├── Chapter 11 └── Example_8_3_Baird_Counter.ipynb ├── Chapter 6 └── Example_6_6_Cliff_Walking.ipynb ├── Chapter 7 └── Example_7_2_n-step_TD_Methods_on_the_Random_Walk.ipynb ├── Chapter 8 ├── Example_8_1_Coarseness_of_Coarse_Coding.ipynb └── Example_8_2_Mountain_Car_Task.ipynb └── README.md /Chapter 11/Example_8_3_Baird_Counter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 3, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import matplotlib.pyplot as plt\n", 10 | "import numpy as np\n", 11 | "episodes = 1000\n", 12 | "gamma = 0.99\n", 13 | "alpha = 0.01\n", 14 | "rho_dashed = 0\n", 15 | "rho_solid = 1/(1/7)\n", 16 | "reward = 0\n", 17 | "numofstates = 7\n", 18 | "\n", 19 | "theta = [1,1,1,1,1,1,10,1]\n", 20 | "thetas = np.zeros([len(theta),episodes])\n", 21 | "value = np.zeros(7)\n", 22 | "features = np.zeros([7,8])\n", 23 | "for i in range(7):\n", 24 | " features[i][i] = 2\n", 25 | " features[i][7] = 1\n", 26 | "features[6][6] = 1\n", 27 | "features[6][7] = 2\n" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 4, 33 | "metadata": { 34 | "collapsed": true 35 | }, 36 | "outputs": [], 37 | "source": [ 38 | "def execute_action(state):\n", 39 | " #solid action\n", 40 | " if np.random.binomial(1,1/7) == 1:\n", 41 | " return 1, numofstates-1\n", 42 | " else:\n", 43 | " return 0, np.random.randint(0, numofstates-1)\n" 44 | ] 45 | }, 46 | { 47 | "cell_type": "code", 48 | "execution_count": 5, 49 | "metadata": {}, 50 | "outputs": [ 51 | { 52 | "data": { 53 | "image/png": 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REOiymZA8gczE8N2kFhF3xiqlWtq1axfvv/8+lmUF2kpLSwH41re+pd01YbT9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\nzDmiVYI+bLFGi/YjVmorDzVrb8TDx9V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54 | "text/plain": [ 55 | "" 56 | ] 57 | }, 58 | "metadata": {}, 59 | "output_type": "display_data" 60 | } 61 | ], 62 | "source": [ 63 | "for i in range(1000): \n", 64 | " state = np.random.randint(0,numofstates)\n", 65 | " action, next_state = execute_action(state)\n", 66 | " \n", 67 | " if action == 1:\n", 68 | " rho = rho_solid\n", 69 | " else:\n", 70 | " rho = rho_dashed\n", 71 | " \n", 72 | " value = np.dot(features, theta)\n", 73 | " delta = reward + gamma*value[next_state] - value[state]\n", 74 | " delta *= rho*alpha\n", 75 | " # update theta\n", 76 | " theta += features[state,:]*delta\n", 77 | " thetas[:,i] = theta\n", 78 | " state = next_state\n", 79 | "for i in range(8):\n", 80 | " plt.plot(thetas[i,:],label='theta '+ str(i+1))\n", 81 | "plt.legend()\n", 82 | "plt.show()" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "## Exercise\n", 90 | "https://classroom.udacity.com/courses/ud600/lessons/4627968925/concepts/46743885780923" 91 | ] 92 | }, 93 | { 94 | "cell_type": "code", 95 | "execution_count": 6, 96 | "metadata": {}, 97 | "outputs": [ 98 | { 99 | "name": "stdout", 100 | "output_type": "stream", 101 | "text": [ 102 | "1.42\n", 103 | "2.0626\n", 104 | "3.045778\n", 105 | "4.55004034\n", 106 | "6.8515617202\n", 107 | "10.3728894319\n", 108 | "5.22017361027\n" 109 | ] 110 | } 111 | ], 112 | "source": [ 113 | "w = np.ones(8)\n", 114 | "f = np.zeros([7,8])\n", 115 | "for i in range(7):\n", 116 | " f[i][0] = 1\n", 117 | " f[i][i+1] = 2\n", 118 | "f[6][0] = 7\n", 119 | "f[6][7] = 1\n", 120 | "\n", 121 | "for i in range(7):\n", 122 | " v = np.dot(f,w) \n", 123 | " delta = 0.9 * v[6] - v[i]\n", 124 | " w[0] += 0.1 * delta * f[i][0]\n", 125 | " w[i+1] += 0.1 * delta * f[i][i+1]\n", 126 | " print(w[0])\n" 127 | ] 128 | }, 129 | { 130 | "cell_type": "code", 131 | "execution_count": null, 132 | "metadata": { 133 | "collapsed": true 134 | }, 135 | "outputs": [], 136 | "source": [] 137 | } 138 | ], 139 | "metadata": { 140 | "kernelspec": { 141 | "display_name": "Python 3", 142 | "language": "python", 143 | "name": "python3" 144 | }, 145 | "language_info": { 146 | "codemirror_mode": { 147 | "name": "ipython", 148 | "version": 3 149 | }, 150 | "file_extension": ".py", 151 | "mimetype": "text/x-python", 152 | "name": "python", 153 | "nbconvert_exporter": "python", 154 | "pygments_lexer": "ipython3", 155 | "version": "3.6.1" 156 | } 157 | }, 158 | "nbformat": 4, 159 | "nbformat_minor": 2 160 | } 161 | -------------------------------------------------------------------------------- /Chapter 6/Example_6_6_Cliff_Walking.