├── Genetic-Algorithm-And-Neural-Networks ├── Intelligent_Flappy_Bird │ ├── Intelligent_flappy_bird.py │ ├── README.md │ ├── Trained_biases-Best │ │ ├── biases_0.pkl │ │ ├── biases_1.pkl │ │ ├── biases_10.pkl │ │ ├── biases_11.pkl │ │ ├── biases_2.pkl │ │ ├── biases_3.pkl │ │ ├── biases_4.pkl │ │ ├── biases_5.pkl │ │ ├── biases_6.pkl │ │ ├── biases_7.pkl │ │ ├── biases_8.pkl │ │ └── biases_9.pkl │ ├── Trained_biases │ │ ├── biases_0.pkl │ │ ├── biases_1.pkl │ │ ├── biases_10.pkl │ │ ├── biases_11.pkl │ │ ├── biases_2.pkl │ │ ├── biases_3.pkl │ │ ├── biases_4.pkl │ │ ├── biases_5.pkl │ │ ├── biases_6.pkl │ │ ├── biases_7.pkl │ │ ├── biases_8.pkl │ │ └── biases_9.pkl │ ├── Trained_weights-Best │ │ ├── weights_0.pkl │ │ ├── weights_1.pkl │ │ ├── weights_10.pkl │ │ ├── weights_11.pkl │ │ ├── weights_2.pkl │ │ ├── weights_3.pkl │ │ ├── weights_4.pkl │ │ ├── weights_5.pkl │ │ ├── weights_6.pkl │ │ ├── weights_7.pkl │ │ ├── weights_8.pkl │ │ └── weights_9.pkl │ ├── Trained_weights │ │ ├── weights_0.pkl │ │ ├── weights_1.pkl │ │ ├── weights_10.pkl │ │ ├── weights_11.pkl │ │ ├── weights_2.pkl │ │ ├── weights_3.pkl │ │ ├── weights_4.pkl │ │ ├── weights_5.pkl │ │ ├── weights_6.pkl │ │ ├── weights_7.pkl │ │ ├── weights_8.pkl │ │ └── weights_9.pkl │ ├── bestScore │ ├── best_biases.pkl │ └── best_weights.pkl └── Self-Driving-Cars │ ├── README.md │ ├── Self-Driving-Cars.py │ ├── Trained_Biases │ ├── biases_0.pkl │ ├── biases_1.pkl │ ├── biases_2.pkl │ ├── biases_3.pkl │ ├── biases_4.pkl │ ├── biases_5.pkl │ ├── biases_6.pkl │ ├── biases_7.pkl │ ├── biases_8.pkl │ └── biases_9.pkl │ ├── Trained_Biases_Best │ ├── biases_0.pkl │ ├── biases_1.pkl │ ├── biases_2.pkl │ ├── biases_3.pkl │ ├── biases_4.pkl │ ├── biases_5.pkl │ ├── biases_6.pkl │ ├── biases_7.pkl │ ├── biases_8.pkl │ └── biases_9.pkl │ ├── Trained_Weights │ ├── weights_0.pkl │ ├── weights_1.pkl │ ├── weights_2.pkl │ ├── weights_3.pkl │ ├── weights_4.pkl │ ├── weights_5.pkl │ ├── weights_6.pkl │ ├── weights_7.pkl │ ├── weights_8.pkl │ └── weights_9.pkl │ ├── Trained_Weights_Best │ ├── weights_0.pkl │ ├── weights_1.pkl │ ├── weights_2.pkl │ ├── weights_3.pkl │ ├── weights_4.pkl │ ├── weights_5.pkl │ ├── weights_6.pkl │ ├── weights_7.pkl │ ├── weights_8.pkl │ └── weights_9.pkl │ ├── bestScore │ ├── best_biases.pkl │ └── best_weights.pkl └── README.md /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Intelligent_flappy_bird.py: -------------------------------------------------------------------------------- 1 | # ----------------------------------------------------------------------------- 2 | # 3 | # A.I. Learns to Play Flappy-Bird 4 | # 5 | # Language - Python 6 | # Modules - pygame, sys, random, numpy, pickle, cPickle 7 | # 8 | # By - Jatin Kumar Mandav 9 | # 10 | # Website - https://jatinmandav.wordpress.com 11 | # 12 | # YouTube Channel - https://www.youtube.com/mandav 13 | # GitHub - github.com/jatinmandav 14 | # Twitter - @jatinmandav 15 | # 16 | # ----------------------------------------------------------------------------- 17 | 18 | import pygame 19 | import sys 20 | import random 21 | 22 | import numpy as np 23 | 24 | import pickle 25 | import cPickle 26 | 27 | pygame.init() 28 | 29 | width = 800 30 | height = 400 31 | display = pygame.display.set_mode((width, height)) 32 | clock = pygame.time.Clock() 33 | 34 | pillar = None 35 | generation = 0 36 | bestScoreGeneration = 0 37 | bestSoFar = 0 38 | font = pygame.font.SysFont("Agency FB", 12) 39 | font2 = pygame.font.SysFont("Agency FB", 20) 40 | 41 | mutationRate = 30 42 | speed = 0 43 | pillarGap = 300 44 | index = 0 45 | 46 | # Pillar or Obstacle 47 | class Pillar: 48 | def __init__(self, start): 49 | self.gap = 150 50 | self.start = start 51 | self.x = start 52 | self.upperY = 0 53 | self.upperH = random.randint(20, height - 20 - self.gap) 54 | 55 | self.w = 30 56 | 57 | self.lowerY = random.randint(self.upperH + self.gap, height) 58 | self.lowerH = height - self.lowerY 59 | 60 | self.color = (144, 148, 151) 61 | 62 | def