzBqHQwxPyd8bbTPTXsv\n8utQY9QrEsFufCFNe27LQwrXVtwBfSQi6Vhq55RXo9dh7noVJY5AMu7aJfj5Z680XvG1jY/72FuL\nD1SfvBsvt2IhELrpzPhImGkYg9X5QA26P7Qdpsj9542/64lEQ+m+X1q5rcoRSJGw2uG4759bzqDP\nTFF80ujlpyVQ/dKL7x1mlw+PGadgrQxWlAH3OxdqnXPi691DkdA8z/w4YwNiQJEIhrYS6T4kS06a\nikfaiDkyCbPTPwsGwT3UBc2aeaXxqhtGvhK8n3ugeqA1peuli1QdMtoRafH+8tbrQP19XdvRUGob\nXkPx8VUkrMoRaCaA932xOrU2WF1QUGyd/mkkr3DUz0DKkdF7qbe/iNUZTU7ir30xe59jZa2UmFEk\n/L0LQccJTc9eCFeFC1CO6WmcNsls9wwV22baeFsKgqzJkFvuihV59cZHkdCxSPhk423FCKB8aNeJ\n0EtnyiJh0nRtNi9TFgmhb5Hw93UdCYVJtSjZsK12oEWOrDbq2vFxozIEKsusxUvXIhGgDjjVeXnf\nB6MdbDBFPxq/8v3JFI2y2kHUX5UR7d2N/kOy8FKoHarz2uCOHZCj0XVq1oRrr3W6XJsa+6CKgnBk\n2m7wl9FV5lVXwaRJ+mmsbssdTCbdr3dFMaUION0ZB+sotOcFIqBFwmi4k+hZJKzihI+ElaENO5fI\nNmSRsOm5BfTVcN8HvaENC+VHamVLI3gPbajHUSirHUS9IuHGiEVC92Uy25EJO30EgpXjQiv25s1Q\nvIRbZl85HnsscF6hEaFKbvoZxRm+5q++ghMnAg9TqA2b8LZ8mPeR0DvnLywc6A5tmLBIQGDFw8zw\nj1Po+UhYRR13N3DfjF6r6mRocGgjTokzPbQRqK7q3Rcff6BwDG0EcLa0ZJHQuZ/+6mqoipZZvNsX\ns1apWCPmr0r7YGyzSLiJ9OqQOj4SlYzPDLRYXiSweTlgzXObPx/S0uCzWQGmo/lztjSgfLjx8JEI\ncWjDTqyY6M10yIGGf8KFnkXCib1JQlVUzHxBm1HetHkbzcuM0mwGyyuwWig/Foc2TPtIxMjqrdH3\nBPzg1yLhhLOliHzj6C7ZG/374BnvX/+yr7xoJC6oj4Rvg//WmxaGNgL5SHhbJOKCWCSi6N4G6xCN\ndJh+/UoiuMJiqLMAtOjO2jAxq0cvneH4QgR8BnZM5wykqDjVeQUa4glokdBaHLR+MBhXJIwMudiJ\n97XJWRsBUBRltKIoOYqiTAwS72ZFUTYpinJGUZT1iqIYXuXEyHhiwLghOVtGRhsM/JIHTrtmDTz1\nlM0C+SWIMIoIEMf6viCKlZfRWzk08uVkojOyc/qnnQ25laEN7flgcX2sNjYoTKbN+jYObah56lyH\nZR8JL5O+kXplZx0wZJGwSQE0o6A74SMRLV/wch0JgyiK0gQYBKwPEq858CEwBWgIzAZmK4pibuUa\nf/kHdba0lqvzGChDZ6hB9z3RxIvLnwqvB0rQPUh0bpIwb5HwWz6+zpNBLRImGmo7v5DCPrRhU4dk\npUOwVZEwMP3TKN4WiWB1L0fk2FoHwulsaUQO0xvJ+fORMKGYGZHLTjxW29Q4LMvpnxoURUkFpgN3\nAX7WYVYZAXwhhJgohNgihMgC1gDDDZZl+Hx+8ZHwqNheMujWeU0ctyJRsaKVgu1uTATGlDIjlo08\njh0L4hCr99y8FQkLX+paXwrv9IGWZBaIiO1IGqqzpdmGzI4OyWrHYIePRKD0oTpbGl33INg9N6uo\nGlmQKpxDUnbsUhssPzM4vSBVJAi3Zcbq1b4GzBNCfGMgbnPgK6+wxbnhhgmbj4TOypaOvGSGOm0r\nDkjmRbGMoXtrf4Xu1y9YDHssEmYaKHVoQ288WugPd/jD6UbAzPRPfzi5IJVVnLZIWF5Hwsl2xEj5\nJi0SdnSsge6f7joSIQxtWJXHSfwNbZglWoZqgmF6ZxtFUW7FNURxhcEkZYG/vML+yg0PXp4ZHwnd\nymL2QShhcra05vzj7NCGzddpVIkzqeydOWMlv9B9JAI1fMEaNVM+Ek4PbZhckMpM/hF1ttT6doR4\nD+30kfDOM1h7YnfnYXZBqlAwMutFb5aUUWdLLVq/JDfRMhQQaYtEuDGlSCiKkg68DFwvhDgfQrkK\nRnr4RfDnij/ptqQb587h8rSol/unZmSzRUIVz2paG7GwRHYE23F97LiHpodcnPWR0ENdkMrCPHZv\nHHe2tNNHwgFnS6vYaZEwkr+ZWRhgfEzf7s4wlFk4ThDK9E895SMav9q976ejCs6G3D8N04pOc648\nHcyqTZlAKWC1oijnFUU5D1wNjFAUJVvRr40HgDJeYaXxtVL40gHS705n7ty5JCbOhT54KBFgwEfC\n9KwNIrieQm7xZn0kPKYahVKwEz4SDhFIVqs+EgblVfDdOyPYF4iZDjbSsw/M+Eg45mxpoe7Y6SMR\nLH8zFI53bQVu9CvVdouEgemf4Zj54M/hsEhCEUsLUjUs2xDw3CK8TBHvrsa1+23l4pWDyhWIWiVr\nBY2jpWqJqh6/m5Rv4hMn0C68bgLJnVku03VQD1ffqPl7eOzDhmW1A7OKxFe4xG4INMj9W4XL8bKB\n0H8iPwLXeYVdnxtuGMs+EiF1ZpGe/mlh3NDIE/XbCdvw5e+RndH7Z1c+AfLzUiTOng0+ldZHjABT\njQN9HZntFJ02z5qZ/ukPv+tIOGSR8N6aXQ/tVvEVi/n3Nq5SokrQvOqXqe8bVtozTHsP/n3tv3m4\nxcM8e92zPule6fAKnap3Crpb5VUZVwEw65ZZHuE9avegc/XO6u+GZRqqx+4OzrvD6VKji3rcp14f\n9bhR2UZ0r93d5xkLBM3TTbmt8XbXtxnQcACvd3pdDTOyXoO3j8SygcvoVacXZYqUYf0969kwZAMb\nhmzwm4+bl9q/BECn6p3UfG+qfZNHnF3372LVoFW0q9qO4U08/fuvrXwtkPes5946F4Dd9+/myCNH\n+OZ2lwtgRloGI64c4VN+udRy/G/Q/1gxcAXj2owD4PFWj7NhyAZuqXOLGi+hUAKTu05m6/CtHuln\n3TKL5QOX8063dzzCe9ftrV5f15pdda/95stv5rsB3/Hb0N88wneO2Mn83vOpX9a3/jqJKUVCCHFK\nCPGb9g84BRwWQmwCUBTlXUVRxmmSvQJ0VBTlAUVRaiqKMgaXZeM/wcorm1qWcde5stLuR9Eh/Vb1\n+LoqeTpKnVJ1fDP54Qn4R6fh2HsFRX7vS7HCxbit/m154btb4t2hXlbiMo/fFddN9s1vfRAPwJX3\nAVDsTH24UBi2+V9KIyMtw3VwMcHnnBDAli7w072aQP8WieYVrgosl0fmmsQrHgwe/7hO4/7DE8bL\nA/irHrV2ToJ5b/qP8+eVcD7JM2zH9a77eKCBZ/jZNPjfUN88DtWE7CLwzb/hRFmSk2H8+LzTlYpV\n4ulrn1ZfYnA1zkUTi+qK5IQpeFSLUQCUTCnJoMaD1PBmFZr5S+JDrzq9eKj5Q+pvt5zPX/+82nAO\nyhykm9bNv67+F8nxyervSsUqqcf3Nr2X1YNXq53Ai+1e9Egb6L64G36AR656hF+H/qobb3TL0R4d\n0k93/cSyO5Yxv/d8Ng/b7Df/rjXyGt1hTYfpxlk+cDk/3fWT3zzczLx5Ju90e4cTo0+wfOBylt6x\nlJc7vMzSO5bSvXZ3n/hPtH6C565/jkdbPupzrnvt7izos0D9rb1HaYXTuDvzbvY/uJ9lA5chsgTt\nq7X3WHL901s+5ZObP2Fgw4Es7LOQF9q9oJ7bNGwTIkswtMlQVQHpVL0T83rPA1xtaEaxDDX+mrvX\nMOuWWfS4vAcDGw5kQtsJ6rkVd64A/CuyqYmpHr/vbHwn026Yxt1X3K17bd64LTrew4BFE4tSIa0C\nBx46QP0y9albui51S9fVzVOrABVPKs6hUYe4/8r7AZci5j17qmKxihRLKgbAq51e9Ti/qN8ijj16\njOuqXMehUYfUTjujWAbFk4pTr4zL/H15qct1P1LvaHgHV5S/guYZzRndajQiS/B0m6epW7quWn+f\nuuYpEgslkhSfRPVLq3ukL1WkFC0yWvgogeVSywH6VgxwKYaf3PwJqYmp1C5V2+NcWuE0OtforJvO\nSezwCPGudRloHCmFED8CvYHBwDqgO3BDrhISkAV9F6gvbcM8JZzOGX0BaF2pNVO7TUVkCUSWUCuM\nltoJHRmh/M7cxoKhDUap4R+3W8nvz0/n6KNHee+m9/ISnMzzAb2yuaB9e3j3xnf5tv+3MEbAGEHJ\n3b4N8ZIh7yOyBPVK1/M5x382kfTdKzBG0Hb7enj6LByr5BsvlxfbvciSfkvgXDG8b68QwEfz4ItJ\nsOwRn7Tud879vj3eMst18L3JDn7JC7zf+id4aRe8v8h17V+8ohFEgSUvwLHcRuq9L13/b+8Iyx/y\nzc8fb/xC+sE7YPXdnuFjs+GTT1zHp0rBM14elsfTXffxzXVwpLIrbMpKGH8M9up0vNlFYdxJl6Lz\n4n4AZs/OO73z/p30uLwHH/b4UK1PKQkpHB993CcrI8sXiyzBvU1dyp4QgqT4JG6sdaP/+4Drawag\ncKHCTO46mcblGgMwo+cM/+V5Kb0zes7g+XbP+5x/qMVDfNPf9YXlztcfnap34sBDB1TZV961Uj2X\nkpDikb5v/b668uiZbe+/8n7uybwHgBtq3sDlpfSXkulQrYPacB955AhNKzSlUvFKdK7RmZola/qV\nO/hUcGiR0cJHOdT7+i1auCgDGw0kNTGVFhktaFmxJYqi0LJiSz7s/iF7H9jrV45gaJ9ZfFw8b3Z5\nk7Kpnr7n6+9Zz4fdP1R/pySk8M4N79CxekePNUvcxClxqkXDCO78KqRVsHAFnhi1QrkVCa2SFCqX\nplzqdwZRMBILJZJWOE3Nxxs7rGslkkoEjeNvWrEVy2SknDxDLlUI0UYI8YDX74FecWYJIWoJIZKF\nEPWFEIvNlrNYk8JdX9pVaaeOP/rjt9/g5Zeha1d47cYJqimwZw+FMr7DaR506CBYtMil5V1T+Rqf\n8rVcf32AjA7V4ocfApelJTkhmeur6mdoZIls63jm0+PKpqz7viL83l5HkDi4kAzr+vue+z7LQFG+\na18EksUQf5kz5+3dC4cPG49vZLqa3hK+4GocPu/1uSn57MCq5UQru3cnFwijayUYPe/EUEmoDqKF\n4wtTvmh5Rx1L65SuQ+96vYNH1BCKlSwci1ZZ3bjKzMw9OwlpbQsb1sWw4qsSqVlTMTNHpURwxc4+\ngjgdWnlW5cvnZu1VN1JTfeMGwjO9b0Xz7pRVWS3MnlAUaNAAxo3TORlodUmT27DrKhKmHD+tvTx7\n90J68KH3vFI0nZuZ6aOGnTijaAaEUdn9+YoEbfyDnDeqkFgh0ssUR3KKrDfhXEDMe/qn3fnbjS3L\nvVuwKkRycS6rxIwiYTeh7MAX6DmbqQRZWVCqlOHohrDznfMeJvFQDLwVCY+OX3tsxHHPgnB+yzbH\n2bMhlu2FrUsbey2tbAXLu2Ba/AoMZmY2ujhTOBvEcK09oPc8nb5Oo1+1HmsxhDBrI9ybYwUrM6R8\nQxhiCMmqEMr7Li0SxgnlXhlb89x+i4Q7jTZt1ar6cd38978YnP7pW46/3wHx2djK8/Rdd3mcDZ6P\nQSuIvoxWHnL4XqJIf9UaxfLQhsE5+j5WlNzfoU6HDefQhtVpnaHeW7uJ9jppx7TmgP5JUXT9oSgh\nIZUrLRLGCZfS5cR8am2WwaZq9u8Pzb1mZHXNdUyvXFk/jTtPdzmh3Cpv5cdzVCW3IAOrSFoiDEMb\nbho1gnXrjJRiwIrlZ4lsM9hhiQgVIz4ggdKFOq4dzqGNcC1oFE0dndM4sTJo0DJtrivhsir4Uzb8\nhgeQR1okogkHfCT0MLLmg3ecatVcSsIff+SFzZ6dJ1DJkqFIFNgi4YGPj4QmskkfCWcdSIOzbp1L\nmQhG8OXYveKH6pwWweV+Q/aRCHGdCb39GOzCrlUHzcrmtIJopROxvfMNUG8sW35i0NkyUkiLRFQR\n+tiYobgGogaUIdcaULx4XlBKincZeoX4KdhLgfJWYjw0YR9FQiunZmgjnMuMh3FFUr9f7A50/tHU\noOGhCXcAACAASURBVAUbcjBskQjxfCh45+30l7JP+SYV0lAwWh/t2qck0HML9322i1DuhxMzL6zs\nS+I0salIhOIjYaRSiMAmrVB8JLQYsUgYqYhxtlUeMxaJ6Onc7JKlWDG4cMFYXFN7Z4Tpi9dOLDtp\nGtxPwvD0TyeGNmzykbADO5+xlbwcs47o5Gt1CClSjrnR6vQYcGhDWiRMYMhhMjrx2EYjZPHd98F/\njLxz1qZ/Bjjr/1TElIzQyj1+HBJ8FxPV5G5uOMPqpkLmrFrOmnVNO1vabJEIR8PoVAcXKaJxEysI\ng49EFPqghNuaEbMLUkWEcDlbOjz9M7QtvzVlGrghw4YHjWJOAQjkI2HoAdnZ2IW/ATEytGG1YQu0\noVK4COZs6dcHwiaLhJPOlt6Ebfqnwx287kZ0YZ7+abQMMwTbUCzSyrRu2lCsGaEMrcuhjfBgrFKE\n52EYGtow4CMRCHe9KpaWF5ZSJHi5QbFxQaqQCZMFJJxj25HG6vQ1uywS4fyyCruPhMPti5m66ZSz\npR6x6iMRCuFenVIObZgg0tM/AykAdjtbWkWd/qlTxpV+94AKJlAgZ0t/+cSes2W/IPuvBRQhSs3K\nZom0IuDkOhLeRNRHwoFGwKk6GKqsTq3XEY1DGmYUcTufl7RImMDxW+XA9E+rzpZOYEtdC2R1MLkg\nVejYWyM++CBIaYpiyzxxn3z9zYSIQONg1axrdOXKoOW7LRvhGNooIMpfpIm1dSRikUjVgfjgUQou\nToydmlmQKuyE4CNRqaLCob/h1CmIhM+CE3zzDaxZk/fb7LLqsdywWfbvcPtQhFgHnFxHwptIrtfh\nNNE0/TNWCbfDZChppUXCDCHcK2OVwn6LhNV8DO3KF5HK41lmzVqwb593nFAWpLKCfffh2Wfhuutg\n1Cj987GsJBjBqo+EXRYJtyIRjk4+XEMbkRhCuST5koDnk+OTXf8nJAdNc2my71bb3ph57rfWuRWA\n0kVKB4x3Y60b1WO9+uBd5kPNH/Kb1+iWow3LlxSfBMAdDe/wCK9dqjYAnap38pvWjEJ1eanLAehV\npxcAXWp0AVB3qo4FYlKRiFdchpSEuABz9fzgfnECkmM9/5SElOCRcgndIuG/kXV3zvFxuddSKO9a\nChdK8pMqcOUvpGjuR+494mIiAHHE+SpGF5Igx889/LO5frgWdxk5QQxnDjhbPvaY/3MpCSl+Gwpt\n/SpcyLXFvVF/AXeD6E6nhqP4zcNpp0R3Y+rGXY+C1XPvdGa5ta6rkymkFDKVrlvNbh6/jXR+VUpU\nISEugbZV2poqyyxFEl1ezhXSKrDubtea7E5+wc+8eSZf3fZVwDida3TmPx3/w6DGgwCYdcsslt2x\nzCPOl7d9yS/3/MJ3A77j05s/1c3nnW7vAJBe1Hc73Zk3zwTg9U6vk1kuUw3vWrMrIktQtHDRgDK2\nuaxNwPNu3Pfy8daPI7L028ax1471e86b+Lh4RJagX31Pp6nyRcsjsgRXVbzKvywmFPEKaRUQWYIZ\nPWcgsgQtMlogsgTlipYzJOeXt33J+nvWG74uJ4ipoY27L/2It96Ip9uwdjwT9wzDm/rOaaxZE7Zs\nAT6aw8qvyvicn9d7Hou2L/IJ//r2r1n5Uw6PA+xuyY1Fx/HwVfd5xOncGRYscO13sfSVn6Dpq9Bg\nukecGT1nMHfLXIYtHOYRrqeoa8OuKH8Fq/at8okTsCLubgUtJpKRluE3SutKrRnXZhz3NbuPLxcV\nZsX6A9w3OouF+971jXzB1YGVLQs//+x7ut2lg7ik8p/8tq4o8/bnrim9YhRcTKRs9VaekRe8Blu6\nwanSkLofrnwFihyEN9fCgYb6wr6xDoZozm3tCl8/DavvBmBsrfn8a1Rx/bTg+OwNRVF478b3aFKh\nCcMWDmPToU0s6bdEVdamdptKy4ot1fijW40mNTGVK9OvVMPm3jqXtMJpLNu9jCvTr2THkR0Mnj8Y\ngNTEVF5u/zK96/X2KbtsallebPciDy55UA0b3mQ4FYpWcOpyea3Ta9xQ8wYAlg9czg0zblC/6Nz1\n3JtSKaV4sd2L3N7gdj759RMAFvRZEFSBf/rap3ni2yeYe6srz6FNhjK0yVDTMs+5dY56PKXrFK67\n7DouK3EZylN+prPmNr7ZT2abLkub3gj/uvpftK3SlrKpZSmZEtJa9oboeXnPoHHilDiGNc1rq7rX\n7u4TR6tgVSxWUTefgY0GkpGWodu5ujv4IU2GMKTJkKAymSUah1PCKZPTCrARYkqReGPYrUwc6FoG\n+rFW+p+MGzZAYiKwpRvNfJVjKhWvxN1X3O0T3uayNpzb7Dp+eFQc40eO9un858xxrXyYmAgPP9wU\nPn/fR5FIT0tnaJOhPoqEHlqLxI93/qhr+gw4tLH5RnjmFBlZ/r8O45Q4RrdyNf5Nz41mxSIoluUb\n74PuH9B3jMtkl5gIGRrdxC1CQlxhxrcdzxvbYJ775PkUWDYapbqXsvQ/TSew9HFY35/Fa36j/Rg/\nSgTAXw1gwkFI/9H1OyfelTaXZiU6