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import random\n", 13 | "import matplotlib.pyplot as plt" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "alpha = 0.5\n", 25 | "epsilon = 0.1\n", 26 | "gamma = 1\n", 27 | "x_length = 12\n", 28 | "y_length = 4" 29 | ] 30 | }, 31 | { 32 | "cell_type": "code", 33 | "execution_count": 3, 34 | "metadata": { 35 | "collapsed": true 36 | }, 37 | "outputs": [], 38 | "source": [ 39 | "def observe(x,y,a):\n", 40 | " goal = 0\n", 41 | " if x == x_length - 1 and y == 0:\n", 42 | " goal = 1\n", 43 | " if a == 0:\n", 44 | " y += 1\n", 45 | " if a == 1:\n", 46 | " x += 1\n", 47 | " if a == 2:\n", 48 | " y -= 1\n", 49 | " if a == 3:\n", 50 | " x -= 1\n", 51 | " \n", 52 | " x = max(0,x)\n", 53 | " x = min(x_length-1, x)\n", 54 | " y = max(0,y)\n", 55 | " y = min(y_length-1, y)\n", 56 | "# the last step still has four directions to choose, \n", 57 | "# it need next state to update q, and reward is -1, but after the last step, \n", 58 | "# it doesn't need to take the next state and action\n", 59 | " if goal == 1:\n", 60 | " return x,y,-1\n", 61 | " if x>0 and x= q_max:\n", 194 | " q_max = q[x][y][i]\n", 195 | " a_max = i\n", 196 | " a = a_max\n", 197 | " return a\n", 198 | "\n", 199 | "def max_q(x,y,q):\n", 200 | " q_max = q[x][y][0]\n", 201 | " a_max = 0\n", 202 | " for i in range(4):\n", 203 | " if q[x][y][i] >= q_max:\n", 204 | " q_max = q[x][y][i]\n", 205 | " a_max = i\n", 206 | " a = a_max\n", 207 | " return a" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": 7, 213 | "metadata": { 214 | "collapsed": true 215 | }, 216 | "outputs": [], 217 | "source": [ 218 | "#sarsa on-policy\n", 219 | "def sarsa_on_policy(q):\n", 220 | " runs = 20\n", 221 | " rewards = np.zeros([500])\n", 222 | " for j in range(runs):\n", 223 | " for i in range(500):\n", 224 | " reward_sum = 0\n", 225 | " x = 0\n", 226 | " y = 0\n", 227 | " a = epsilon_policy(x,y,q,epsilon)\n", 228 | " while True:\n", 229 | " [x_next,y_next] = actionDestination[x][y][a]\n", 230 | " reward = actionRewards[x][y][a]\n", 231 | " reward_sum += reward\n", 232 | " a_next = epsilon_policy(x_next,y_next,q,epsilon)\n", 233 | " q[x][y][a] += alpha*(reward + gamma*q[x_next][y_next][a_next]-q[x][y][a])\n", 234 | " if x == x_length - 1 and y==0:\n", 235 | " break\n", 236 | " x = x_next\n", 237 | " y = y_next\n", 238 | " a = a_next\n", 239 | " rewards[i] += reward_sum\n", 240 | " rewards /= runs\n", 241 | " avg_rewards = []\n", 242 | " for i in range(9):\n", 243 | " avg_rewards.append(np.mean(rewards[:i+1]))\n", 244 | " for i in range(10,len(rewards)+1):\n", 245 | " avg_rewards.append(np.mean(rewards[i-10:i]))\n", 246 | " return avg_rewards" 247 | ] 248 | }, 249 | { 250 | "cell_type": "code", 251 | "execution_count": 8, 252 | "metadata": {}, 253 | "outputs": [], 254 | "source": [ 255 | "def q_learning(q):\n", 256 | " runs = 20\n", 257 | " rewards = np.zeros([500])\n", 258 | " for j in range(runs):\n", 259 | " for i in range(500):\n", 260 | " reward_sum = 0\n", 261 | " x = 0\n", 262 | " y = 0\n", 263 | " while True:\n", 264 | " a = epsilon_policy(x,y,q,epsilon) \n", 265 | " x_next, y_next,reward = observe(x,y,a)\n", 266 | " a_next = max_q(x_next,y_next,q)\n", 267 | " reward_sum += reward\n", 268 | " q[x][y][a] += alpha*(reward + gamma*q[x_next][y_next][a_next]-q[x][y][a])\n", 269 | " if x == x_length - 1 and y==0:\n", 270 | " break\n", 271 | " x = x_next\n", 272 | " y = y_next\n", 273 | " rewards[i] += reward_sum\n", 274 | " rewards /= runs\n", 275 | " avg_rewards = []\n", 276 | " for i in range(9):\n", 277 | " avg_rewards.append(np.mean(rewards[:i+1]))\n", 278 | " for i in range(10,len(rewards)+1):\n", 279 | " avg_rewards.append(np.mean(rewards[i-10:i]))\n", 280 | " return avg_rewards" 281 | ] 282 | }, 283 | { 284 | "cell_type": "code", 285 | "execution_count": 9, 286 | "metadata": {}, 287 | "outputs": [], 288 | "source": [ 289 | "q = np.zeros([12,4,4])\n", 290 | "sarsa_rewards = sarsa_on_policy(q)" 291 | ] 292 | }, 293 | { 294 | "cell_type": "code", 295 | "execution_count": 10, 296 | "metadata": {}, 297 | "outputs": [], 298 | "source": [ 299 | "qq = np.zeros([12,4,4])\n", 300 | "q_learning_rewards = q_learning(qq)" 301 | ] 302 | }, 303 | { 304 | "cell_type": "code", 305 | "execution_count": 11, 306 | "metadata": {}, 307 | "outputs": [ 308 | { 309 | "data": { 310 | "image/png": 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1rU67AHsCIcRlQdAVo/tHIS5Mi/9cmotzhyfZ7A/V+mP60ESnrdrJA+Ow5l/T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\ncRnrxnsCCzxmGVBKdQBUbWtK6XMAnvPUZ1ujM5p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311 | "text/plain": [ 312 | "" 313 | ] 314 | }, 315 | "metadata": {}, 316 | "output_type": "display_data" 317 | } 318 | ], 319 | "source": [ 320 | "plt.plot(range(len(sarsa_rewards)),sarsa_rewards,label=\"sarsa\")\n", 321 | "plt.plot(range(len(sarsa_rewards)),q_learning_rewards,label=\"q learning\")\n", 322 | "plt.ylim(-100,0)\n", 323 | "plt.legend(loc=\"lower right\")\n", 324 | "plt.show()" 325 | ] 326 | }, 327 | { 328 | "cell_type": "code", 329 | "execution_count": 12, 330 | "metadata": {}, 331 | "outputs": [ 332 | { 333 | "name": "stdout", 334 | "output_type": "stream", 335 | "text": [ 336 | "Sarsa Optimal Policy\n", 337 | "→ → → → → → → → → → → ↓ \n", 338 | "→ ↑ ↑ ↑ ↑ ↑ → → ↑ ← ↑ ↓ \n", 339 | "↑ ← ↑ ↑ ↑ ← ← ↑ ↑ ↑ → ↓ \n", 340 | "↑ ← ← ← ← ← ← ← ← ← ← ← \n", 