draw(self): 63 | pygame.draw.rect(display, self.color, (self.x, self.upperY, self.w, self.upperH)) 64 | pygame.draw.rect(display, self.color, (self.x, self.lowerY, self.w, self.lowerH)) 65 | 66 | def move(self): 67 | self.x -= 4 68 | 69 | if self.x < 0: 70 | self.reset() 71 | 72 | # Resetting the pillar 73 | def reset(self): 74 | index = 0 75 | for i in xrange(len(pillarSet)): 76 | if pillarSet[i].x > pillarSet[index].x: 77 | index = i 78 | 79 | self.x = pillarSet[index].x + pillarGap 80 | self.upperH = random.randint(20, height - 20 - self.gap) 81 | self.lowerY = random.randint(self.upperH + self.gap, height) 82 | self.lowerH = height - self.lowerY 83 | 84 | def resetPillars(): 85 | global index 86 | index = 0 87 | for i in xrange(len(pillarSet)): 88 | pillarSet[i].x = width + i*pillarGap 89 | 90 | # Brain or the Neural Network Implementation using Numpy 91 | class Brain: 92 | def __init__(self, size): 93 | self.size = size 94 | self.weights = [np.random.randn(y, x) for x, y in zip(size[:-1], size[1:])] 95 | self.biases = [np.random.randn(y, 1) for y in size[1:]] 96 | 97 | def feedForward(self, data): 98 | i = 0 99 | for w, b in zip(self.weights, self.biases): 100 | activation = np.dot(w, data) + b 101 | if i == 0: 102 | data = ReLU(activation) 103 | data = softmax(data) 104 | else: 105 | data = sigmoid(activation) 106 | i += 1 107 | 108 | data = abs(data[0][0]) 109 | 110 | if data > 0.5: 111 | return 1 112 | else: 113 | return 0 114 | 115 | # ReLU activation Function 116 | def ReLU(z): 117 | z[z < 0] = 0 118 | return z 119 | 120 | # Softmax or averaging the value 121 | def softmax(z): 122 | summation = np.sum(z) 123 | if summation == 0.0: 124 | summation = 1.0 125 | for i in xrange(len(z)): 126 | z[i] = z[i]/summation 127 | return z 128 | 129 | # Sigmoid Activation Function 130 | def sigmoid(z): 131 | return 1.0/(1.0 + np.exp(-z)) 132 | 133 | # Flappy Bird or Species 134 | class Bird: 135 | def __init__(self, color): 136 | self.x = 200 137 | self.y = height/2 - 20 138 | self.w = 40 139 | self.h = 40 140 | self.color = color 141 | 142 | self.fitness = 0 143 | self.score = 0 144 | self.prob = 0 145 | 146 | self.life = True 147 | 148 | self.brain = Brain([5, 4, 1]) 149 | 150 | def draw(self, pillar): 151 | pygame.draw.rect(display, self.color, (self.x, self.y, self.w, self.h)) 152 | pygame.draw.line(display, self.color, (self.x + self.w/2, self.y + self.h/2), (pillar.x, pillar.upperH)) 153 | pygame.draw.line(display, self.color, (self.x + self.w/2, self.y + self.h/2), (pillar.x, pillar.lowerY)) 154 | 155 | def move(self, pillar): 156 | self.y += 8 157 | self.score += (1.0/60.0) 158 | self.think(pillar.x, pillar.upperH, pillar.lowerY) 159 | 160 | if not (-self.h < self.y < height): 161 | self.fitness = self.score 162 | self.life = False 163 | self.score = 0.0 164 | if self.y < pillar.upperH or self.y + self.h > pillar.lowerY: 165 | if (pillar.x < self.x + self.w < pillar.x + pillar.w) and self.x < pillar.x + pillar.w: 166 | self.fitness = self.score 167 | self.life = False 168 | self.score = 0.0 169 | 170 | def think(self, x, upperH, lowerY): 171 | jump = self.brain.feedForward([x, upperH, lowerY, self.x, self.y]) 172 | if jump: 173 | self.y -= 14 174 | 175 | def reset(self): 176 | self.y = height/2 - 20 177 | self.life = True 178 | 179 | # Population Pool 180 | class Population: 181 | def __init__(self): 182 | self.population = [] 183 | self.eliminated = [] 184 | 185 | def createPopulation(self): 186 | colors = [(241, 148, 138), (187, 143, 206), (133, 193, 233), 187 | (171, 235, 198), (249, 231, 159), (237, 187, 153), 188 | (202, 207, 210), (241, 196, 15), (169, 50, 38), 189 | (91, 44, 111), (33, 97, 140), (25, 111, 61)] 190 | 191 | for i in xrange(len(colors)): 192 | bird = Bird(colors[i]) 193 | self.population.append(bird) 194 | 195 | def draw(self, pillar): 196 | for bird in self.population: 197 | bird.draw(pillar) 198 | 199 | def move(self, pillar): 200 | for bird in self.population: 201 | bird.move(pillar) 202 | 203 | popCopy = self.population[:] 204 | for bird in popCopy: 205 | if not bird.life: 206 | self.eliminated.append(bird) 207 | self.population.remove(bird) 208 | 209 | if self.population == []: 210 | self.evolve() 211 | 212 | def loadData(self): 213 | # Loading the Previous generation Weights and Biases 214 | index = 0 215 | for i in xrange(len(self.population)): 216 | with open("Trained_biases/biases_" + str(index) + ".pkl", "rb") as f: 217 | self.population[i].brain.biases = pickle.load(f) 218 | with open("Trained_weights/weights_" + str(index) + ".pkl", "rb") as f: 219 | self.population[i].brain.weights = pickle.load(f) 220 | index += 1 221 | 222 | def loadBest(self): 223 | # Loading the Best Weights and Biases So Far 224 | index = 0 225 | for i in xrange(len(self.population)): 226 | with open("Trained_biases-Best/biases_" + str(index) + ".pkl", "rb") as f: 227 | self.population[i].brain.biases = pickle.load(f) 228 | with open("Trained_weights-Best/weights_" + str(index) + ".pkl", "rb") as f: 229 | self.population[i].brain.weights = pickle.load(f) 230 | index += 1 231 | 232 | def evolve(self): 233 | self.reproduce() 234 | 235 | def normalizeFitness(self): 236 | summation = 0 237 | for bird in self.population: 238 | summation += bird.fitness 239 | 240 | for i in xrange(len(self.population)): 241 | self.population[i].prob = float(self.population[i].fitness)/summation 242 | 243 | def reproduce(self): 244 | global pillarSet, generation, bestScoreGeneration, bestSoFar, speed 245 | 246 | generation += 1 247 | 248 | self.population = self.eliminated[:] 249 | self.eliminated = [] 250 | 251 | resetPillars() 252 | 253 | self.normalizeFitness() 254 | 255 | index = 0 256 | fitness1 = self.population[index].fitness 257 | 258 | for i in xrange(len(self.population)): 259 | if self.population[i].fitness > self.population[index].fitness: 260 | fitness1 = self.population[i].fitness 261 | index = i 262 | 263 | bestScoreGeneration = fitness1 264 | 265 | if bestScoreGeneration > bestSoFar: 266 | bestSoFar = bestScoreGeneration 267 | save_best_generation(self.population) 268 | 269 | for i in xrange(len(self.population)): 270 | self.population[i].reset() 271 | 272 | newPop = Population() 273 | 274 | i = 0 275 | 276 | while len(newPop.population) < len(self.population): 277 | indexA = pickOne(self.population) 278 | indexB = pickOne(self.population) 279 | 280 | childA = Bird(self.population[i].color) 281 | i += 1 282 | childB = Bird(self.population[i].color) 283 | i += 1 284 | 285 | childA.brain, childB.brain = self.crossover(self.population[indexA].brain, self.population[indexB].brain) 286 | 287 | childA.brain = self.mutation(childA.brain) 288 | childB.brain = self.mutation(childB.brain) 289 | 290 | newPop.population.append(childA) 291 | newPop.population.append(childB) 292 | 293 | self.population = newPop.population[:] 294 | 295 | def crossover(self, brainA, brainB): 296 | newBrain1 = Brain(brainA.size) 297 | newBrain2 = Brain(brainA.size) 298 | 299 | for i in xrange(len(brainA.weights)): 300 | for j in xrange(len(brainA.weights[i])): 301 | for k in xrange(len(brainA.weights[i][j])): 302 | if random.randint(0, 1): 303 | newBrain1.weights[i][j][k] = brainA.weights[i][j][k] 304 | newBrain2.weights[i][j][k] = brainB.weights[i][j][k] 305 | else: 306 | newBrain1.weights[i][j][k] = brainB.weights[i][j][k] 307 | newBrain2.weights[i][j][k] = brainA.weights[i][j][k] 308 | 309 | 310 | return newBrain1, newBrain2 311 | 312 | def mutation(self, brain): 313 | prob = random.randint(1, 100) 314 | 315 | if prob <= mutationRate: 316 | for i in xrange(len(brain.weights)): 317 | for j in xrange(len(brain.weights[i])): 318 | if random.randint(0, 1): 319 | k = random.randint(0, len(brain.weights[i][j]) - 1) 320 | val = np.random.randn(1)[0] 321 | brain.weights[i][j][k] = val 322 | 323 | return brain 324 | 325 | def displayInfo(self): 326 | text = font2.render("Generation: " + str(generation), True, (236, 240, 241)) 327 | display.blit(text, (10, 10)) 328 | 329 | text = font2.render("Speed: " + str(speed/60.0) + "x", True, (236, 240, 241)) 330 | display.blit(text, (10, height - 90)) 331 | 332 | text = font2.render("Best Score From Previous Generation: " + str(bestScoreGeneration), True, (236, 240, 241)) 333 | display.blit(text, (10, height - 60)) 334 | 335 | text = font2.render("Best From All The Generations: " + str(bestSoFar), True, (236, 240, 241)) 336 | display.blit(text, (10, height - 30)) 337 | 338 | i = 0 339 | for species in self.population: 340 | text = font.render("Score: " + str("{0:.4f}".format(species.score)), True, species.color) 341 | display.blit(text, (10, 30 + (i + 1)*15)) 342 | i += 1 343 | 344 | # Picking Parents based on their fitness score 345 | def pickOne(population): 346 | prob = random.uniform(0, 1) 347 | 348 | for i in xrange(len(population)): 349 | prob -= population[i].prob 350 | if prob < 0: 351 | return i 352 | 353 | # Saving the Weights and Biases 354 | def save_best_generation(population): 355 | i = 0 356 | for species in population: 357 | with open("Trained_biases/biases_" + str(i) + ".pkl", "wb") as f: 358 | pickle.dump(species.brain.biases, f) 359 | with open("Trained_weights/weights_" + str(i) + ".pkl", "wb") as f: 360 | pickle.dump(species.brain.weights, f) 361 | i += 1 362 | index = 0 363 | for i in xrange(len(population)): 364 | if population[i].score > population[index].score: 365 | index = i 366 | 367 | save_best_brain(population[index].brain.biases, population[index].brain.weights, population[index].score) 368 | 369 | 370 | def save_best_brain(biases, weights, score): 371 | fp = open("bestScore", "r") 372 | prevScore = float(fp.read()) 373 | fp.close() 374 | 375 | if score > prevScore: 376 | fp = open("bestScore", "w") 377 | fp.write(str(score)) 378 | fp.close() 379 | with open("best_biases.pkl", "wb") as f: 380 | pickle.dump(biases, f) 381 | with open("best_weights.pkl", "wb") as f: 382 | pickle.dump(weights, f) 383 | 384 | def close(): 385 | pygame.quit() 386 | sys.exit() 387 | 388 | def gameLoop(): 389 | loop = True 390 | global pillarSet, generation, bestScoreGeneration, bestSoFar, speed, index, pillarGap 391 | 392 | pillarGap = 200 393 | 394 | generation = 1 395 | bestScoreGeneration = 0 396 | bestSoFar = 0 397 | 398 | pillarSet = [] 399 | noPillar = 15 400 | for i in xrange(noPillar): 401 | pillar = Pillar(width + i*pillarGap) 402 | pillarSet.append(pillar) 403 | 404 | population = Population() 405 | population.createPopulation() 406 | speed = 60 407 | 408 | index = 0 409 | 410 | while loop: 411 | if len(pillarSet) < noPillar: 412 | index = 0 413 | for i in xrange(len(pillarSet)): 414 | if pillarSet[i].x > pillarSet[index].x: 415 | index = i 416 | pillar = Pillar(pillarSet[len(pillarSet) - 1].x + pillarGap) 417 | pillarSet.append(pillar) 418 | 419 | for event in pygame.event.get(): 420 | if event.type == pygame.QUIT: 421 | close() 422 | if event.type == pygame.KEYDOWN: 423 | if event.key == pygame.K_q: 424 | close() 425 | if event.key == pygame.K_s: 426 | save_best_generation(population.population) 427 | if event.key == pygame.K_l: 428 | population.loadData() 429 | if event.key == pygame.K_b: 430 | population.loadBest() 431 | if event.key == pygame.K_r: 432 | gameLoop() 433 | if event.key == pygame.K_1: 434 | speed = 60*1 435 | if event.key == pygame.K_2: 436 | speed = 60*2 437 | if event.key == pygame.K_3: 438 | speed = 60*3 439 | if event.key == pygame.K_4: 440 | speed = 60*4 441 | if event.key == pygame.K_5: 442 | speed = 60*5 443 | if event.key == pygame.K_6: 444 | speed = 60*6 445 | if event.key == pygame.K_7: 446 | speed = 60*7 447 | if event.key == pygame.K_8: 448 | speed = 60*8 449 | if event.key == pygame.K_9: 450 | speed = 60*9 451 | 452 | display.fill((39, 55, 70)) 453 | 454 | for i in xrange(len(pillarSet)): 455 | pillarSet[i].move() 456 | if pillarSet[i].x < population.population[0].x: 457 | index = (i + 1)%noPillar 458 | pillarSet[i].draw() 459 | 460 | population.move(pillarSet[index]) 461 | population.draw(pillarSet[index]) 462 | 463 | population.displayInfo() 464 | 465 | pygame.display.update() 466 | clock.tick(speed) 467 | 468 | gameLoop() 469 | -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/README.md: -------------------------------------------------------------------------------- 1 | # Intelligent Flappy Bird 2 | 3 | Implementation of A.I that tries to learn to play Flappy-Bird 4 | 5 | Starting with initial population of size 12, Each "Flappy Bird" tries to think whether to move up i.e., to flap or go down/fall. Each Bird's is a simple 3-layer Feed Forward Neural Network. 