wx69hGGa/onCbQ1uA+Djnh+zYs8Krq96vXr+jkaeJtDUxFT1\n3rvpWtO169rVla8G4DquUxUJgBFXjsgrz2u66QPNH6BJ+Sa0/m9rAF7t9KpfWU8/dprEQol+z3/X\n/zvS09I5ff405y6e042j7chbZLTg4KiD6m93PdfjgeYPAFC/TH1++esX2lZpG1AWcGYI467GdwWP\nFEZSE1NpV7VdpMVwDO274BSx5hgba/JaJaYUCUXx3UvCm0ArExrJH1xl6FkQChVy/Zll5EgoVQru\nvNNlMu/eHW6/HVq3zovj/qo1zXnjQymB6FOvD31DSB8X53nPSpWCgwc1EY6n066qjmbnzemSLkuE\nGSKwkmbJlJI+ZnS70Vv8qVWlVv6ie6Ad89bDrcg4iRXHy/zue+LGiVkp0XTvwilLNF23m2iUyUli\n0kfCKZxSHidOdHW0b78NVarAbbe5ykoKbRjZMbzfASPvhKL4xmveHPr0sU8uA1KEszCJQQpaoxop\nCsrXrz+isZ7l5xlBWmLKIiExjp1tSrlcn58SJfzH8X6HhYAVK1zHH35onyx+Sne6AFcpYW6oorFh\nlNiDfLb2Ec0+Ek4od9GonEhFIsqJRKXxbuP694f0dM+hGL00sm20F7vWZIg0sS5/rCCVE0mkkEMb\nMcw114SnHEWBthrH4CI6e3V4KxIRsbI6PWtDdoiWkB2cL+Hc3TS/E831KxqtB04gFYkYZsYMWLnS\ndXxJ4HVnTBHsveyr45Xp7WxphkBKR69eRjKI3oYkFKK5gbRKQWlYjZIfn7E3Tj/zaLTcFYTnqkUq\nElFOoDG2MmWgWe4GXLt2wZEj9pQZ7B0oVAjWroV58/LCQnlvAqV98EH/53Rysi6EkdwLWONgF4aW\nFi9gjoJO1qWCqqxF4/tZUOq19JHIJ6Sm2peXkfexYUPXnzaNE+9xlSpGYoWnAfnySzjXFQoXDh7X\nTqKxgbRKqLuKSmIPO59prChJBa0eS4uEhnzUXoeE1d1NrfpIBIqrnSLrV64wDW0c+Uehf/+wFJUv\nsGvfDYkkVokVxSdUpCKhIRqtULFSEePjnVHEom2H1I8/jrQEkvxEQftydYJobCMLmvIcZc20xBur\nY2x6yXJyV+AO1jlbeQeydJbdtgNjsoT3pa1aFf75J3zlFYTOJho7A0n0Eag9jKb3xMl1JKIRqUgU\nINyKhBPKcvHizkz/NCRruGZt5JazYwdcGnxTSYkkrERjR1pQMbP7Z35AKhIFCCctEqGkC6R0aPM0\n5njpJJ4X+NFHERJDItGhoHRakuhDKhL5lHAPbRjJ10p52nNVq8KmTYGX6g4nffq4lg+3a9qtPwrS\neGtBulYoeNfrBNE8fBDNstmJVCSiHDu/Mtx12l+H/8svrv8rV7ZexsCB1tPq4d3O1qoVPYoEwIED\nrsXAPvgg0pJICjrRNJxQKM7CNskGGHPNGAAW9lmohrmVsaT46NkFMT3NtdNxrZK1Qs7rydZPqsc3\n1bqJ8deNDzlPu5HrSNhA3L7mjuXdsGxD1h1YZ0teWotElRJV2HFkh8f5evXgm2+gUSPrZbzzDkyd\n6qyPRPfu8MILHrHsKSwYh/w3CkOGuBYHq1YtPKJEE8nxyZy5cAaAckXLha3cmTfP5Ns/vrUU95UO\nr3Di3AlH5EqOT+aSZBuXmjWIuyNtX7V92Mv2pmO1jgxvMpwf//yRtlXaBk+gw6pBq3zaqAppFRBZ\nno1LWuE0JrabSJ96Yd1qOCD1y9Rn+73bqXpJ1ZDzur3B7fz7h3/Tt15fpnefboN09mNKkVAU5R5g\nCFA5N+hXYKwQYpGf+P2BaYAgr7U/K4RIsSStQbp0gZtucrIEeP552LAB3pu0lWRRFt5yppw3Or/B\nQ80fou4bdUPOS+tsuWrQKo6cPULVSZ4V/dprQy4GgPvvN5/mnXfgpZdg48a8MD3ryXPP5SkSDz8M\nE9a2hXJree89uP12a/IG5EJhiD8HPzzuN8qJE3D55ZCdba2IRmV9tbcc4XpgcYrnTfhz5J/Ex0XP\nN8Dukbs5lX2Kw2cO07hcYzW87WVt2fj3Ro+4TSs05c3Vb1IypWTI5fa8vCc9L+9pKe59ze4LuXx/\nHH30qOG4dloRCscX5uTok6QkONq8GqJQXCFe7fRqSHlkls8ks3ymobgjm48MqSwnsEOJgNgYHjHb\nGu0BHgG25/4eAMxRFKWhEGKTnzTHgBrkKRKO3xXt0s1O8dBDrv/fU6qDziZWdpEUn0Sd0nVsyUtr\nkSiRXIISyc6MEYRS773T6lkktMpFSgrw1bPUPPwQt612SJGYcAgQIAKba8+ft5b9kUeOULiQ73KZ\nbkWikOJZboW0CtYKcoiSKSUpmVKSSsUreYQ/3+55Hmv1mEfYgIYD6Fi9I2VTy3qEx0JjaZTEQokR\nK7tIooONkUTiB1OKhBBigVfQE4qiDAGuBPwpEkIIcdCKcBJ7MepsGW7cfYii5MloFEUBRCESsksD\nMHQovP66vfKRbXz98aeect3fK66Ajh2NpSmeVFw33N25elskYoX4uHhKFSnlEaYoio8SUZCRzpaS\n/IBl+6iiKHHALUAK8GOAqKmKouzE5di5BnhMCPGb1XKjkVj5mArmbBkN1KjhmplhFHc77L6mSZMc\nUCRMMGZM3nGo9cLf0IZEIil4RLPSabqFUhSlrqIoJ4BzwOvATUKIzX6ibwEGAt2AvrnlrVAUJbps\nswUEJxeksov33zcX361AuK+pkDPO4hGhICoS0TTzQCKJBmJhfRArFonNQAOgONADeE9RlNZ6yoQQ\nYiWw0v1bUZQfcQ2BDAaCLqo8cuRIihUr5hHWu3dvevfubUHsgkUo60hEkqJFzcX3tkjkJ1QfCYem\n0kkkktjno48+4iOv1fGOHTsWVhlMKxJCiAuAe07OGkVRmgIjcM3mCJpWUZS1gKFJci+99BKNGzcO\nHjHCxMrQRixYJLSMGxc8jvtaYuWazFAQLRIFDWmBkYSK3sf1mjVryMw0NuPFDuxooeIAX5dzHXL9\nKuoC+20oN2qIFUUiFnwktBiR0x1HL+6bb9orj1nWrw9tcy+3SbMgKBKxYL6VSCJJNCudplooRVGe\nURSlpaIolXJ9JZ4Frgam555/T1GUcZr4TyqKcr2iKJcpitII+ACoBLxt4zVIdCipM00/FoY2tBix\nMnj7SGi58UZ75TFLw4auzb0mTbKWviBaJKLZoUwiiSTRrGybbaHKAO/h8pP4CsgE2gkhvsk9nw5o\n53aVACYDvwELgFSgeQDnzJgkGi0SS5bArFmeYbGmSBghkI9EmTJ5y35HkhEjrKUriIpEflpPwghS\ncZLkB8yuI3FXkPNtvH4/ADxgQa6YIhrbvgoVXEtJa3FCkRg1ChbprmsaOkba2GDOlvXqGSvrzTfh\nnnuMxbXC4sXQ3uTKxW5FQiKRSPLN0IZEn2hUJPR4/HGoXx9KlQoe1ygTJpj76l+xAj7/3DNMuyCV\nFjOKRKgfdnffDdu3B49nlfEW9tkpiIqE/EKXSDyJBSudVCQKEJmZLgfAhITIydC8OdSsaSxuqD4S\n3tSoEfh8VXuWxtflu+8gPd1cmoKkSMRCYymRSPSRioQNyDbQHLVquTbeevLJ4HGDYWYdiQkTQi8v\nFPbuhVdeMR5fdq75n2g2V0skRpGKhCTsKIpr106vtcZ04wXD7imtntuT24+ZXVELkkWioCKHciRG\niea6IhUJScRwDzVkZOifN/PemFE6AlG6tPEyrfLoo3DhQvB4BVGRkF/oEoknSfFJAJQtEr2b3UlF\nwgakBdoaXbvCtm3QtKn++UDKQb9+rv/dVg2jszOCEQ6l/7nnXH4qwYY5CpIikVY4DchrNAsKD7d4\nONIiSKKcSsUrMa/3PMZeOzbSovhFKhI2oKdIDG482NYyBjUelC+/1qppFks304m/+y5MnQr9+7sc\nSI0MSUSbwnf//YG3Tb+36b3hEybC3NfsPqbdMI3WlVpHWpSwIbIEd19xd6TFkMQAXWp0oXC8oQWk\nI4JUJGzgLp3VNd7q+patZUzuOpmcrPz9herd0QdSLOLi4I47XP/Xrw+Jic7I4DRlyrjW4Th82Pfc\nk1c/iciKMu3HIRIKJTCg4YCoHgeWuNgzcg+/Df0t0mJIoggru39KNFy8mD83jIoG7L6vVpSEDh2c\nW3AL4NAh6NjRddy6NXz/vXNlSSR2kJ5mch6zJN8jLRIhEhcnFYlQCWVBKivluGnSxDeOd5nhtFD8\n8APMnOlaGGvduvCVK5FIJKEgLRKSAkuhQpGWwJdbbsk7jjafDolEItFDWiQkjjN5smsJarM4bZHo\n1St4nEh25u+/H