341 | "\n", 342 | "Q-learning Optimal Policy\n", 343 | "← ↓ → → → ↓ → → → ↓ → ↓ \n", 344 | "↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ \n", 345 | "→ → → → → → → → → → → ↓ \n", 346 | "↑ ← ← ← ← ← ← ← ← ← ← ← \n", 347 | "\n" 348 | ] 349 | } 350 | ], 351 | "source": [ 352 | "def OptimalPolicy(q):\n", 353 | " for j in range(y_length-1,-1,-1):\n", 354 | " for i in range(x_length):\n", 355 | " a = max_q(i,j,q)\n", 356 | " if a == 0:\n", 357 | " print(\"↑ \",end = \"\")\n", 358 | " if a == 1:\n", 359 | " print(\"→ \",end = \"\")\n", 360 | " if a == 2:\n", 361 | " print(\"↓ \",end = \"\")\n", 362 | " if a == 3:\n", 363 | " print(\"← \",end = \"\")\n", 364 | " print(\"\")\n", 365 | " print(\"\")\n", 366 | "print(\"Sarsa Optimal Policy\")\n", 367 | "OptimalPolicy(q)\n", 368 | "print(\"Q-learning Optimal Policy\")\n", 369 | "OptimalPolicy(qq)" 370 | ] 371 | }, 372 | { 373 | "cell_type": "code", 374 | "execution_count": 13, 375 | "metadata": { 376 | "collapsed": true 377 | }, 378 | "outputs": [], 379 | "source": [ 380 | "def OptimalPath(q):\n", 381 | " x = 0\n", 382 | " y = 0\n", 383 | " path = np.zeros([x_length,y_length]) - 1\n", 384 | " end = 0\n", 385 | " exist = np.zeros([x_length,y_length])\n", 386 | " while (x != x_length-1 or y != 0) and end == 0:\n", 387 | " a = max_q(x,y,q)\n", 388 | " path[x][y] = a\n", 389 | " if exist[x][y] == 1:\n", 390 | " end = 1\n", 391 | " exist[x][y] = 1\n", 392 | " x,y,r = observe(x,y,a)\n", 393 | " for j in range(y_length-1,-1,-1):\n", 394 | " for i in range(x_length):\n", 395 | " if i == x_length-1 and j == 0:\n", 396 | " print(\" G \",end = \"\")\n", 397 | " continue\n", 398 | " a = path[i,j]\n", 399 | " if a == -1:\n", 400 | " print(\" 0 \",end = \"\")\n", 401 | " elif a == 0:\n", 402 | " print(\"↑ \",end = \"\")\n", 403 | " elif a == 1:\n", 404 | " print(\"→ \",end = \"\")\n", 405 | " elif a == 2:\n", 406 | " print(\"↓ \",end = \"\")\n", 407 | " elif a == 3:\n", 408 | " print(\"← \",end = \"\")\n", 409 | " print(\"\") " 410 | ] 411 | }, 412 | { 413 | "cell_type": "code", 414 | "execution_count": 14, 415 | "metadata": {}, 416 | "outputs": [ 417 | { 418 | "name": "stdout", 419 | "output_type": "stream", 420 | "text": [ 421 | " 0 → → → → → → → → → → ↓ \n", 422 | "→ ↑ 0 0 0 0 0 0 0 0 0 ↓ \n", 423 | "↑ 0 0 0 0 0 0 0 0 0 0 ↓ \n", 424 | "↑ 0 0 0 0 0 0 0 0 0 0 G \n" 425 | ] 426 | } 427 | ], 428 | "source": [ 429 | "OptimalPath(q)" 430 | ] 431 | }, 432 | { 433 | "cell_type": "code", 434 | "execution_count": 15, 435 | "metadata": {}, 436 | "outputs": [ 437 | { 438 | "name": "stdout", 439 | "output_type": "stream", 440 | "text": [ 441 | " 0 0 0 0 0 0 0 0 0 0 0 0 \n", 442 | " 0 0 0 0 0 0 0 0 0 0 0 0 \n", 443 | "→ → → → → → → → → → → ↓ \n", 444 | "↑ 0 0 0 0 0 0 0 0 0 0 G \n" 445 | ] 446 | } 447 | ], 448 | "source": [ 449 | "OptimalPath(qq)" 450 | ] 451 | }, 452 | { 453 | "cell_type": "code", 454 | "execution_count": null, 455 | "metadata": { 456 | "collapsed": true 457 | }, 458 | "outputs": [], 459 | "source": [] 460 | } 461 | ], 462 | "metadata": { 463 | "anaconda-cloud": {}, 464 | "kernelspec": { 465 | "display_name": "Python 3", 466 | "language": "python", 467 | "name": "python3" 468 | }, 469 | "language_info": { 470 | "codemirror_mode": { 471 | "name": "ipython", 472 | "version": 3 473 | }, 474 | "file_extension": ".py", 475 | "mimetype": "text/x-python", 476 | "name": "python", 477 | "nbconvert_exporter": "python", 478 | "pygments_lexer": "ipython3", 479 | "version": "3.6.1" 480 | }, 481 | "toc": { 482 | "colors": { 483 | "hover_highlight": "#DAA520", 484 | "running_highlight": "#FF0000", 485 | "selected_highlight": "#FFD700" 486 | }, 487 | "moveMenuLeft": true, 488 | "nav_menu": { 489 | "height": "12px", 490 | "width": "252px" 491 | }, 492 | "navigate_menu": true, 493 | "number_sections": true, 494 | "sideBar": true, 495 | "threshold": 4, 496 | "toc_cell": false, 497 | "toc_section_display": "block", 498 | "toc_window_display": false, 499 | "widenNotebook": false 500 | } 501 | }, 502 | "nbformat": 4, 503 | "nbformat_minor": 1 504 | } 505 | -------------------------------------------------------------------------------- /Chapter 8/Example_8_1_Coarseness_of_Coarse_Coding.