6 | 7 | For more Information : [Mandav's Blog/A.I. Learns to play Flappy-Bird](https://jatinmandav.wordpress.com/2018/03/05/a-i-learns-to-play-flappy-bird/) 8 | 9 | Watch Learning in Action: [MANDAV/A.I. Learns to play Flappy-Bird](https://youtu.be/H9BY-xr2QBY) 10 | 11 | Usage and Controls of the Project: 12 | - Key L - Load the Previous Generation 13 | - Key B - Load the Best Generation Ever 14 | - Key R - Reset the Evolution/Progress 15 | - Key S - Save the Current Generation 16 | - Key Q - Quit 17 | - Keys 1, 2, 3, 4, 5, 6, 7, 8, 9 - To Speed up the Game upto the factor of the number pressed 18 | -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xce\xde9L\xbbx\xe3\xbf\xf0s5\xfea\x9b\xb1\xbfq\x1eb\x98=J\xea\xbf\xdb\x10z\xa6\x152\xc0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xfe\xeb\x877\xc4\x1b\xdd\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x91;3y\x13\x85\xed\xbfo}\x07\t\xf3\x0e\xda?~s\xf1Y\xea\x06\xfb?\x14\x99R\xab\xbbu\xf3\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xff\x04\xecsw \xf1\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_10.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x0b\xd2\x89\xf0ft\xfc\xbf\xf5\x14\x0b\xbf\x1cn\xf0?\xd2R\xd5\x191\xd7\xe1?)\xe3\xd1fO\xa6\xf6?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xefkx\xcd\xe7a\xe5?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_11.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"{\xee\xca\xbf!0\xc7\n\x13\x85\xe0?]U\x86\x13|l\xec\xbf\xff\xd3\x18\xee~\xfd\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'p\xb8\xb1\x02\x80\x9f\xc1\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xd1M\x1d@\x1b\xa5\xc8?\xb2,\xdc\xad\xd7\x03\xfb\xbfoxWJ\xcd\xfa\xfd\xbfh\x9a\xf1r\x11/\xeb\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'=\x17\x19\xc5=W\xf2?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x88\xb9\xfe\xf0\x80\xc8\x03@+\xfe\xfb\x9cA:\xdc\xbfHF\xb7\x8b[\x96\xdf?\x99\xaf$\xde\x83\xeb\xd5?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xed\xc8\xf9\xed\x16`\xe9?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x10\x18Y@7v\xbd\xbf\xfaR\x08!\xe1R\xaf?_\x10\xcb\x04e\xda\xfc\xbfQ3\xd5\x1c%*\xf6?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xfc/\x98M\xbe\xcf\xe5?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases-Best/biases_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xda\xc3\x1bVg\r\xea?P\xcd\x06\xf1\xa8t\xec?\x99W\x1fKk{\xca\xbf5c\xd4\xb3\xe2^\xc0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xa2PN\xf8\xf2\x8f\xc8\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\t\xcc\xefZ\xdc?\x05@\x14\xcaU\xc7y\xa8\xf0?\xb24\xd4\x03\xda\x81\x03\xc0\x17ZA\x11\xe5\xa0\xf8?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\x83\x03\xe84\x1a[\xf1\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xbc\x9b\x8c\nq\xc0\xfe?VD4\r\xba\x99\xcc?\x0c\x1e#\x96\x9b\xe7\xe5?\xb9\xc5%d\xef\xd8\xf9?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'^\xb1\xbd\xc7\xd1[\xf4\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_10.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'^\x8a\x85\x8a\xd97\xdd\xbf\x7f\xbfZ\xd2\xdc\x12\xde?\n*\x9b\x05\x01\xc8\xe4\xbf\xf3\xc0t\x92\xe6\x98\xc5\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\x000\xd3\xbf\xe1\xa6\xf6?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_11.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x9b\x1d\xec\x84p*\xf3?