7myJRKJxChSkZA4zqBBrk2xzGJ0KW2rtGkDV18dOE4kh61uvx2aNYNhwyIng0Qi\nkQRDDm1IogbvTvv66+3NX29WyBdfwIkT/mWItP/Lzz+7/mrVgnsLzmxQiUQSQ0iLhKTAoiiQnOy5\nmmU0DW1oue8+2LEj0lJIJBKJL1KRkEQ9110Hqamh5xMtSoFVqlaFgwcjLYVEIpF4Ioc2JBEnWAf/\n1Veh5b90KZw8CceOmU8bbcrH1VfDb3ItIIlEEkVIRUKS72nZ0vX/N98EjxvIJ6JECThyxB6ZrLJl\nS2TLl0gkEm+kIiEpMLRp4/lbT2nwZ4GYPBnOnIERI+yXywzRZiGRSCQS6SMhkQTA3XG3bm3ffh6h\nIETkrSISiUSiRSoSkgKFdoaGGeLiXDuNutHuWhpuLrkE9u+PXPkSyf/bu/P4uab7j+OvT0gkIhuJ\nxBpBEBRJEEFiSRM/VOyJWFpLQ2hVoq3W0lpqaetXpdQP5Udi+dbaUEtQpX4ISogoQkXVGmtji0aS\n8/vjzO3cme9s986duTPzfT8fj3nMzF3PnLkz93PPOfcckTAFEtKhzJuXfV2oaqPYWBudOvlbRRvF\nn//s20u88oqvcvngg7RTJCIdldpISMOoR+dP5UokivUj0Wjjchx6aPb1dtvB44/712pDISL1phIJ\n6XDGjKl82XCJRFgQ9EydmkyaqhEEEaBAQkTqT4FEyBpr+OfBg9NNh9TWwIGVL7t8uX8uViJx/vkw\naVL1aUpKp07w6KO+uuOjj9JOjYh0BKraCNlqK9/ZzyabpJ2S+pmxzww27bdpqmlo5Kvo7t39c6lq\nl0mToK2tPumpxIEHZhtjNnLeikhrUIlEniFD0h+oqZ4O2/Iwhq85PO1kNKw11/TPX35ZeH4jHivh\nOzquuQY+/zy1pIhIB6BAQqSE//5vuOACGDQod3oQQJg19lX/EUckM06JiEgxqtoQKaFnT5g2rf30\nRiyJEBFJg0okpMNKIhholoBizz1h5sy0UyEirUiBhDSc22+Hp55KOxWVKRZInH9+fdNRzt13w777\nwu67w+9+B59+mnaKRKRVKJCQhhGclMePh+E1bP+ZdJuGQtsbPTrZfSRl1iw4+mhfZSMikgQFEiIx\nlKvS6Ny5Pumoxp13+v4mRESqESmQMLMpZjbXzBZlHo+Z2X+VWedAM3vRzBZn1t29uiSLJKOW7Rvy\ne8JsRHvtBRMn6vZQEalO1L+7N4AfAcMzjz8Dt5vZkEILm9lI4Abgd8BWwExgppml2wOSNJRGvn2y\nEoXS3wyBBMAf/wgTJqSdChFpZpH+7pxzdznnZjnn/p55nAZ8BmxXZJUTgHuccxc45+Y7504H5gDf\nrS7Z0orqdQdEPQKX4LMcdVTt91Wtu++G++9POxUi0qxiXzeZWSczOwhYGZhdZLGRwJ/ypt2bmS7S\ntMJBT6kSiWCsjkY3blzaKRCRZhW5Qyoz2xwfOHQFPgX2dc69VGTxAcDCvGkLM9NFmla50pNgfrNX\n24iIlBOnROIlYEtgBPA/wAwzizLMlQH6e5XUlAoCttyysm2ss07p+UGJhAIJEWl1kUsknHNLgQWZ\nt3PMbFt8W4hjCyz+LtA/b9rqtC+lKGjatGn06tUrZ9qkSZOY1EjjNkvVDjgArr7ad5ZUD6VO7lts\nAUuXQu/e8NlnxZdra4O//rX4/GYvkTj8cBg8GE49Ne2UiEgpbW1ttOUNP7xo0aK6piGJsTY6ASsV\nmTcbGAP8JjRtLMXbVOT49a9/zbBhw6pLnTS8/v3T6cmyWMnECivA974H555bfN0+fbLtClqhjQTA\nggXw73/D2mvD9Ol+2g03+IBp5ZXTTZuIFFbo4nrOnDkMr2Wvfnmi9iNxjpntaGYDzWxzMzsP2Am4\nLjN/hpmF/34vAnY3sxPNbGMzOwN/2+glCaVfJLLddvPP/foVX+acc6orTQiClGYKJDbYADbdFHba\nKTvthRd8T6Nz5sDUqemlTarz9+P/zvzvzk87GdKiopZI9AdmAGsAi4DngHHOuT9n5q8NLA0Wds7N\nNrNJwDmZxyvA3s65F6pNuEhcBx0E+++fXO+TpUokmrFq45lnct8/8EC2y/ILL6x/eqR6G6y6QdpJ\nkBYWKZBwzn27zPxdC0y7Fbg1YrpEairJLqzXW6/9tGZvIyEiUqkm6X9PpHENHw5/+hO8/HJ2WjO2\nkRARiUOBhEgCxozxdzkEWrVEwgwuvhi++so/REQUSEjLaWuDGTPS2/9BB0Urkbj44tqmJ2nf+x50\n6QKDBqWdEhFpBEnc/inSUA46KL19ByUQb7+d+z6KN9/0t2A2urfegiuvhG+XbDklIq1OJRIiNRDl\nro38/izWWiv59NTK5Mlpp0BE0qZAQqQGVl0VuneH448vv2y9Rj0VEakFBRIiNdCli+9ie5ddyi9b\nKJCYM8cHIs3AObjpJv9Zly1LOzUiUm8KJERSViiQGDo03bYeUXTqBBMnwkMPwXe+A4884huQvvtu\n2ikTkXpQICGSskqqNmbNqn06knD55TBqlL+zY4014NVX006RiNSaAgmRlFUSSOy2G/z2t7VPS9Lu\nuivtFIhIrSmQEElZpY0tt966tumohRNOgIUL006FiNSSAgmRCMaOjb5OuMfLQioNJLbdNvq+G8GA\nAfDYY2mnQkRqRYGESASzZsHSpeWXC3vqKd/JVBJ+//tktlNvO+wA550Hd97pO7ISkdahni1FIugU\nI/Tu2dM/kjBxou8188QTk9lePZ1ySvZ1q41BItKRqURCpM7eeiv31s5iJ9Vi06dNg6uvTj5d9XTL\nLWmnQESSokBCpE5GjPDPvXr5RyDO1fnhh8OCBf71WWdVnbS6O/BADbEu0ioUSNTQuA3G0b1zk3RP\nKHVjltvAMm4x/6BBMHt2bpVBM5k717cfufXWtFMiItVQG4kauvfQe9NOgjQgs9zgoZr2Atttl/v+\n5z+HH/84/vbqadiw7Gu1mRBpXiqREKmTYifLvn3hzDOT2cdhhyWznXq75JK0UyAicSmQEKmzQv1G\n/PSnlS1XzpprwssvR18vbZWMkioijUmBhEidJdVGophit6j26ZPsfpK2//5+wK97VSMo0lTURkIk\nBbVsE1CsJOO552CddWq332rddpt/gL+jI06JjIjUn0okROqs0hNk3GCjWInEGmvE214a1PhSpHko\nkBCpk+DkmF+1kbT8bY8aBe+9F69XzrQ89VTaKRCRSjXRX4tIa8g/0Re7+l533Xjbzw8YevWCfv38\nfpPqqrvWRoyARYvSToWIVEKBhEgKKim6P+UUOPts+Mtfom27VKCS9FDkBx+c7PbCRo3Kfb98uR9n\nREQaixpbitRZ/om+WDVH585w6qnRt1+qCmPGDD+C6FZbwSuvwLHHRt9+2Iknwg03VLeNYubNg5kz\n4Zln4PPP4Ve/8tPVfkKksSiQEKmTYm0kkj4xliqRWGst+P73/esxY+D22/3Q6NXs66yzCveDkYR9\n920/7d//hpVWqs3+Gsntt8Mqq/jvSaSRqWpDpM5qfVtjlO3PnAm/+U11+/r61+OvH0dHaTuxzz71\nz1uROBRIiKQgqbE2CokSSKy0EmyySXX7qndVw+ef13d/9fbqq7DjjrnTnGv8z33//fD662mnQtKg\nQEKkzhqto6Wdd46/bhqBxNChvnOtVnXttfDoo9n306fD5Mm+muPDD9NLVznjxrVvICsdgwIJkTpZ\nMdQiqZ7BRLkTfefOxeeVK1pPI5BYtAi23BK+8Q3fWPStt+q7/1rr1i33/eGHw1VX+dcLFtQ9ORW5\n5x7/3MiBjtSOAgmROvn97+EXv/CvBw2q336rOdFfeGHp+WkEEoG77oLLLoO11658nWXLfGPNRvGv\nf8Hf/ubvfHHOBwqlhoG/8sr6pS2KPfbwz198AVtskW5apP4iBRJmdrKZPWlmn5jZQjP7g5ltVGad\nb5nZcjNblnlebmZfVJdskeYzcCCcdJJ/Hdw5AcmciOfOhQceqH47UXXq1Bi3Yy5bVnzeV1/B3nvD\nFVf4UqGuXePt4//+z5/4o/jgAzjiCB/w3H67D7w+/TQ7v08f2HxzOOQQ/x1usEHp7V1xhU9HvvNw\npwAAHutJREFUI/X8Gf484G/blY4laonEKOBiYATwdaAzcJ+ZdSu5FiwCBoQeAyPuV6SlrLBC+av9\nKLbYAnbdtfC83XePvr0bb4TddoPBg0tXb5j5jqLSduONPqB5/HFYsiR3XpcucMcdcMwx2Wnz5/tu\nwys1bx6MHu1P/EGJxpVXwh//WHq9I46Aa67xVTD77OOn9ewJU6bATTflLjt0aGVpGT0attmm8rTX\