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": { 7 | "collapsed": true 8 | }, 9 | "outputs": [], 10 | "source": [ 11 | "import numpy as np\n", 12 | "import random\n", 13 | "import matplotlib.pyplot as plt" 14 | ] 15 | }, 16 | { 17 | "cell_type": "code", 18 | "execution_count": 2, 19 | "metadata": { 20 | "collapsed": true 21 | }, 22 | "outputs": [], 23 | "source": [ 24 | "square_left = 0\n", 25 | "square_right = 2\n", 26 | "domain = square_right - square_left" 27 | ] 28 | }, 29 | { 30 | "cell_type": "code", 31 | "execution_count": 15, 32 | "metadata": { 33 | "collapsed": true 34 | }, 35 | "outputs": [], 36 | "source": [ 37 | "def square_wave(x):\n", 38 | " if x > 0.5 and x < 1.5:\n", 39 | " return 1\n", 40 | " else:\n", 41 | " return 0\n", 42 | "\n", 43 | "def sample(n):\n", 44 | " samples = []\n", 45 | " for i in range(n):\n", 46 | " x = np.random.uniform(square_left,square_right)\n", 47 | " y = square_wave(x)\n", 48 | " samples.append([x,y])\n", 49 | " return samples" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 11, 55 | "metadata": { 56 | "collapsed": true 57 | }, 58 | "outputs": [], 59 | "source": [ 60 | "class value:\n", 61 | " def __init__(self, width, alpha = 0.2, features = 50):\n", 62 | " self.width = width\n", 63 | " self.features = features\n", 64 | " self.weights = np.zeros(features)\n", 65 | " self.step = (domain - width)/(features - 1)\n", 66 | " self.alpha = alpha\n", 67 | " \n", 68 | " def get_value(self, x):\n", 69 | " featuresToUse = []\n", 70 | " for i in range(self.features):\n", 71 | " if x >= square_left + i*self.step and x < square_left + i*self.step + self.width:\n", 72 | " featuresToUse.append(i)\n", 73 | " return np.sum(self.weights[featuresToUse])\n", 74 | " \n", 75 | " def update_value(self, delta, x):\n", 76 | " featuresToUse = []\n", 77 | " for i in range(self.features):\n", 78 | " if x >= square_left + i*self.step and x < square_left + i*self.step + self.width:\n", 79 | " featuresToUse.append(i)\n", 80 | " delta *= self.alpha / len(featuresToUse)\n", 81 | " for i in featuresToUse:\n", 82 | " self.weights[i] += delta" 83 | ] 84 | }, 85 | { 86 | "cell_type": "code", 87 | "execution_count": 12, 88 | "metadata": { 89 | "collapsed": true 90 | }, 91 | "outputs": [], 92 | "source": [ 93 | "def update(samples, vf):\n", 94 | " for x, y in samples:\n", 95 | " delta = y - vf.get_value(x)\n", 96 | " vf.update_value(delta, x)\n" 97 | ] 98 | }, 99 | { 100 | "cell_type": "code", 101 | "execution_count": 19, 102 | "metadata": {}, 103 | "outputs": [ 104 | { 105 | "data": { 106 | "image/png": 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Aaa1azbt9Dkp6AHN6mkomjeXwUnaqFNRRU8UATGcA99atLMxXGteZyE8wXZzm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qRs8dIPzKK9FHRFD+zDNt2hs2b+bAvJ9ilC7i08LI31bZ4XyDMHBb4r3YdvTl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0gdx+9ulVuak9OHd4L164fgK7CqqYv2g9pdUBLvCDzmOStQdZNflU\n1BQ1HD7iljmS4kIRGChx7wZkFmfSgwg0w40lvAXb9b9/zpy1T/DfhqVXvtvH37/cxZUTEnhg1nC/\n9Xu6c87wGF5cMIHswirmL15PSSALvBBMHnMTPiFI/f6pHw87HOh48Ko9TAFFi8RdCDFTCLFDCJEt\nhHigiTbThRCbhRBZQojV/jVT0RayirLoJaLRfG600GZm4kXZsPMzSL4FrC1w4bSADzfn8fDHWZw/\nohd/vGLU6Z0xsR2YPiyGRQsmsruwivmL1gW0wI8ZcwNBUrB+18dmdlDMhWKLDl5Dfa6BRLPiLoTQ\ngeeAWUAicI0QIvG4NhHA88AlUsok4Kp2sFXRCqrcVeyr2EekjEA33Ohhzczc1z0Puh2Sb/bL+Gt2\nFvLLdzNI7t+DZ+ePw3Iab0pqT84e2pPFCyayt6ia+YvWUVwVmGGFVouNcZFDSdU9sPmNhuMWq8CQ\n6rMNJFryaaQA2VLKPVJKN/A2cOlxbeYD70sp9wNIKQ/710xFazmyXTxEBqP73GghJxH3mhLY/CaM\nvhpCYto89qb9pdz+WjqDY0JZvHAiDutpENPdiUwb2pOXFiY3bOwK1EXW5AEzyLbZKPnmafCaNlpt\nOj50fIbyuwcKLRH3eODAUa9z648dzVAgUgixSgiRLoRY0FhHQojbhBBpQoi0wsLC1lmsOCWyirIA\nsBuO5mfuaUvAW2vma28juwoqufHlVGLC7LxyUzJhji5Qgq4DOHNINC8tTGZPUTXXv7SBirrAix2f\nGJsMQLq7qGH2bnWYX9wetxL3QMFfz1EWYAIwB5gBPCiEGHp8Iynli1LKiVLKiT179vTT0IqTkVmc\nSXxIPNIDmnESn7vXBRtehEHnQcyINo2ZV1bLgiUbsOoar900iZjQ7hXH3lbOHBLNv64bz/ZDFdyw\nZANVrsCKH0+KTiLIEsSG6D6w1py9W+u/vD11alU1UGiJuOcBfY56nVB/7GhygeVSymopZRGwBmhF\nRQiFv8kqyiIxKhGv20D3nWTmnvUBVBXAlJ+2abziKhfXv7SeKpeXV25Mabcapl2dc4f34h/XjCcj\nt5ybXk6lNoDS6Vo1K+NjxpMWEgHl+yHjLazBZjoCbwDZ2d1pibinAkOEEAOEEDZgHvDRcW0+BM4U\nQliEEE5gEnBiblBFh1LuKie3KpekqCS8HoluuNFCmxD3DS9A1BBz5t5Kqlxebnw5lbzSWl5amExi\nnP9i5LsjM0fG8te5Y0nbV8Ktr6ZR5wkc4ZwYO5Hs2kMU9x4N3/0Dq9MUd3eArhN0R5oVdymlF7gb\nWI4p2O9IKbOEEHcIIe6ob/MD8DmwBdgALJZSZraf2YqWkFVs+tuTopPwekHHi2ZvJC96bjrkpUPK\nbWYekVbg8hrc/loaWQcreG7+eFIGnKbl5QKMS8bE8ecrx/BNdhF3vp4eMNkkk4/43YefC8W7sMkS\nAFxllZ1pluIoWuRzl1J+KqUcKqUcJKV8vP7Yv6SU/zqqzV+klIlSypFSyr+1l8GKlrOteBuA6ZYx\nBHpTwSobXgRbCIyZ16pxDJ/k5//O4NvsYv70k9Gcn+i/XDQKuHJCAk9cPoqvdxRyz1sb8QRAREpi\nVCJBliBSbTo4o7GVbwXAXarEPVBQgaldmKyiLPqF9SPMFoZXalgaS/BcVQhZ78PY+a1KNSCl5Hcf\nZvLJ1nx+PXs4V05IaLvhihOYP6kvD12cyPKsAn7+TkanF/ywalbG9xpP2uFNMOGGH8W9vKpT7VL8\niBL3LkxWsbmYKqXEkBoWayMf98aXwXBD8q2tGuOvK3byxvr93H72QG6bpvLFtCc3njGA/5s1nI8z\nDvK/723B18kCn9wrmeyybIpHXYHdZm66clfUdqpNih9R4t5FKa4tJr86n6SoJAyvD4SG9fjCEIYH\nUpfAwHOg5wmRq83y8rd7eearbK6emMADM1W+mI7g9rMH8bPzh/Kfjbn85oNMpOw8gT/id0+rycU+\neBwA7nLllgkUVCWmLsoRf3tSVBLe+lSsVsdxH/f2T6DyIMx58pT7N/PFbOOCxF48cbnKF9OR3Hve\nYFxeg+dX7cZu0Xjo4sRO+f2PiBqB0+Ik9VAqZ02dC9vBnb+nw+1QNI4S9y5KZnEmAsGIqBF4Ks0Q\nOsvxu0Q3LIKIvjB05in1vWrHYX7xTgaTBvTgH9eofDEdjRCCX80Yhsvr46Vv9mK3ajwwc3iHC7xV\nszK652i2Fm3Ffv6vgG/xFOaZ9QDUl32no+7KLkp6QTpDIocQbA3GU7/D0XZ0cexDmZDzjZn9UWt5\nzpf0nFLufH0jQ3uFskjli+k0hBD8ds4IrpvclxdW7+GvK3d1ih1JUUnsLN2JYRVoPo9ZjWn/iUW0\nFR2PEvcuiNtwk3E4g5TYFAA8VWZVeqvzqDQAqYvA4oBx17e4350FldzUkC8mReWL6WSEEDxyyUiu\nnpjAM1/u4rmvszvchqToJLw+LztLd6JLDx6c5oY4Raej3DJdkMyiTOqMOibGTgTAVR97bA2pF/fa\nUtjyDoy6Cpwt22yUW1rDgpc2