\xbf\xf1K1\x1e\x89\xe2\xbf~\x16\xef\xca\xbf\xfc\xe4?H\xc5Y/ -\xf7\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xae\xff\xf9dAK\xf1\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_2.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'V\xf3T\xac(\x81\xf1?\xd6\x96\xcb4j\xb7\xd3?c\x8bK\xea\xe5>\xf5\xbf\n}\xe3@\x91\xef\xed\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'A\xe33W\xd1\xcd\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_3.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\tn\xf8Z\xb8\xff\xe1?hQ\x9b\x1c\x8c;\xe2\xbfm\xd5c[\xd61\xf2\xbfX!\x7f\xbf\xb0\x9f\x02\xc0' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\x9fQ\xa8_\xc1\xa8\xee?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_4.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"\xcf\x07\xc1#\x1a\xb4\xc7\xbf\x0b\xb0B'!\xc4\xb6\xbf\x92.\xafh\xf0,\xf6?\x8b{\x8f\xb0\n;\xf2\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'{\xd5z\xb2)\x9bv\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'0\xa9\x00#\x1c\xde\xd4?E\x81\xb5\'g\xf7\xf1?|\x00\xf1er\xef\xea?\xc2\x80\'\xef"\xdd\xf7\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xa6\xfb\xb1%\x93\n\xb8?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x1dj\xd3\x8a\x96g\xd4\xbff\x0b\x1bGo\x10\xd7?\xe2\xf1\xcaOh7\xe3?\xfe\xc1c\x93\x0bA\xf5?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xb8N\x8cV\xdb\xbb\xf9?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S's\xfb+\xa3E+\xf0\xbf\x9c\xe1%\xb9\x8d\xd1\xeb\xbf\x07[\r\xef\x95\xbd\xd5?\xbc\xf0\x02\x00CM\xe3\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xd0\xe8\xc6\xf3\xec\xeb\xf4?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x924C\xe8%6\xed\xbf\x8d\x9a@\xfa\x08\xe7\xf2\xbf\xcbm\xef\xfa\xc8\x07\xd3?\xba\x00\xd9\x05\xd9k\xe5?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xfd\xa1\x81\xb6w\xd6\xe7?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_biases/biases_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xc3v\xf4\x99\x0fl\xe0\xbf\x90U\x10\xb9\xf0\xf9\xa0?c\xdb"|\x9a\x97\xde?;\xa8&H\xef\r\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'Z\xd8\xb0H7\x93\xf3?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xc0P\xc7\x11\xc1\x95\xea?;\xd2i\n\xd0\xcb\xb7\xbf<\xcfL\xb6\x14\xc2\xee\xbf\x1b(\x90U\x8dY\xd8\xbf1\x06\x0e+B\x81\x07@$YCY \x1e\xf7\xbf\xa7p\xdd\x88S\x80\x02@&\x03I)\xcc\xe8\xc2\xbf\x98.\xc9\x1f\x13\xc7\xe7?\x01{b4v\xf5\xd9\xbf\xf6\xa1$\xc6\xfb\x9b\xee?\xf6\x87\xbb\x90\xc9\xa6\xef\xbf&\xd15\xa5\xdf}\xe9?n\xdc\x9d\xfb\xdf\xb6\xce\xbf\xe9\xda\x0b\x8a\x0b\x0b\xde?"\xc4]S%}\xf2\xbf\x12\x8a\xf5\x11\xd8\xa4\xc4\xbf\x83LL\xf0\xd8r\xed\xbf3\xbc|\xb2\xb6e\xea?@\x98\x98\x9b\x9d\x98\xf2\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'\x1c=W\xac+\xdf\xec?\xda$c\x8b\x9at\xdb\xbf#\xc9\xd0m\xc3"\x03@\xf0[\x10h\xcf\xcf\xf1?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'[\x96h)L\xf4\xc6?\x88^s\xd7\x8d^\xf7\xbf+QK*!i\xf0?\xe7\x89\xc0che\xe1?\x8eX5\xb5[\x04\xed\xbf\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbf\x92t\xc5A^l\xe2?\xc5\xf9\x1d\xa1c\xf1\xdc\xbf/\xf6c\x87?\x02\x01@?V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfE\xb4\xcc$\xf9\xb0\xb5?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?IB\x0fMO\x11\xd4?\xd5\xbc\x91\xb86\x9d\xe1\xbf\xee&a\x99\x8a\x80\xf5?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'FN\x05k\x11\x95\xd4?\x15f\xa2\xbb\x0f\xf0\xc6\xbf,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_10.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S" j\x06\x11\xb2=\xdd?\x88^s\xd7\x8d^\xf7\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?\x01\xbd\xe4\x9b\xe0\xaa\xff\xbf\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?g\xa0\x8f\x1cU\xfe\xe6\xbf\n\xed.N\xbf\x9d\xd5?\xb5=z\xe21U\xe4\xbf2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfX'\xc5\xd6G-\xdd\xbf\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\xd5\xbc\x91\xb86\x9d\xe1\xbf\xee&a\x99\x8a\x80\xf5?" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'FN\x05k\x11\x95\xd4?\xc9\x1c%f!"\xe4?,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_11.