n2v33t5+2YIEvnah3A9ElS7KBzd13w+WX13f/HVWkQMI5t4dz7lrn3IvOuXnA4cC6wPDyq7r3nXPv\nZR7vx0yvSMupdXuJ44+Pvs6ECb5/iS5d4Oqr/bSddmq/XJpVG2GHHAIPPggjR/oeQc8/36dthRUK\nL7/JJtC/v28cmH9FHTjvPLjoIt+bZri4fuFC/zx5MowfDx9/XHj911+HO+8sPO/yy2HixMo+WzHz\n51e+7Asv+Px45ZXq9llIoTYqG2wA3bvDqqsmv79SxozxgdpRR8Gee/qALQj8vvzSH9f33JNbgvX2\n2/DSS6W3+9VXpUu98l12GfzgB9HT36yqbSPRG3DAR2WWW8XM/mFm/zSzmWa2aZX7FWkZSZ+I+/TJ\nvj7ggOoDlWD9FQt0X9cogQT4kyX4HjCDKqRypSWPPOLzKAiWwF9Jm/mAZOpU34lX2MCBsOGG2fdT\nphTe9l57RUt/VJX0//HKK746JAhaHnyw9PJxvsulS4vPW7Ik2jafeSZ+49lbbvHfJ8D//m92eteu\n8OyzvhHrzTf79hxDh/rg4Kuv/PshQ3w1XbGAoksXf5yU88kn/i6oY4/N9sTaEcQOJMzMgAuBR5xz\nL5RYdD5wJDAeOCSzz8fMbK0S64i0vFqVRHTp4v+8//739sXn1ejVq/20RqnagHglLwD33QdHHuk/\nS//+8MQT5dd59dXs65tuyj35ffqpP0FFbU8R1dy5/grczN8a+vOfw+LF8NBDvgEnwEYb+Sqd55/3\n7485JnuHReDDD/2YLmuu6U+6++/vp7e1+W337Qu33dY+cBk92s8/8cTS6aykbwnn/Ml/2DDfePa4\n43xAF8WBBxafl19tNG+e/5106eLzMUjDkCE+GChk5sz2gdhHH2W/53328b+R8Ng4kyZF+wxNyzkX\n6wH8D7AAWCPieisCrwBnllhmGOCefvppJ9KqLrrIOXCurc2/939l6aQl2HehNLz5pp+2337+/XXX\nZZebP9+5++9vv35HfHzwgXOLF6ebhokTs6/nzi2+XKnvHpy7+ebC05ct8+u89FL0tB19tH8++2y/\njauvdu6003y+VZLOUpYvTzYfr7rKH/fXX+/cbbflzlu+3O/zww+z0044ofi2li1z7osvnFuyxD/e\ne8+5rl2de/1156ZPd272bOdOPdW5f/0r8s+2qKefftoBDhjmXLxzfJRHrLE2zOwSYA9glHPunYiB\ny1IzewbYsNyy06ZNo1feZdCkSZOY1GHCPJHG1r+/ejMM9O2bdgp8o9PAjBnFl3vtNX8L8ujRhecX\nu7rfbDNfgvF+jFZuV1zhn087zTfGDKofzj67+DpBiVe50ruk+xI56qji84YO9XfaXH99dtpFFxVf\nPr+dznrr+fYaAwfmTj/nHF+aEf5OHn/ct+fp3du/X7AAHn7Y9y0SaGtro62tLWdbi+rdj3zUyAO4\nBHgDWD9O5IKv2ngB+O8Sy6hEQlpeUCJxww3+fdSrsCSVuhIsViIxdap/P2tWsleDetTnscoq6ach\n6mPbbQsfv1tvnX7aknhMnuw/z7Jlzl15pZ+2yy7O3X67c2+8kV0uKBnKt3ixf9S7RCJqPxKX4ts5\nHAx8bmb9M4+uoWWmm9m5ofc/MbOxZjbIzIYC1+Nv/2zQrlVE6qPRusquVJDuoFMt59JLi8T32Wdp\np6C0b3yj/bQnn8web8uXw623+q65G6lfjWp8+aV/PuMM+Pa3/esHH/T9oIS7sn//fd8OB7JhCMD6\n6/u2LvUWtbHlFKAn8BDwdugxIbTMOvi+IgJ9gCvwpRB3AasAI51zZW64EZFGFPxpBQFFkoHEyy8n\nt61qbLdd2ino2MaPL95HR9BPyA03+DspCvVj0ayuvdZXk/zsZ+3nhasQBwzwDUXPOcffbRIMNf/O\nO8VvR66lSG0knHNlAw/n3K55708EyrTrFZFmleRdGz16JLctaV6lSuu6doWzzoKf/rR+6amnqVML\nTy90m+1pp/nnBx/MzbN6jz+jsTZEUtasVQNBugtdva+2WrxtNkp1T7N+J62iU5kzU6sGEUkZP76+\n+1MgISKR5J/sV1vNdyQU7szngw/qmyZpLY0SUEplYt3+KSIdV34bCYCttirekU8UOoGINB+VSIhI\nIvKDgMMOSycdIlJfCiREUtLsV9/F2hFstpl//uEPs9PCHeiU0ux5IsnQcdBcFEiIpKxVGvbl//kH\nn+voo6sf6VI6llb5TXQUCiREJJZyV43hk8G4cdnBnYJb1uJsE+Dkk8svUy2dyEQqp0BCREoKbuUM\netorplQQ0KmTH1bZucKd7VSyjcC55/qRGFtZoXwYPLj+6Yjq8svTToGkQXdtiKQkGJxnm23STUc5\nXbtWd4Vei/ruvfdOfpthadfRm7XP8/nzYeTIyoY5T0szBDuSPJVIiKRkiy38yWKjjdJOSTKKnXyj\nnJTTPoEHkqzamDCh/DL5CnXIZAazZ1efnloaOTLtFEgaFEiINJBWGuOh2dsZ/OEPyWyn0DDZJ58M\n++2XO61bNz8IVSmNEmgVU65HSmlN+tpFGsTixfDww2mnIr4oJRKffx5tG2kYNy6Z7VT6mTbcEIYO\nTWafaVlhhbRTUN6++6adgtajQEKkQXTtCp07p52K8oKTRf7VZ5QgYOWVk0tPMWPHll/m0kuzr4cP\nz77u0sWnMYlSlUrzpZGCqEp973u578PHxJNP1jctlSpU6nPYYTBiRHL7eOSR5LbVDBRIiEgk++/v\nB0066qjkt13tyXTUKHj+eT8ceZRtTZ8Od94J228Pp5/uh6hOSi0DiX/9C3r3hn79yi8bDpTKGTSo\nsuWOOCL3ffgzrLde5fvL99ln8dfNd+ON2dfz5/s0nnVWbs+rm2wCDzwQfdtHHgl33JE77Y47YIcd\n4qW1WSmQEJFIOneGM8/0JShhxTqkKue++4pvIyrnfM+agwdXtq1gGedgwAB49FE44wxYZ53q0hHo\n379wOpxrPz18NV9pPvTqBR9/DG++Wb5R52OPwU9+Utl2N944+7pPn8LL/Pa3pdtE9Ovn8xPggAPg\nrrvgnXdytx3IvxMlOCYGDKgsvWutVXzegQf6ABNgxcx9ij/5CcyYkbtc9+7Z+YF77y2937Fj2wdd\ne+1VPr2tRoGEiNREocG9Chk71g/6leQ+K9lveJlqqzAmTPBBA8BFF2XbOsydG69EImp6unRpH9gV\nWuaMM9pPL1QFdOONcNVV/vXy5YW3t+OO5dtEbL89vPEG3Hwz7LGHDwzC1UkAm24K225beP133im9\n/cChhxafZwY9e2Zfl5Jf5bbzzrnvBw6E44/Pvh8yJHoD0913z74Oj5jbzBRIiEgikqjjN/PVEnGF\nT8D1vIPgm9+Ed9/1I6Aefzw89RQsXVq8RKKQagKJ8Dq33FJ8mU6d4B//yJ225prtl+vZM1s8Xyot\nleTx2mvnvs/Pj3L589VX5UtbTjmlfDoq2Vf+/C5doEeP7PtwXjzyCGy5ZfTj/qqrsu0xwlVSCxZE\n204jUSAhIg1l8ODs1X1U4avnejZeDE4wPXr4/XbqVLxRav46gSSqdQA237z0cgMH5r4PrtbzBele\ntqz4tuLcpVGqSqeQFVcsX9pS7DNEFaXb96BhdJTvbdAgWGON7HbCn33QIDjmmMq31UgUSIhIIoq1\nkYjTIVWhevRKxK3aqFapq/Y4JRJx0lXo5FSJYp1IBUFCqUAiTqlP1BKJsCeeaH9nU35gFGXfUecX\n+p6rOYby173ssvKNPtvafDqeey7+fpOmQEJEairOH+155/l68z/+MTsE+Y9+VL7xW9y2DtW2kYi6\nfqE8SapEIqkqnWA7xdpIQLwSiWrS16cPLFmSOy1Kw9hyfZ2US1uhQDXp7uN33TX7ulev4uv27euf\nu3SJv/+kaKwNEUlE/p9i8Ee3xRbRt7H99tmW/CNG+KvQn/2sfD8bUUskklLqZFvoRFPuro1q2khE\n/dzF9lUukAiqcKKqpkQiMHky/O53/nWUNFRbIlFIlO8q6mcPPtuQIXD00TBtWvt1d90VVl012VuW\no1KJhIjUxMCB8Npr5UcNDSv0x9qvH1xxRWWddcVtbFnLEolSQUaYWfbqMgjCkkpDHJVccScRSMTZ\nRrjxZRIBY6kgrFwj2FoOaBfML5euTTeNn4YkqERCRBJR6M+umk6J4mjENhKVnmjMfH8IN97ouxA/\n8sh4aUnqM9WrsWWU9BbKy3qWSIT3H7yOEkjkByxJBRJpU4mEiDSMpNoJRN1WLUskKt12cEKcMMEP\n4BU3DUmdYGoViCVRtRF1/agn7mLqXSIRHBNm7T9DtbcLJ0mBhIjw8MO+i+hqNMIVUlptJJKq2qhk\ne+XSkFQbiUpOqkkEEpWUKJS6VbaSNFSaN/UKJCptGBsOJPLXbYTfW0CBhIgwahTsuWd120iqQ6pK\nFeoWOXzSDv6ES/1ZJ/VnHLWxZaF9n3VW+XVKSfqOlSh5M2ZM5R2JVTPYWyFJnlCLdWceKPQ916uN\nRP5+GmnI9gZKiohI5ebM8eM9hBUqkXj+eXj/ff96jz1qk5ZK+pE4/HD48ks46CCYOjV3mSlTcrtj\nrmeJRDFRttOjh+9IDHx31fmDeZXabrVVKHH6KSk2vZLbP/O/m0pLnArtP6k2EmmXTqixpYgkot4l\nEquvDsce63sK3G8/P61QINGtm78L4vXX/R0g4fEUkugLoNz6668P557rey1caSXfoRD42/aC7qw3\n2KDy7e22W+H+NOpdtVFs/rXXRluv2kCgkivzWrSRKHTsRC3Vq1Vjy0IlGLWkEgkRSVSlJ4Yrr4S/\n/CXeuuHl990XLrjAv19llfbbCv5Q113XBxWLF0fbR+CQQ/yw3YUEg3QVS+PJJ/t7/cOmTIFFi/y4\nHCeemDuv1FXurFmFpxcKJPLHuYiiku8i6DBp4sT42622iD5/e0OGtF+mmjYSld7+edxx0dsZ1TKQ\nqCeVSIhIKo46KrltTZvmez0cNy477dxz/R/quuvmLlto3IZKrt6uuy73/Y03+gGwTjopcnIBn7ae\nPWH48HjpKbZO+CTy9NO+7cXs2dG3F/RpUSr46tat+ivfpNtIjBgBL77oXy9cWPu0BOtU0s9J1P2F\nq1yilJ516hStyqVaKpEQkYZRzUnl8MNzR7Jcf31fjVCor4MXXoBnn61suy++mLvsRhv55wkT4gcR\n5SQVSKy+OlxyiQ8oKnXYYf65Vy/fRXm4GiU8EmZclQxW9rWvVb6NcO+P+VZfvfS+fvOb3PdRusgO\nbLklnHlm4SHa84chjzpQW6VVMmlTICEiHc6QIblDQJc6cW+yiV828NxzvsOoWioXSMyb175a6Ac/\n8O1FovaKGezr61+Hm26CGTOy877xDX9Hz8cf+yHSP/44Wk+lpfY3ZYp/zj9JXnFF5UFeeP0nnoC5\nc0sPrJW/r0mTfEnWgQcWnp8vCCLDOnWCn/4UevfOnf7ZZ3DffZWlvdz8OFV+9aRAQkQaRqNfeYFv\nMBlusFkL5QKJzTeH0aNzp+2wA7z9dvRBnIIi8AMOyJ5Q8/Xu7UsjVlghO4halFE3wzbe2Jfm/PjH\ncPDBvsQkbPLk8iUD4eMkWLZHj+LjuhRrI9G3L3z0EWy4oX9/xhm+nc3JJ2ernM47z5dsPP443H13\n2Y/3H927t6/uCPZ/+eX+s7dKIKE2EiIiDabadgdPPumrb8p55BGYP9+/rvTks8MO1aWvZ0/fvgTg\n+usrW+eHP/QlQfntXSDaXSDlPuM3v+kf4NvYgG9Eedxx2WWOP96XBm22Wfl059t8c/+81VaVffZm\nCSQilUiY2clm9qSZfWJmC83sD2ZWoLCn3XoHmtmLZrbYzOaa2e7xkywiraref4CNWgJSbSCxzTbw\nrW8Vn9+5M4wfnxsUNGpegD8BP/OMLw3K9/3v1zctG23kg5pKS6Xuu88Hdc88U3ngFAg6XTPz39fK\nK2fbXQQlTyNGtF+v0UskRgEXA09l1j0PuM/MhjjnCrbrNbORwA3Aj4C7gIOBmWY21DlXQcwsIh1F\nWoFE2mMV5Kt1epYsyb4OqjYaqafESp1yim/DEVaqjUQaxo6Nvo5zvrO1/fbzDYhXXBEGDcptm9Ot\nm+8bZa214Oabc9ffYw+47bbq0h1FpEPHObeHc+5a59yLzrl5wOHAukCBG5j+4wTgHufcBc65+c65\n04E5wHfjJlpEpJXVM7BpxkAiuEOj0p5KGy1QrMR3vuMbz557bvGSjHXX9e1WJk70DU233tqXgJx8\ncn3TWm0bid6AAz4qscxI4Fd50+4F9q5y3yLSgGo59kCtNNqJpp7paYaqjXxrrpnMGCHNoJKgwMw3\nNP3rX/37OXNqm6Z8sWNQMzPgQuCRMlUUA4D8bkEWZqaLiEgeBRLJauXP1giqKZG4FNgU2CHGuoYv\nyShp2rRp9Ar6YM2YNGkSkyZNirFLEamHvJ9sJCqR8Ap1rlQrzVi1IVltbW20BQO4ZCxatKiuaYgV\nSJjZJcAewCjn3DtlFn8X6J83bXXal1K08+tf/5phw4bFSaKIpODaa/2Q0s2iUa9Ud9rJ90ZZqPvs\npCmQaG6FLq7nzJnD8HocPBmRD51MELE3sItz7p8VrDIbyP9rGZuZLiIt5NBDfQOxuBr1xJ6GYcPq\nU1LSEao2pLYilUiY2aXAJGA88LmZBSUNi5xzX2aWmQ685Zw7JTPvIuAvZnYi/vbPSfi7PCYnkH4R\nEalCUCKhQELiilq1MQXftuGhvOlHAEEP7esAy4IZzrnZZjYJOCfzeAXYW31IiDSe7t1rP45EI2q0\nNhJhDz5YeMTSpASfvVWqNsoN+CXJixRIOOfKHmrOuV0LTLsVuDXKvkSk/t54I7ezolbXDFfh+SNI\nJq3V2khMnVr/3i47Oo21ISL/0adP2imor0bt2bKeWq1qo1UComaiLBeRDqtnT/9czS2rza7VqjYK\n+W6mH+UePdJNR6tSiYSIdFjjx8Pvf198+OyOoNWqNgDmzcstZdptt45d6lRrCiREJHXrrw/PPlv/\n/Zr5cQo6sp128s9pN1K85hpYbbVkthUM190MDjmk8PDozUSBhIik7pprfCM5qb+RIxvjar3UsOet\n7Lrr0k5B9VqoMEtEmlWPHu2HgxaR5qBAQkRERGJTICEiIiKxKZAQERGR2BRIiIiISGwKJERERCQ2\nBRIiIiISmwIJERERiU2BhIiIiMSmQEJERERiUyAhIiIisSmQEBERkdgUSIiIiEhsCiREREQkNgUS\nIiIiEpsCCREREYlNgYSIiIjEpkBCREREYlMgISIiIrEpkBAREZHYFEiIiIhIbAokREREJDYFEiIi\nIhKbAgkRERGJTYGEiIiIxKZAQkRERGJTICEiIiKxKZBoIW1tbWknoSkp36JTnsWjfItOedb4IgcS\nZjbKzO4ws7fMbLmZjS+z/E6Z5cKPZWa2evxkSyH6wcWjfItOeRaP8i065Vnji1Mi0R14FvgO4Cpc\nxwGDgQGZxxrOufdi7FtEREQayIpRV3DOzQJmAZiZRVj1fefcJ1H3JyIiIo2rXm0kDHjWzN42s/vM\nbPs67VdERERqKHKJRAzvAMcATwErAZOBh8xsW+fcs0XW6Qrw4osv1iF5rWPRokXMmTMn7WQ0HeVb\ndMqzeJRv0SnPogudO7vWY3/mXKXNHAqsbLYc2Mc5d0fE9R4CXnfOfavI/IOB62MnTERERA5xzt1Q\n653Uo0SikCeBHUrMvxc4BPgH8GU9EiQiItIiugLr4c+lNZdWILEVvsqjIOfch0DNoygREZEW9Vi9\ndhQ5kDCz7sCG+AaUAOub2ZbAR865N8zsPGDNoNrCzE4AXgP+ho+SJgO7AGMTSL+IiIikKE6JxNbA\ng/i+IRzwq8z06cCR+H4i1gkt3yWzzJrAF8BzwBjn3MMx0ywiIiINoqrGliIiItKxaawNERERiU2B\nhIiIiMTWcIGEmX3HzF4zs8Vm9riZbZN2mtJiZqcXGPDshdD8lczst2b2gZl9ama35A+GZmbrmNld\nZva5mb1rZr80s4b73qtRyUByZnZWpmfVL8zsfjPbMG9+HzO73swWmdnHZnZlpmFxeJktzOzhzLH5\nupn9sNafrVbK5ZmZXV3g2Ls7b5mOlmcnm9mTZvaJmS00sz+Y2UZ5yyTymzSznc3saTP70sxeNrOC\nfe40gwrz7aECAztemrdMh8k3M5tiZnMzv61FZvaYmf1XaH5jHWfOuYZ5ABPx/UZ8E9gEuBz4COib\ndtpSyo/T8Y1T+wGrZx6rhub/D76vjZ2Aofjbff4vNL8TMA9/L/HXgN2A94Cz0/5sCefTfwFnAfsA\ny4DxefN/lDmO9gI2B2YCrwJdQsvcA8zBNybeHngZuC40vwf+luXpwBBgAvA58O20P3+N8uxq4K68\nY69X3jIdLc/uBg7LfJavAXdmfn/dQstU/ZvE3///GfBLYGP8AIlfAWPTzoMa5tuDwGV5x9sqHTXf\ngD0zv9ENM4+zgX8DQxrxOEs9w/Iy73HgotB7A94ETko7bSnlx+nAnCLzemYOrH1D0zYGlgPbZt7v\nnjkw+oaWOQb4GFgx7c9XozxbTvuT4tvAtLy8WwxMyLwfkllvaGiZ3YClwIDM+2OBD8L5BpwHvJD2\nZ65Rnl0N3FZinU06cp5lPkvfTB7sGDquqv5NAr8AnsvbVxtwd9qfuRb5lpn2IHBBiXWUb/AhcEQj\nHmcNU8RtZp2B4cADwTTnP9mfgJFppasBDM4UP79qZteZWXBr7XD87bvh/JoP/JNsfm0HzHPOfRDa\n3r1AL2Cz2ic9fWY2CH9LcjifPgGeIDefPnbOPRNa9U/425tHhJZ52Dm3NLTMvcDGZtarRslP286Z\nouiXzOxSM1s1NG8kyrPe+M/7UeZ9Ur/J7fB5Sd4yrfI/mJ9vgUPM7H0zm2dm55pZt9C8DptvZtbJ\nzA4CVgZm04DHWcMEEvgodQVgYd70hfgTQUf0OHA4/kpvCjAIeDhTDz0AWOLaD80ezq8BFM5P6Dh5\nOgD/p1XquBqAL/b7D+fcMvwfXUfNy3vwVYy7Aifhi1DvNrOgI7oOnWeZfLgQeMQ5F7RbSuo3WWyZ\nnma2UrVpT1ORfAM/ttKhwM7AufiqkGtD8ztcvpnZ5mb2Kb704VJ8CcRLNOBxllYX2VEY/kTQ4Tjn\nwv2kP29mTwKv4+uai41BUml+dcg8Dakkn8otE5xUWy4vnXM3hd7+zczm4duV7Iwvhi6mo+TZpcCm\nwI4VLJvEb7LV8i1nrCXn3JWht38zs3eBB8xskHPutTLbbNV8ewnYEl+Csz8ww8xGl1g+teOskUok\nPsA3+uqfN3112kdNHZJzbhG+QduGwLtAFzPrmbdYOL/epX1+Bu87Sp6+i/9xlDqu3s28/w8zWwHo\nk5kXLFNoG9AB8jLzZ/4B/tiDDpxnZnYJsAews3Pu7dCsan+T5fLtE+fckmrSnqa8fCs61lLGE5nn\n8PHWofLNObfUObfAOTfHOXcqMBc4gQY8zhomkHDOfQU8DYwJpmWKwcZQx8FHGpmZrQJsgG88+DS+\nYVs4vzYC1iWbX7OBr5lZ39BmxgGLgHCxYsvKnADfJTefeuLr8cP51NvMhoZWHYMPQJ4MLTM6c7IM\njAPmZwK8lmZmawOrkR1sr0PmWeZkuDewi3Pun3mzq/1NvhhaZgy5xmWmN6Uy+VbIUPxVcfh463D5\nlqcTsBKNeJyl3RI1r8XoBHxr+vDtnx8C/dJOW0r5cT4wGhiIv73ufnzEuVpm/qX4AdF2xjfAeZT2\ntwDNxdd3b4Fva7EQ+Fnany3hfOqOLwLcCt9yeWrm/TqZ+SdljqO98LdCzQReIff2z7uBp4Bt8MWu\n84FrQ/N74gO46fii2Yn4W6eOSvvzJ51nmXm/xAdbA/F/Nk/h/4A6d+A8uxTf6n0U/koueHTNW6aq\n3yTZ2/J+gW+NfxywBPh62nlQi3wD1gdOA4ZljrfxwN+BP3fUfAPOwVebDcTfsn4ePnjYtRGPs9Qz\nrEAGHoe/P3YxPjLaOu00pZgXbfjbXxfjW+TeAAwKzV8JuBhf5PwpcDOwet421sHft/1Z5kD6BdAp\n7c+WcD7thD8ZLst7/G9omTPwJ7Uv8C2TN8zbRm/gOnzE/jHwO2DlvGW+Bvwls41/Aj9I+7PXIs/w\no/TOwpfkfAkswN+33i9vGx0tzwrl1zLgm6FlEvlNZr6fpzO//VeAw9L+/LXKN2Bt4CHg/cxxMh9/\n4lwlbzsdJt+AKzO/u8WZ3+F9ZIKIRjzONGiXiIiIxNYwbSRERESk+SiQEBERkdgUSIiIiEhsCiRE\nREQkNgUSIiIiEpsCCREREYlNgYSIiIjEpkBCREREYlMgISIiIrEpkBAREZHYFEiIiIhIbP8P4FC/\nMd0CInkAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x10b4712d0>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"%matplotlib inline\n", | |
"from matplotlib import pylab as plt\n", | |
"plt.plot(np.mean(data, axis=1)[0], label='sarsa')\n", | |
"plt.plot(np.mean(data, axis=1)[1], label='qlearn')\n", | |
"plt.legend()\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"SARSA は clif 問題に弱いと聞いたが、こういうことなのかもしれない。\n", | |
"方策オン手法であるため、next_action として大きな負の報酬を与えるものを取ってしまう可能性が残っている。\n", | |
"すると、結局安牌な方策に陥ってしまう。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 18, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"---SIM 0---\n", | |
"---SIM 10---\n", | |
"---SIM 20---\n", | |
"---SIM 30---\n", | |
"---SIM 40---\n", | |
"---SIM 50---\n", | |
"---SIM 60---\n", | |
"---SIM 70---\n", | |
"---SIM 80---\n", | |
"---SIM 90---\n", | |
"---SIM 100---\n", | |
"---SIM 110---\n", | |
"---SIM 120---\n", | |
"---SIM 130---\n", | |
"---SIM 140---\n", | |
"---SIM 150---\n", | |
"---SIM 160---\n", | |
"---SIM 170---\n", | |
"---SIM 180---\n", | |
"---SIM 190---\n", | |
"---SIM 200---\n", | |
"---SIM 210---\n", | |
"---SIM 220---\n", | |
"---SIM 230---\n", | |
"---SIM 240---\n", | |
"---SIM 250---\n", | |
"---SIM 260---\n", | |
"---SIM 270---\n", | |
"---SIM 280---\n", | |
"---SIM 290---\n", | |
"---SIM 300---\n", | |
"---SIM 310---\n", | |
"---SIM 320---\n", | |
"---SIM 330---\n", | |
"---SIM 340---\n", | |
"---SIM 350---\n", | |
"---SIM 360---\n", | |
"---SIM 370---\n", | |
"---SIM 380---\n", | |
"---SIM 390---\n", | |
"---SIM 400---\n", | |
"---SIM 410---\n", | |
"---SIM 420---\n", | |
"---SIM 430---\n", | |
"---SIM 440---\n", | |
"---SIM 450---\n", | |
"---SIM 460---\n", | |
"---SIM 470---\n", | |
"---SIM 480---\n", | |
"---SIM 490---\n" | |
] | |
} | |
], | |
"source": [ | |
"import numpy as np\n", | |
"n_sim = 500\n", | |
"n_episodes = 3000\n", | |
"data = np.zeros(shape=(2, n_sim, n_episodes))\n", | |
"for sim in range(n_sim):\n", | |
" if sim%10==0: print '---SIM %d---' % (sim)\n", | |
" sarsa = SARSA(epsilon=0.1)\n", | |
" qlearn = QLearn(epsilon=0.1)\n", | |
" for i in range(n_episodes):\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = sarsa.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[0][sim][i] = revenue\n", | |
"\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = qlearn.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[1][sim][i] = revenue\n" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 19, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"data": { | |