YLOY+WJ6hjayGUrR4Wia4A9XjMbl9fGX5TuwWzRuOWtgh42fFJUE\nmGG3Og68lh7ww/NQcRDC4jrMDsWJqJl7FyT1UCoCwcReprh7KmsAsIXW53nZ9AZ4aswdqS2guMrF\ngpc2UO328upNKl9MoKFrgqeuGsOskbE89skPvLYup8PG7h3cm0h7JFnFWViEgUcLBp8BaUs7zAZF\n4yhx74KkHkplWI9hhNvDAXCXVwNgC3eaN17qIug7BXqPbravhnwxZbUsuSGZEb1VvphAxKJr/H3e\nOM4fEcODH2TyTuqB5i/yA0IIkqKTTHHXJIbPAkMuhPSlDZWaFJ2DEvcuhttws7lwc8OsHcBdZW4s\nsYYFQ/ZKKN0HKc1vWnJ5DW571cwX8/y140nur/LFBDI2i8Zz145n2tCe3P/+Fj7cfHzy1vYhKSqJ\n3WW70XWJ1yfMJ8LqQth2fH5BRUeixL2LsbVoKy7D1bDBBMBdaYq7PTIE1r8AIbEw4pKT9mP4JPe9\nvZnvdhfz55+M5rwRKl/M6YDdovPCdROYPCCKn7+Twadb89t9zKSoJAxp4NM8eKUOg86FHoPUwmon\no8S9i3HE3z6h14SGY94a8/HYRhns/hIm3gR605EuUkoe/DCTzzIP8ds5I/iJyhdzWhFk01m8cCLj\n+kRw71ubWLmtoF3HS4o2F1W9mgsD3dw1m3Ir5KZC3sZ2HVvRNErcuxjH+9vhxxzb9j3vgWaFCTec\ntI+nvtjJm+v3c+f0QR0aeaHwH8F2C0tvTCYpLoyfvrGR1Tvbr6xljDOGnkE9qRW1GLodX3W1mYjO\nGmxulFN0CkrcuxAuw0VGYcYxLhkAb60H4fNi2f5vSLoMQpt2sSz5Zi/Pfp3NvOQ+/O+MYe1tsqId\nCXVYefWmSQyOCeG2V9P4bndRu42VFJVEFRX4NCtGRSU4ws0U0pn/ger2G1fRNErcuxBbC+v97b2O\nFXePy4vucyPcFZBye5PXf7Apj0eWbWNGUi8eu2ykyhfTBQh3Wnnt5hT6RTm5+eU00vaVtMs4idGJ\nlFNhztwrK8yDKbeB4YKNr7TLmIqTo8S9C5FaYPrbx/caf8xxj8tA97mg91hImNjotV/vOMwv381g\nysAo/j5P5YvpSkSF2Hn9lkn0Dndww9JUNh8o8/sYSVFJ1Fnc+HQb3vJ6cY8ZDgOmmZlHjcAq8t0d\nUHdwF2JD/gaG9xh+jL8dwFPnwiJdMOn2RhM6peeUcOfr6QyLDeXFBRNUvpguSEyogzdvnUyPYBsL\nXlpP1sFyv/afFJVEndVc23EfXWov5XaoyIUdn/p1PEXzKHHvItR4athcuJnJvSefcM7r9mKRbki6\n4oRzOw5VcuPSVGLDHLx8YwqhKl9MlyU23MGbt04ixG7husXr2XHIf7nXo4Ki0IPri2SXHVWNaehM\nCO9jlnJUdChK3LsImw5vwuvzMqn3pGNPlO3H69OxWAVYHcecOlBSw4Il63FYdV67WeWL6Q4kRDp5\n89bJWHWNaxevZ3eh/8ri9YjqCYC7ovrHg7oFkm+GfWvh8A9+G0vRPErcuwjr8tdh1ayMixl37InU\nlzCEHUtQ0DGHi6pcLFiygVq3was3p9Cnh8oX013oHx3Mm7dOBiTXLlrP/uIav/Qb36s/AGUlx0XH\njFsAul3N3jsYJe5dhPX56xnTcwxO61Ei7alFpr2Kodmx2G0NhyvrPNywdAP55bUsvTGZ4bEqX0x3\nY3BMCK/fMok6r8E1i9aRV9b22qeDepk1dAuLDh17IjjKzECa8TbU+n8xV9E4Sty7AKV1pWwv2X6i\nSybzfYyKMgzd1lBir85jcNur6fyQX8k/r53AhH4qX0x3ZXhsGK/fPImKOg/zF62joKKuTf3179EH\ngIqyRsItU241M5FufrNNYyhajhL3LsCGQxuQyGMXU6WEDS/gCxmEodmwBVka8sV8v6eYJ68azTnD\nYzrPaEVAMDI+nFduSqGo0sX8ResorGx9Jke7w3w6rK1uxM0TNxb6TDIzkvp8rR5D0XKUuHcB1uev\nJ9gazMjokT8ezE2F/AyMgZdi6HYsQTZ+89+tfJ51iAcvSuTycSpfjMJkfN9Ilt6YwsGyOq5bvJ6S\naner+rHazBBao86g0t1IJE7KbVCyx8xvpGh3lLh3Adbnr2dir4lYtKMKa333DNjD8fSchE+3saPM\nxdupB7jrnEHcfOaAzjNWEZCkDOjB4oUT2VtczfUvrae81nPKfVjqxd1m2MgozDixwYhLIKSXmZlU\n0e60SNyFEDOFEDuEENlCiAdO0i5ZCOEVQlzpPxMVJ+Ng1UH2V+4/1iVzKBN++Bgm34G7yrxJ0w9X\nc01KX355ocoXo2icMwZH88L1E9hZUMnCJRuocp3arlKLzZQTq2FjY0Ej2SAtNjMjafYKKOqcgt7d\niWbFXQihA88Bs4BE4BohRGIT7f4EfOFvIxVNsz5/PcCxi6mr/wT2MJh8J5syzYo88XE9VL4YRbOc\nMyyG5+aPJzOvnJuWplLjbrnAW+31M3efg42Hm0j1O/EmMyxy3fP+MFdxEloyc08BsqWUe6SUbuBt\n4NJG2t0D/Ac47Ef7FM3wff73RDmiGBwx2DxQkAU/fASTbuerHDdrMkxx/8m0weiaEnZF81yYFMvf\n5o0lLaeEW15Jo85jtOi6I24ZIS1kFW7FbTTiuw+JgdFXw+a3oLrYn2YrjqMl4h4PHF2QMbf+WANC\niHjgcuCfJ+tICHGbECJNCJFWWNh++aW7C16fl+8OfscZ8Wf8OCNf/WewhbIpfj4/fWMjcVbzeFCI\n4yQ9KRTHctHoOJ68agzf7ynmjtfTcXmbF3hNE2hC4tNsiFoX24q3Nd5wyl3grYX0JX62WnE0/lpQ\n/Rtwv5TypDFOUsoXpZQTpZQTe/bs6aehuy9bCrdQ7ipnWsI080DBNtj2AYdGLOT6N3cRFx5ESqy5\nqcnqUMnAFKfGFeMTeOLyUazaUcjdb27CYzQfwmjRTXF3usyUGI0SMwIGnWcW8lBFtNsNS/NNyAP6\nHPU6of7Y0UwE3q6fPUYDs4UQXinlB36xUtEoq3NXYxEWpsZNNQ+s+TOGNZirMsYRGWzljVsnkf9/\nq4Efw9TaisfjITc3l7q6tm14UXQuDoeDhIQErNaTJ4q7JqUvbq+Phz7K4r63N/P3eWNPmg7aYhUY\nup3Blt5sLNjIjSNvbLzhlLvg9SvMYh5j57flrSiaoCXingoMEUIMwBT1ecAxn4aUsiG2TgjxMrBM\nCXv7syZ3DeN7jSfUFgoF25BZH/CyuBzDHsm/b5lM7/AgcqpdEPTjYldbyc3NJTQ0lP79+6vF2dMU\nKSXFxcXk5uYyYEDzYbELp/bH7fXx+Kc/YLNoPHnVmCbXbyxWDUO3MjZoKG8UpOHxebBqjXyBDDoX\neo6A75+DMdc0mopa0TaadctIKb3A3cBy4AfgHSlllhDiDiHEHe1toKJx8qryyC7LbnDJVH3xONU4\neFu/hDdvndyQCMxbH698JEytrdTV1REVFaWE/TRGCEFUVNQpPX3dOm0gv7xwKP/dlMdv/rsVn082\n2s5q0zE0O2ODhlDlqWLz4c1NGQFn3AsFmWYAgMLvtGTmjpTyU+DT4479q4m2N7TdLEVzrMldA8DZ\nCWeTvyOd3ruXsVj8hH/edj79o4Mb2nnqY5X9NXMHlLB3AVrzGd597hDqPD6e/Tobm0Xj95ckndCP\nxWHBpdsYao3C4rawJnfNCTV9Gxg9F759Blb+HobNBl3VEvAnaofqacrq3NX0C+uHVfbih3//hiqC\nmLbgIQbHhB7TzuMyF8EsfvK5K7o3v7hwKLeeNYBXv8/h8U9+QMpjZ/DWICs+3Yal2s2EXhNYm7u2\n6c40Hc5/CEp2w8ZX29ny7ocS99OQGk8NqfmpTOg5ld++8Dbn+r6nZuwtDB3Q75h2Prcbr6zfNejH\nmXt3Yvr06aSlpfm1z5kzZxIREcFFF110zPG9e/cyadIkBg8ezNy5c3G7W5fjpT0RQvDr2SNYMKUf\ni7/Zy1Nf7DzmvDXIhqHZ8FVVMi1+GrvLd5NXdXz8xVEMnQl9p8KqP4LLf4VDFErcT0vW56/H7XPz\n1cYorql5C8MaQsyFPz+hna+83Iw5FhLdoj7qQOFXv/oVr7322gnH77//fn72s5+RnZ1NZGQkL730\nUidY1zxCCB6+OIl5yX149utsnvs6u+Gc1WHBZ3FgVFQ2rAeddPYuBFzwe6g+rHat+pkW+dwVgcWK\nfV8jpIPIQoML9fUw+ZfgPDEvu1FejqHbsbbTp/z7j7PYdrDCr30mxoXx0MVJTZ6vrq7m6quvJjc3\nF8MwePDBB5k7dy6PPPIIH3/8MbW1tUydOpUXXngBIQTTp09n3LhxrF27lurqal599VX+8Ic/sHXr\nVubOnctjjz3Gvn37mDlzJhMmTGDjxo0kJSXx6quv4nQeW53qiy++4KGHHsLlcjFo0CCWLl1KSEgI\nDzzwAB999BEWi4ULL7yQJ5988qTv8bzzzmPVqlXHHJNS8tVXX/Hmm2a+84ULF/Lwww9z5513tu4X\n2c5omuDxy0dR5zH4y/IdRDitXDupHxa7jmGxY1RW0C+sH31C+7A2by3zhs9rurM+KTDiYvj27zDh\nBnMXq6LNqOncaUZxdS2f7F6Jt2ooSwasNnPITLmr0bZGRQWGbsNi6ToLoJ9//jlxcXFkZGSQmZnJ\nzJkzAbj77rtJTU0lMzOT2tpali1b1nCNzWYjLS2NO+64g0svvZTnnnuOzMxMXn75ZYqLzS3wO3bs\n+Gnh9+wAACAASURBVP/2zju8imL94585Lb2REEhIgECokVASehFEpAgiCApKbyr489pQFK69gHpR\nuAiKBOkqol6QKlKkE3pHagIplCSkl9Pm98cmkZJyEtLdz/OcJzm7s7Pv7pnzntl33vkOEydO5MyZ\nM7i6ujJ37p29yLi4OD788EP++OMPDh8+TGhoKDNnziQ+Pp5ff/2VU6dOcfz4caZNmwbAmjVrePvt\nt22+rvj4eNzd3dHplF9iPz8/oqMLCGdUALQawWeDm9OtUXWm/e8k60/EotdrsWj0WJNTEELQxa8L\n4bHhZJoLyczp/i6YM2Hrh2Vi+z8BtedeiUjNMjN06QqsDim80LA1XnvegS6v59lrh5yeuwFdKcXb\nC+phlxbNmjXj1Vdf5Y033qBv37507twZgG3btvHpp5+Snp5OQkICQUFB9OvXD4DHHnss99igoCB8\nfHwAqFevHlevXsXd3R1/f386duwIwLBhw5g9ezavvfZa7nn37dvH6dOnc8sYjUbat2+Pm5sb9vb2\njB07lr59++bG0R977LHc81Zl9FoNc58JYVjYfl764SgfBfpjEXrMKcoTXedanVl+ZjkHrh2gs1/n\n/CvyCoQ2zyqhmdbjwCe4jK6g6qL23CsJ6UYzY747wNWsPdhpHBidEJ7da5+Y7zGWpCQsGrsqNZja\nsGFDDh8+TLNmzZg2bRrvv/8+mZmZTJw4kVWrVnHixAnGjx9/Rw63nZ0dABqNJvf/nPdms5IqendK\n393vpZT06NGDo0ePcvToUU6fPk1YWBg6nY7w8HAGDRrE2rVrc58kioqnpyeJiYm59kRFRVGrVq1C\njqoYOBi0LBzZmgAvJ9acjAGhwZycBkBozVAcdA65qbsF8uBkcPCATW8pK4mp3Beqc68EZJosjF9y\nkIORN3HxPEP3Gi1xOLsO2j2vfBnywZodltE7VJ384ZiYGBwdHRk2bBiTJ0/m8OHDuY7cy8uL1NRU\nVq1aVeR6r1y5wt69ewFYsWIFnTp1umN/u3bt2L17NxcuKIOHaWlpnDt3jtTUVJKSkujTpw9ffPEF\nx47lsUiFDQgh6NatW67tixcvpn//vMRXKyZujnqWjG2D3k4JBqRnr8dqp7Wjbc227IjacU/a5D04\neMBDUyFiJ5xdW3BZlUJRnXsFJ8ts4fllh9hzMZ4JPa1kWFLodTM6V6+9ICxJycqAqoOhjKwtfU6c\nOEGbNm1o0aIF7733HtOmTcPd3Z3x48fzwAMP0LNnT1q3zmfSTAE0atSIr776iiZNmnDr1q17BjKr\nV6/OokWLGDp0KMHBwbRv356zZ8+SkpJC3759CQ4OplOnTsycORMoOObeuXNnBg8ezJYtW/Dz82PT\npk0AzJgxg5kzZxIYGEh8fDxjx44t8nWUJzVc7RnZWZEzSE5MJzYpA4ButbsRkxbD2YSzhVfSapQi\nS/D7NFVU7H6RUpbLKyQkRKoUjNFskeMXH5B13lgrV+yPlFN3TpXtl7WRWe+4Srn1o0KPj/3gQ/nd\niBVy/dfHS8ym06dPl1hdFYXLly/LoKCg8jajzCmNz/Kv/bFyzrNb5L6WXWX3z7fJhNQsmZCRIIMX\nB8vZh2fbVsmFLVK+4yrlzi9K3L6qAHBQ2uBj1Z57BcVssfLyj0f5/fR13u3XlCdCarL1ylYestph\nsHODdvnH2nOwJCdh0dmVmCKkikph5C7YodFx/WYiYxYfwE7jQmiNUP6I/MO2Suo/BA17w47PIVVd\n+6e4qM69AmK1Sl5fdZy1x2N5s3djRnUMYHf0blJMKfSK+Ss71u5eaD2WpCSs2qo1oFoa1K1bl5Mn\nT5a3GVWCnI6ERWvHF33qc+xqIs8tO0w3v+5cSrrEpcRLtlX0yIfKgh5qamSxUZ17BUNKydT/neCX\nI9G82qMhzz5YH4CNERtxR0tbq12hsfYcrEnJmIW+1FIhVVTuJkd91KLR09nHnk8GNmPHuZvsPFYD\ngD+u2Nh7z0mNPLwEYo+XlrlVGtW5VyCklLz322m+D7/KC90C+b/uDQBFS2bblS10T05C336iTb12\nAHNyMlahQ19Ccr8qKoWR05Gwau2wpKTyVOvavN6rEZuOZ1JN28D20AzAg68rGTQb31RTI4uB+q2v\nIEgp+WTDWRbtiWBcpwBefaRh7r7tV7eTYcniURPQ1nYJfWOKkq2g9txVyorcsIzGgDV7ItPzD9Zn\nXKcAYmIacCbhDFEpUbZV5uCupEZG7oIzv5WWyVUW1blXEL7YfI75Oy4xon0dpj7a5I5JNOtOLqam\n2UxI6CSbe+1SSkxpSq6xOqCqUlbocmPuBizJKcDfSpLda3cH4NOdRZiH0GoUeDeFzf9WUyOLiOrc\nKwBfbbvA7K0XeCrUn3f73bkAQkLqNXYnnKa3xYCmw//ZXKfMyMBiVeV+75fSkPwFSE5Oxs/Pjxde\neCF3W2WQ/C2MnJi7VWvAkvK3qJxGI/jv4IdxpDZ/RP7B2uMxtlWo1UHPj+BWBOybVwoWV11U517O\nLNh5ic82/cWAlrX4eGAzNHetTblp+1QsAvq2fQ10dvnUci85ipCgLtRREfn3v/9Nly5d7thWWSR/\nCyKnI2HRGLBm99xz92k1DG/WD61jJC+v2sHO8zdtq1RNjSwWqnBYObJkbwQfrjvDo818+GxQ8L2L\nDt+KZF3MbhrYu9Cw+fAi1a0oQirOvdR67humwLUTJVtnzWbQe3q+u6uC5O+hQ4e4fv06vXr1yn0q\nkJVM8jc/tDoNGo3AanDAmppyz/7e9XrwzYmvqFHzAs8udWfF+Ha08Lch1PjIhzC3HWz9AB77bylY\nXvVQe+7lxI8HrvD26lP0aFqDL4e0QKe966OQkqvrX+aYnZ5Hmwwtcv05ipAAeruq8zFXdslfq9XK\nq6++es8PQGWU/M0PnUGD1cE5N+Z+O/Xc6lHXtS71617C09nA6O/CuXDj3nL34BUIbZ+Fw0sh+nAp\nWF31UHvu5cCvR6KY8ssJHmxYnTlPt0R/t2MHOLqC9TcPgIc7fYrr3DU5PfdS+pgL6GGXFpVd8nfu\n3Ln06dMHPz+/UrpD5Y/OTovVzglLStw9+4QQ9KjTg4UnF7Jk2IeMWXiaEWHhrHq+A77uDgVX3GUy\nnPoVVo2BZ3eAvWspXUHVoOp06SoJ647H8urKY7QL8OSb4SHY6fIImSRcRm54nbXuXoR4h+Dj7FPk\n8+QoQsLfg1xVgcou+bt3717mzJlD3bp1ee2111iyZAlTpkyp1JK/d6MzaLHqHe6JuefQvU53LNLC\nxbT9LB7TmpRMMyMWhnMrrZABZAd3GLQQEq/Aby+que+FUHW+9ZWA309d418/HCGkjgdho0Kx1+fh\n2C1m+PVZjhkMRAgzjwUWb8EHRREyJyxTdQZUK7vk7/Lly7ly5QoRERF8/vnnjBgxgunTp1d6yd/b\n0Ru0WPQOWJLzXoKxabWm+Dr5suXKFoJ83fh2ZChXEtIZtegAaVnmgiuv3Q66/1vpwR+sfAPOZYnq\n3MuI7X/d4IUVRwiq5cbCUa1xNOQTKtn1BVzdz6pGHXHUOdKrbvEWf7AkJWHVKY+5VSlbpipI/uZH\nZZf8zUFn0CAN9liyxzPuRghB9zrd2ROzh1RjKu3qeTJnaEtORCXy3LJDGM3Wgk/Q4V8Q2EOZuRpb\nPP38fwJCltOjTWhoqCyN/OGKyJ4LcYxedIBAb2dWjGuHm2M+i2dEH4IFPUhp2o/uxjP0CejDux3e\nLdY5Y96ayomzggveD/HcnK5odSXzO37mzBmaNGlSInVVFCIiIujbt+8/TjystD7L1V8eISMyiua/\nv0njY0cRuns7MkduHGHEhhF82uVTegf0BmDlgau8/vNx+gb7MGtIy3uzx24nLR6+7gR6e5jw5z8q\n/i6EOCSlDC2snNpzL2UORCQwdvFB6no6sXRs2/wduzENfh4PLj5saPIQGeYMBjUcVOzzmmJikK7V\n0GhEiTl2FRVb0Bm0WLV2YLFgvpl3Lnvz6s3xcvBic+Tm3G1PtvZnSu/GrD0ey7trThW8cpOTpxJ/\nvxUJv/1Ljb/ngU3feiFELyHEX0KIC0KIKXnsf0YIcVwIcUIIsUcI0bzkTa18HLlyi9HfHcDH3Z5l\n49pSzamAFZE2vQUJl2DA1/wSsYGGHg0J8iz+AtSmmBiks5uqK2MDquRvyaI3aLAIpbduisl7JqpG\naHjI/yF2Re8i3ZSeu/25B+szoUs9lu6LZNaW8wWfqE57eGganPoFDi4sMfurCoU6dyGEFvgK6A00\nBYYKIZreVewy8KCUshnwATC/pA2tbJyMTmLEwnCqORlYMa4d1V0KmF16dj0cWgQdX+SsW3VOxZ9i\nYIOB92Rs2Iq0WjHHxmJ1dFUVIVXKHJ1Bi1kqnQpTTGy+5XoF9CLDnMG2q9vu2P5m78YMCvHjyz/O\ns3RvRMEn6/gSBD6sxt/zwJZvfhvggpTykpTSCPwA3DGML6XcI6W8lf12H1B1k3ht4K9rKQwP24+r\nvZ4V49tS080+/8IJl2DNC8rMzG5T+fnczxg0BvrW61vs85tvxiFNJqx2jujt1akMKmWLzk6L2aL8\nn1/PHSCkRgg+Tj78dulOxUchBNMHNuPhJjV4e80p1hwrQIdGo4EB34CjJ/w4HNITSuISqgS2OPda\nwNXb3kdlb8uPscCGvHYIISYIIQ4KIQ7ezCcWV9m5