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"&`X\xcf\xd1\x98\xda\xbf\xf9\xf4f\x1f\xca\x91\xeb\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?'\xce\x0c9\xcf\xfb\xf2?\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xb0lIS\xd8\xd9\xea?zN#\xc5f\x92\xde?\xc5\xf9\x1d\xa1c\xf1\xdc\xbf\n\xed.N\xbf\x9d\xd5??V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfuM\xa9\x0f\xef\x9a\xf2?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?IB\x0fMO\x11\xd4?\xd5\xbc\x91\xb86\x9d\xe1\xbf\xc1\x00B\x17\xa5\x18\xd4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'R9\xe5\xe6:\xe2\xf1?\x15f\xa2\xbb\x0f\xf0\xc6\xbf,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_2.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S' j\x06\x11\xb2=\xdd?\x88^s\xd7\x8d^\xf7\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?\xfd\x00\xc0R\x8bo\xe4?\xe3K\xe6\x1c\xd2\x0c\x95?d?-gF\xca\xf6?\xb0lIS\xd8\xd9\xea?zN#\xc5f\x92\xde?\x92\x8e\x16\xfc\xbc\xcd\xf5?/\xf6c\x87?\x02\x01@?V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfuM\xa9\x0f\xef\x9a\xf2?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?IB\x0fMO\x11\xd4?\xd5\xbc\x91\xb86\x9d\xe1\xbfC\xdaz\xafY\xe1\xe1\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'R9\xe5\xe6:\xe2\xf1?\x15f\xa2\xbb\x0f\xf0\xc6\xbf,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_3.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"&`X\xcf\xd1\x98\xda\xbf\xf9\xf4f\x1f\xca\x91\xeb\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?'\xce\x0c9\xcf\xfb\xf2?\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?g\xa0\x8f\x1cU\xfe\xe6\xbf\n\xed.N\xbf\x9d\xd5?\xb5=z\xe21U\xe4\xbf2 \x0bB5\x8f\xf2?e\xa7W\xb4\x06\xcc\xdc?X'\xc5\xd6G-\xdd\xbf\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\x1cr\xb4\r\xb9\x91\xe5\xbf\xee&a\x99\x8a\x80\xf5?" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'FN\x05k\x11\x95\xd4?\xc9\x1c%f!"\xe4?\xc2\xadC\x9e\xf1d\xa7\xbf\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_4.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"&`X\xcf\xd1\x98\xda\xbf\xf9\xf4f\x1f\xca\x91\xeb\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?'\xce\x0c9\xcf\xfb\xf2?\xe3K\xe6\x1c\xd2\x0c\x95?d?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?g\xa0\x8f\x1cU\xfe\xe6\xbf\n\xed.N\xbf\x9d\xd5??V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfX'\xc5\xd6G-\xdd\xbf\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\xd5\xbc\x91\xb86\x9d\xe1\xbf\xc1\x00B\x17\xa5\x18\xd4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'B\x8b\x84\x90\xd0\xae\xf0?\xc9\x1c%f!"\xe4?G6\xea\xa53\xae\x03\xc0\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\x96h)L\xf4\xc6?1\x96\xa1|\x03 \xd9?>G\xd7\n\xb0\x93\xb6\xbf\x1b\xb8\x18Ru\x06\xf7?'\xce\x0c9\xcf\xfb\xf2?o_a\x9b\xf8[\xf5\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?\xc5\xf9\x1d\xa1c\xf1\xdc\xbf/\xf6c\x87?\x02\x01@?V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfE\xb4\xcc$\xf9\xb0\xb5?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\xf2\xe9X\x0e\x1bt\xf9?\xc1\x00B\x17\xa5\x18\xd4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'R9\xe5\xe6:\xe2\xf1?\x15f\xa2\xbb\x0f\xf0\xc6\xbf,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S' j\x06\x11\xb2=\xdd?\x88^s\xd7\x8d^\xf7\xbf\xa8\xc6\\\x7f\xd84\xd4?\xe7\x89\xc0che\xe1?\xfd\x00\xc0R\x8bo\xe4?\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xb0lIS\xd8\xd9\xea?zN#\xc5f\x92\xde?\x92\x8e\x16\xfc\xbc\xcd\xf5?$\xf3\x13y\xbe\xbd\xe5\xbf?V\xb8\x13\xb6H\xa3?\x84^\x8d\xb0\xfft\xe6\xbf\xf2 \xc8\x84\xa3U\xfb\xbfuM\xa9\x0f\xef\x9a\xf2?\x1f\x18\xe1\xd9D\xa6\xdb\xbf\n\x80\x90M\x99t\xcc?IB\x0fMO\x11\xd4?\xd5\xbc\x91\xb86\x9d\xe1\xbfC\xdaz\xafY\xe1\xe1\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'B\x8b\x84\x90\xd0\xae\xf0?n>\xb3Q\xe1M\xd4?,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S" j\x06\x11\xb2=\xdd?\xf9\xf4f\x1f\xca\x91\xeb\xbf+QK*!i\xf0?\xa4N:\xebK\x18\xe0\xbf'\xce\x0c9\xcf\xfb\xf2?\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?