"image/png": 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YjIiSFJfkiHYgHBQrVgyJiYn4888/VdcOHDige2+lSpVw9epVtGnTRjfcZ599hsTERHz7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\nfGapQeYAAAAASUVORK5CYII=\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x10feceb90>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"%matplotlib inline\n", | |
"from matplotlib import pylab as plt\n", | |
"plt.plot(np.mean(data, axis=1)[0], label='sarsa')\n", | |
"plt.plot(np.mean(data, axis=1)[1], label='qlearn')\n", | |
"plt.legend()\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"$\\varepsilon$を0.01から0.1に上げてみた。すると、SARSAとQ-learningで異なる結果に収束している。SARSAは探索によるノイズも含めた価値観数を学習しているため、ひとまず回り道をする方策(平均方策=2)を学習できている。一方で、Q-learningは一定の確率で探索をするため、SARSAよりも低い収益に収束している。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 20, | |
"metadata": { | |
"collapsed": true | |
}, | |
"outputs": [], | |
"source": [ | |
"R = {\n", | |
" 's1': {\n", | |
" 'a1': ('s3', 0),\n", | |
" 'a2': ('s2', 1),\n", | |
" },\n", | |
" 's2': {\n", | |
" 'a1': ('s1', -1),\n", | |
" 'a2': ('s4', 1),\n", | |
" },\n", | |
" 's3': {\n", | |
" 'a1': ('s4', 5),\n", | |
" 'a2': ('s1', -5),\n", | |
" },\n", | |
" 's4': {\n", | |
" 'a1': ('s4', 0),\n", | |
" 'a2': ('s4', 0),\n", | |
" }\n", | |
"}" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"すこし、ミスったときの収益をなだらかにしてみる。clif 問題でなくしてみる。" | |
] | |
}, | |
{ | |
"cell_type": "code", | |
"execution_count": 21, | |
"metadata": { | |
"collapsed": false | |
}, | |
"outputs": [ | |
{ | |
"name": "stdout", | |
"output_type": "stream", | |
"text": [ | |
"---SIM 0---\n", | |
"---SIM 10---\n", | |
"---SIM 20---\n", | |
"---SIM 30---\n", | |
"---SIM 40---\n", | |
"---SIM 50---\n", | |
"---SIM 60---\n", | |
"---SIM 70---\n", | |
"---SIM 80---\n", | |
"---SIM 90---\n", | |
"---SIM 100---\n", | |
"---SIM 110---\n", | |
"---SIM 120---\n", | |
"---SIM 130---\n", | |
"---SIM 140---\n", | |
"---SIM 150---\n", | |
"---SIM 160---\n", | |
"---SIM 170---\n", | |
"---SIM 180---\n", | |
"---SIM 190---\n", | |
"---SIM 200---\n", | |
"---SIM 210---\n", | |
"---SIM 220---\n", | |
"---SIM 230---\n", | |
"---SIM 240---\n", | |
"---SIM 250---\n", | |
"---SIM 260---\n", | |
"---SIM 270---\n", | |
"---SIM 280---\n", | |
"---SIM 290---\n", | |
"---SIM 300---\n", | |
"---SIM 310---\n", | |
"---SIM 320---\n", | |
"---SIM 330---\n", | |
"---SIM 340---\n", | |
"---SIM 350---\n", | |
"---SIM 360---\n", | |
"---SIM 370---\n", | |
"---SIM 380---\n", | |
"---SIM 390---\n", | |
"---SIM 400---\n", | |
"---SIM 410---\n", | |
"---SIM 420---\n", | |
"---SIM 430---\n", | |
"---SIM 440---\n", | |
"---SIM 450---\n", | |
"---SIM 460---\n", | |
"---SIM 470---\n", | |
"---SIM 480---\n", | |
"---SIM 490---\n" | |
] | |
}, | |
{ | |
"data": { | |
"image/png": 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UTOWyq6S0JOoP1uOOgHLUZ3d0aFQfvTzdbrJGgkaQ0OQpu892hJq0zDTcvKcd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\n", | |
"text/plain": [ | |
"<matplotlib.figure.Figure at 0x10fee8a10>" | |
] | |
}, | |
"metadata": {}, | |
"output_type": "display_data" | |
} | |
], | |
"source": [ | |
"n_sim = 500\n", | |
"n_episodes = 3000\n", | |
"data = np.zeros(shape=(2, n_sim, n_episodes))\n", | |
"for sim in range(n_sim):\n", | |
" if sim%10==0: print '---SIM %d---' % (sim)\n", | |
" sarsa = SARSA(epsilon=0.1)\n", | |
" qlearn = QLearn(epsilon=0.1)\n", | |
" for i in range(n_episodes):\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = sarsa.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[0][sim][i] = revenue\n", | |
"\n", | |
" env = Environment()\n", | |
" revenue = 0\n", | |
" for _ in range(5):\n", | |
" if_end, reward = qlearn.step(env)\n", | |
" revenue += reward\n", | |
" if if_end: break\n", | |
" data[1][sim][i] = revenue\n", | |
"plt.plot(np.mean(data, axis=1)[0], label='sarsa')\n", | |
"plt.plot(np.mean(data, axis=1)[1], label='qlearn')\n", | |
"plt.legend()\n", | |
"plt.show()" | |
] | |
}, | |
{ | |
"cell_type": "markdown", | |
"metadata": {}, | |
"source": [ | |
"もう少しなだらかな報酬ならば、どちらの学習方法でもまあまあ学習できる。" | |
] | |
} | |
], | |
"metadata": { | |
"kernelspec": { | |
"display_name": "Python 2", | |
"language": "python", | |
"name": "python2" | |
}, | |
"language_info": { | |
"codemirror_mode": { | |
"name": "ipython", | |
"version": 2 | |
}, | |
"file_extension": ".py", | |
"mimetype": "text/x-python", | |
"name": "python", | |
"nbconvert_exporter": "python", | |
"pygments_lexer": "ipython2", | |
"version": "2.7.11" | |
} | |
}, | |
"nbformat": 4, | |
"nbformat_minor": 1 | |
} |
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