eDOVZxbsw6DTsGJ8W/w8HPMvnBwLSx5X4oWD\nviNdWlh3aR0P13kYNzu3YttgilFmNlr1DlUqx12lcqA3aDCbrGg9PDDF5u+YNUJD33p92Ruzl7iM\nOyc86bQa5jzdktZ1qvHqyqPsOFeAv3DygqeWQkos/DwOrJaSupRKTYl+84UQ3VCc+xt57ZdSzpdS\nhkopQ6tXr16Sp64QRMan8fS3+wBYPq4ddTyd8i+cngBLB0B6PAxbBV4NWHtpLSmmFIY0HnJfduT0\nlizqEnsq5YDOoMVqlmh9fAvsuQP0rd8Xq7Sy/tL6e/bZ67V8OzKUQG8Xnl16iCNXbuVRQzZ+odD7\nU7i4BbZ9fL+XUCWwxblHA/63vffL3nYHQohgYAHQX0qZd4JrFSbqVjpPf7sfo9nK8nHtCPR2zr9w\nVgosH6yEZIZ+D7VCkFLy/dnvaVKtCS2qt7gvW8yxSpzTgk7Vcr9PSkPyt1evXri7u+dKFeQgpWTq\n1Kk0bNiQJk2aMHv27NztL774IoGBgQQHB3P4cMXWVsmZV6Hx9c9ti/lRz60eQZ5BrL20Ns/9bg56\nFo9pTXUXO0YvOlCwDk3oaGg1AnZ+Dmfyru+fhC3O/QDQQAgRIIQwAEOANbcXEELUBn4Bhkspz5W8\nmRWba0mZPP3tflIyTSwd25ZGNV3yL5yVAssGQcwRJZUrQJF9Db8WzoXECzzT5JliD6TmYIqJQePm\nhtmirsJUEZk8eTJLly69Z/uiRYu4evUqZ8+e5cyZMwwZojzBbdiwgfPnz3P+/Hnmz59f4ZUic58W\na/hiio4pOKUR6Fe/H2cSznD+Vt7ZMd4u9iwd2wadRsPwsHCiEzPyr6z3Z1ArBH59Fq6fLu4lVAkK\nHW2TUpqFEC8AmwAtsFBKeUoI8Vz2/q+BtwFPYG62YzLbkmRfFbiZksXTC/aRkGZk2bi2PFCrgFh5\nVqrSY486AIPCoMnfPbflZ5bjYedBr4DizUi9HVN0DHpfX0xZllLtuc8In8HZhLMlWmfjao15o02e\nUT2gakj+du/ene3bt9+zfd68eaxYsQKNRulzeXt7A7B69WpGjBiBEIJ27dqRmJhIbGxsrgBaRSNn\nnEdU98Gano41KQmte/6yvr3q9uLzA5/z26XfeCXklTzL1PF0YsmYNjw1fy/Dw/az6rkOeacW6+3h\nqWUwvxv8MBTGbwPHaiVyXZUNm2LuUsr1UsqGUsr6UsqPsrd9ne3YkVKOk1J6SClbZL/+EY49Ic3I\nsAX7uZaUyaLRrQvWpTamwYon4Wo4PLEAggbk7opKiWL71e0MajgIO63tC3Lkhynmb+de1XrulV3y\ntyAuXrzIjz/+SGhoKL179+b8eaUnGx0djb//35HRii4HnNOhENWUHydTIaEZTwdPOtbqyLpL67AU\nMBja1NeVsJGtib6VwejvwknNT4fG1Vdx8Mkx8NMoRa/pH4iaJ1dMktJNDA/bT0R8Gt+Nbk1o3QJ6\nB8Y0pcd+ZS8M/BYeGHjH7h/O/oBGaHiq0VP3bZeUElNMDI5t22K+binVPPeCetilRWWX/C2IrKws\n7O3tOXjwIL/88gtjxoxh586d93G3yodcLSMPJWnCFBODfSEyB/0D+/Pn9j/ZfnU73et0z7dcm4Bq\nzHm6Fc8tO8RzSw8RNio0b2VV/9bQbxb873lY/yr0/RLuM9xZ2VDz5IpBSqaJEd+Fc/56KvNHhNKh\nvlf+hY1psPzJvx17szslBVKNqfxy4Rd61OlBDaca922bNTkZa1oaOh9fzCZrlcuWqeySvwXh5+fH\nwIHKD/+AAQM4fvw4ALVq1eLq1b+zkSu6HHDO4jDSzQMoeCJTDt38u+Hn7MfCkwsLjdH3aFqD6QOb\nsetCHK/8eAyLNZ/yLZ6GTq8oEwQ3vPGPkyhQnXsRScsyM/q7A5yKTuKrZ1rxYMMCUjqN6bDiKbiy\nJ0/HDrD0zFJSjCmMDBpZIvblpJ6JGr5A1RtQreySvwXx+OOPs22bMlvzzz//pGHDhoDyFLBkyRKk\nlOzbtw83N7cKG2+Hv3vu0s4JYWdXaDokgE6jY1TQKI7HHefg9cKzkwaH+jO1TxPWnYjlnTUn8/9B\n6P42tJsE4d/A79P+UQ5eDcsUgUyThXGLD3L4yi3mPN2KHk0L6Gkb05UYe+RuGDA/T8eelJXEklNL\neMj/IR7weqBEbMx17tVrAvFVLhXyxIkTTJ48GY1Gg16vZ968eXdI/tasWfO+JH/HjBlD06ZNC5T8\nzcrKAuDDDz/ExcWF/v37k5mZiZTyDsnfgwcP8v77799zrs6dO3P27FlSU1Px8/MjLCyMnj17MmXK\nFJ555hm++OILnJ2dWbBgAQB9+vRh/fr1BAYG4ujoyHfffVfk6ytLcpy72WhF7+NTaMw9h/6B/Zl7\nbC4LTy6kdc3CP8PxXeoRl5bFN39eopqTHa/0aHhvISGg50dgNcHeOaDVQ/d3/hkhGillubxCQkJk\nZSLDaJbDFuyTdaeslb8ejiq4cFaalIv6Svmuu5RHf8i32MyDM2WzRc3kuYRzJWZn/JKl8nSjxjL+\nXLSc8+wWeWZPTInVLaWUp0+fLtH6KgKXL1+WQUFB5W1GmVNan2VyfIac8+wWeWpXtIwcPUZeGvyk\nzcd+ffRr+cCiB+TZ+LM2lbdarXLyT0dlnTfWyu92XSqooJRr/iXlO65Sbv3IZnsqIsBBaYOPVcMy\nNpBhVHrsuy7E8ekTwTzesoB4pzEdvh8Cl3fC419D87wHSW+m32TFmRX0qdeHBh4NSsxWU0wMwt4e\nq4OSa1+VFupQqRzkpEKajRZ0vj4FShDczZDGQ3DQObDo1CKbygsh+HhAM3o0rcG7v51m9dF8soiE\ngEdnQsth8OcM+PMzm22qrKjOvRAyjBbGLj7A7otxfDaoOYND/fMvnHoDFveFyzvg8Xn5OnaAb098\ni8lqYmLziSVqrykmBr2PD2ajsppNVRtQLQ1Uyd+SRX97WMbXF8vNOKzZoazCcLNzY1DDQWy4vIGr\nKVcLPwBFh+a/Q1vSJqAar648xva/buRdUKOBfrMheAhs+xB2zrSp/sqK6twLIN1oZsyiA+y9FM9/\nBjdnUEgBYpc3zsK33ZVZcU8tgxZD8y0amRzJT+d+4vHAx6ntWrtEbc5x7iajki+ck7mgolJWaPUa\nECiT6HyUgX3ztWs2Hz8qaBQGrYFZh2fZfIy9XsuCkaE0rOHC88sOczg/HRqNFh6fC80Gw5b3lGUt\nqyjqNz8f0o1KVsz+y/HMfLI5A1sV4NgvboOwR8CSBaPX3zHz9G6klLy39z0ctA5MbFGyvXbIdu61\nfDFlKs5dDcuolDVCCEXT3WhB76s4d1syZnLwdvRmdNBoNkVs4uiNozYf52qvZ/GYNni72jFm0QHO\nX89Hh0ajVUKmDwyCP96tsg5ede55kJZlZtR3BzgQkcAXT7VgQMsCHPuhxbB8ELjVgnF/QK1WBdb9\n64VfOXDtAK+EvoK3o3eJ2m3NzMQSH4/e1xdzbs9dde4qZY/eoMFktKL3VVI2bcl1v52RQSPxdvDm\n0wOfYpWFLJh9G9Vd7Fg6pi16raJDE3UrPe+CWp2iA5/j4LfPAKvt56kMqM79LpIyTIz6LpxDkbeY\nNaQl/VvkM3hqtcLmd+C3FyHgQRizCdwLDrHcTL/J5wc/J6RGCAMbDCywbHHI+QLlSA+A6txVyofc\nnnuNGiBEkXruAI56R15s9SIn4k6w8fLGIh1b29ORJWPakGY0MyIsnPjUfOL9OQ4++CnY/jEseUxZ\nk7WKoDr327iZksXQ+fs4ejWR/w5tSb/mvnkXNGXAqlGw+0sIHQtPr7Rp9fVPwj8hy5zFO+3fQSNK\n/tbnfIGUnrvSC1HDMvdHSUv+Hj16lPbt2xMUFERwcDA//vhj7r5Ro0YREBBAixYtaNGiBUeP/h2S\n2L59Oy1atCAoKIgHH3wwd/vGjRtp1KgRgYGBTJ8+vcTsvF90Bi3mLAvCYEBXvbrNue63069+P5pU\na8KXh78k05xZ+AG30cQnW4cmMYO+/93F5tPX8y6Y4+Afm6Motc7rCIeXVonJTqpzzybqVjpPfrOX\ny3FpLBjZmj7N8pkBmHoDFvWF02vgkY/g0f8oDaQQNkduZnPkZp5t/iwBbgElbL1CTsqZ2nOvuDg6\nOrJkyRJOnTrFxo0beemll0hMTMzd/9lnn+VKHLRooej6JyYmMnHiRNasWcOpU6f46aefALBYLEya\nNIkNGzZw+vRpvv/+e06frhgyt0pYJrsN+ha+aEdeaISGya0nE5sWy7xj84p8fJuAavwwoR2u9nrG\nLznIc0sPcS0pjx8JIaDVcHh+D/g0V5a9/H4opOTzg1BJUGeoArsvxPGvH46SZbawbFwbQurkIwJ2\n4yysGAxpcUpGTAEDp7cTnxHPB3s/oKlnU0Y/MLoELb8TU0wMaDToatTAZIxEoxFodaX3+33t44/J\nOlOykr92TRpT86238t1f2SV/cyQFAHx9ffH29ubmzZu4FyCJu2LFCgYOHEjt2krYL0cKODw8nMDA\nQOrVqwfAkCFDWL16NU2b3r1+fdmjhGWy03F9fcg4UbxU09Y1WzOwwUAWnVpEjzo9ijyTu2VtD9a+\n2Ilvd15i1h/n2TUzjtd7NeKZtnXQau6apepRB0b+BvvnwR/vwdx20PcLCHq8WLaXN//onrvZYmXm\n738xLGw/7o56fn6+Q/6OPScjxpwFo9bZ7NillLy/933STGl81PEj9Bp9CV7BnRgjItD7+CB0OtJu\nZeHgUnrnKi+qkuRveHg4RqOR+vXr52578803CQ4O5uWXX86VOTh37hy3bt2ia9euhISEsGTJEqBi\nSwHr7bS5T492DRpgunoVS3Jysep6NfRVvOy9+Pfuf2O0GItui1bDxK6BbH75QVrWduft1ad4Yt4e\nTsUk3VtYo4H2k+DZHcoY2k8jYeUI5Ym9kvGP7blfT87k/74/QvjlBAaH+PFe/yAcDfncjsNLYO3L\n4NUInv4R3AuYyHQXay+tZevVrbwa8iqBHoElZP29SClJP3gQp7btAIiPScWzVgFL/ZUABfWwS4uq\nIvkbGxvL8OHDWbx4ce7iHJ988gk1a9bEaDQyYcIEZsyYwdtvv43ZbObQoUNs2bKFjIwM2rdvT7t2\n7UrytpY4Dq4GbkQmI6XEITu8lHH8BM6dOha5LleDK+90eIdJWyYx//h8Xmj5QrFsyhloXX00hg/W\nnqbvf3cxqJUfrzzSEB83hzsLezdWst92z1JmtF7eAb2mK4OvlUSX5h/Zc9/+1w16z9rJyegk/jO4\nOZ8Nbp63YzdlwIYpsOb/sjNiNhbJsUelRPHJ/k9o5d2K4U2Hl+AV3IsxIgLLzTgc27TGarFyKzad\naqXs3MuDqiD5m5yczKOPPspHH310h5P28fFBCIGdnR2jR48mPDwcUHrkPXv2xMnJCS8vL7p06cKx\nY8cqtBSwl58zGSkm0pON2DdrBkKQcdT2nPW76eLXhX71+hF2Iowz8WeKXY8Qgsdb1mLrq10Z1ymA\n1Udj6Pb5dj5ad5oLN1LvLKzVQ5fX4Lld4NlAWbpv6QBl7eNKwD/KuZssVqZvOMuo7w7g7WLHmhc6\n8UR+s06jD8E3XZT4W9vnbM6IyeF62nXG/T4OBHzY8UO0mtId2EwPPwCAY+vWJN7IwGK24lnLqVTP\nWR5Udslfo9HIgAEDGDFiBIMG3akUGpudUSKl5H//+x8PPKDEl/v378+uXbswm82kp6ezf/9+mjRp\nQuvWrTl//jyXL1/GaDTyww8/FHmBkNKiur/SsYi7morW2Rm7Bg3IuA85ZFAWh/Gw9+CtXW8VKzxz\nO26OeqY+2pQtrz5Ir6CaLNwdwcMz/2Tg3N18H36FlEzT34WrN1I6dn0+V/zC3Paw8z9gvj8bSpt/\nTFgmOjGDF78/wqHIWzzdtjZv922KvT4Ph2s2wo5PFd0Jl5ow/Feo/1CRzhWfEc/4zeNJzErk2x7f\n4u9qe2+/uKQfOICuenUMdety5ZASH/T0rXo998ou+bty5Up27NhBfHw8ixYtApSFsVu0aMEzzzzD\nzZs3kVLSokULvv76awCaNGlCr169CA4ORqPRMG7cuFzHP2fOHHr27InFYmHMmDEEBQUV+dpLA08/\nRbguLiqFOg944tC8OcmbNiGtVoSmeH1KNzs33u3wLpO2TGLu0bm8FPLSfdvpX82RL4e05K1Hm/C/\nI9H8dDCKN385wXu/naL3Az4MDvWjXYAnGo0W2oyHxn1hw+uw5X04sUpZ4al22/u2o1SwRTqy9Uv5\n/wAAF1BJREFUNF5lKfm7+dQ1GfzuJhn09ka5+mh0/gWvnZRyXkdFFvSXZ6VMv1XkcyVkJMiBqwfK\n0KWh8uC1g/dhte1YrVZ5rsuDMurlV6SUUu5bfVF+9fxWaTKaS/xcquRv1aG0P8slU3fLjfNPSCml\nvLXqZ3m6UWOZefHifdf79u63ZfDiYHnk+pH7ruturFarPHLllnzrl+PygXc2yjpvrJUdp2+RM3//\nS16JT/u74Nn1Uv6nqZTvuEn520vF8hXFBVXyF4xmKx+uPc24JQfx83Bg7f914rG8JiZZLYq+xPyu\nkHINhqyAAV+DQwELXufB6fjTDFk7hIikCGY9NIuQGiElcyGFYLpyBfP16zi2UXqs8dGpuHs7oMvr\nyURFpYzw8nchLkqJYzu0aA5AxpHix91zmBw6mZqONZm2exrppnzkBYqJEIIW/u58NKAZB6Y+zKwh\nLajr6cTsrefp/Ok2nv52H78eiSIj4BGYtB/aTVSW8fuqDZz8pUJNfqqyzv1qQjqDv97Dgl2XGdm+\nDj8/34G6XnnEoOMvwne9FX2Jhr1g4j5o/GiRz/fbxd8YsWEEVqws6b2EDr4d7v8ibCT9QHa8vU0b\nQHHu1apgSKa0UCV/SwcvP2cSb6RjzDRjCAhA4+p633F3AGeDMx90/IDI5Ehe+fOVIs9etRV7vZb+\nLWqxbFxbdr3xEK/0aEjUrQxe/vEYbT76gzfXXeJw08nIcVuVEO6q0crqa4lXSsWeolIlnfuGE7H0\nmb2TS3FpfD2sFe/1f+De+Hp6AuyeDV93gptnlTVOn1wCTgUsdp0HiZmJTN01lbd2vUVw9WB+7Psj\nQV5lG/dMCw9H6+WFISAAY6aZ5LjMKjmYqlK58PJ3AQnx0WkIjQaH4OD7ypi5nTY+bXivw3vsid7D\nxC0TSTOllUi9+VHL3YEXuzdg+2td+WFCO3oE1eB/R2IYOHcPD/+QxDcNvyWl6/sQsRu+agf7vlYi\nAuVIlRpQzTRZ+Hj9GZbsjaS5vztzhrbEv9ptswxNGcpkpOM/wF8bwGKE+t3hsf8qqo5FQErJ+svr\nmRE+gxRjCuObjWdii4noNGV7S6WUpB84iGPrUIQQJMQqjby0c9xVVArDyy8nYyYFn/puOLRoQdxX\nX2FJTUPrfP+dj4ENBmKntWPqrqlM2DyBeQ/Pw9Vge0ZbcdBoBO3qedK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127 | "text/plain": [ 128 | "" 129 | ] 130 | }, 131 | "metadata": {}, 132 | "output_type": "display_data" 133 | } 134 | ], 135 | "source": [ 136 | "width_list = [0.2, 0.4, 1.0]\n", 137 | "sample_list = [10, 40, 160, 2560, 10240]\n", 138 | "x_list = np.arange(square_left, square_right, 0.02)\n", 139 | "\n", 140 | "for j in width_list:\n", 141 | " for i in sample_list:\n", 142 | " plt.figure(j)\n", 143 | " plt.title('width:'+ str(j))\n", 144 | " samples = sample(i)\n", 145 | " vf = value(j)\n", 146 | " update(samples, vf)\n", 147 | " values = [vf.get_value(x) for x in x_list]\n", 148 | " plt.plot(x_list, values, label='samples: ' + str(i))\n", 149 | " plt.legend()\n", 150 | " plt.show()\n", 151 | " " 152 | ] 153 | }, 154 | { 155 | "cell_type": "code", 156 | "execution_count": 23, 157 | "metadata": {}, 158 | "outputs": [ 159 | { 160 | "data": { 161 | "text/plain": [ 162 | "-0.0011160746050558655" 163 | ] 164 | }, 165 | "execution_count": 23, 166 | "metadata": {}, 167 | "output_type": "execute_result" 168 | } 169 | ], 170 | "source": [ 171 | "vf.get_value(1.75)\n" 172 | ] 173 | }, 174 | { 175 | "cell_type": "code", 176 | "execution_count": null, 177 | "metadata": { 178 | "collapsed": true 179 | }, 180 | "outputs": [], 181 | "source": [] 182 | } 183 | ], 184 | "metadata": { 185 | "kernelspec": { 186 | "display_name": "Python 3", 187 | "language": "python", 188 | "name": "python3" 189 | }, 190 | "language_info": { 191 | "codemirror_mode": { 192 | "name": "ipython", 193 | "version": 3 194 | }, 195 | "file_extension": ".py", 196 | "mimetype": "text/x-python", 197 | "name": "python", 198 | "nbconvert_exporter": "python", 199 | "pygments_lexer": "ipython3", 200 | "version": "3.6.1" 201 | } 202 | }, 203 | "nbformat": 4, 204 | "nbformat_minor": 2 205 | } 206 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Reinforcement-Learning-an-introduction 2 | 3 | First Edition 4 | --------------------------------------------------------------------------------