\x92\x8e\x16\xfc\xbc\xcd\xf5?/\xf6c\x87?\x02\x01@?V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfuM\xa9\x0f\xef\x9a\xf2?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\xfa\xce\x7f\xdegB\xdb\xbf\xc1\x00B\x17\xa5\x18\xd4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'B\x8b\x84\x90\xd0\xae\xf0?\x15f\xa2\xbb\x0f\xf0\xc6\xbf,\xced\xf0\x94u\xe0?\xb7\xaa\xd1\xf3\xa2\x90\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights-Best/weights_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\x96h)L\xf4\xc6?\x88^s\xd7\x8d^\xf7\xbf>G\xd7\n\xb0\x93\xb6\xbf\xe7\x89\xc0che\xe1?\xfd\x00\xc0R\x8bo\xe4?\xe3K\xe6\x1c\xd2\x0c\x95?d?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?g\xa0\x8f\x1cU\xfe\xe6\xbf/\xf6c\x87?\x02\x01@?V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 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30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xb3\xa80V\x03\xce\xf1?\xf9\xf4f\x1f\xca\x91\xeb\xbf\xcaK"\xaa\xc25\xcb\xbf\xe7\x89\xc0che\xe1?\'\xce\x0c9\xcf\xfb\xf2?\x1e ,{+\xfd\xd4\xbfd?-gF\xca\xf6?\xc3\xb4qH1\x1e\xa6\xbfzN#\xc5f\x92\xde?\xc5\xf9\x1d\xa1c\xf1\xdc\xbf\n\xed.N\xbf\x9d\xd5??V\xb8\x13\xb6H\xa3?2 \x0bB5\x8f\xf2?\xf2 \xc8\x84\xa3U\xfb\xbfE\xb4\xcc$\xf9\xb0\xb5?\x13\x06\xb4\x93\xde\xdf\xfd\xbf\n\x80\x90M\x99t\xcc?/}\xc4\x95\xf8\xac\xba?\xd5\xbc\x91\xb86\x9d\xe1\xbf\xee&a\x99\x8a\x80\xf5?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'FN\x05k\x11\x95\xd4?\xc9\x1c%f!"\xe4?>\x8dV\xd2\xd5\xf7\xcf\xbf&c\xec9\xa7\xe6\xe1?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2\'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?\xafF\xf81S\xbc\xeb\xbf(\x8e{\x07;]\xf9\xbfK\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf"\xf4\xda(\xb5&\xe9\xbf\xb6\xe8\xb1\xc8JY\xcc\xbf\xd0L\x0f\xd4\x8cB\xd5?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\xf3\xb6\xdezt7\x01\xc0\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\xd2\xf9\xf6\xc7\x99\xf3\xef\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\x15\x93\x11\xff\xa4\x08\x86?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_10.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?\xc5\xed\x14s\x1b\xf0\xe9\xbf\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbfv\x9e\xee2\x9cz\xfe\xbf/\xf6\xcbf\xad\x1c\xf9\xbf\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?96\xdf\xe6\xb4\x82\xaf\xbfC\x81\xb2B\xf67\xc8?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_11.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?/\xf6\xcbf\xad\x1c\xf9\xbf\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\xe9\xbf\xd9l1,\xe6\xbf\x96\xffW\xb9\xac*\xec?I8\x0f*\x14p\xf0?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\xf3\xb6\xdezt7\x01\xc01`-.|\x00\xe5\xbfUK\xa2Q\xe9\x01\xf7?\x8f\xe6o\x04\x1f\xb0\xf6\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?%ypS\xe4:\xf0?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_2.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"_I\xfb_\xe9\x12\xcb?\xc5\xed\x14s\x1b\xf0\xe9\xbf\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbf\xbd=\xe1<\xc9\xb4\xf9\xbf\\\xc3\xb6\x1a\xe7\xc7\xe4?\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?96\xdf\xe6\xb4\x82\xaf\xbfC\x81\xb2B\xf67\xc8?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\xfa\xd8\xaf\x92\xf8\xf7\xf4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_3.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2\'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf"\xf4\xda(\xb5&\xe9\xbf\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?#\x1e\xe2S\xe4\x8c\xe0?\x92\xb4F\x17\xf0\x86\xd1?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\xd2\xf9\xf6\xc7\x99\xf3\xef\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\x15\x93\x11\xff\xa4\x08\x86?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_4.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?w\x04Vttu\xe6\xbf\xafF\xf81S\xbc\xeb\xbf(\x8e{\x07;]\xf9\xbfK\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\xb6\xe8\xb1\xc8JY\xcc\xbf\xd0L\x0f\xd4\x8cB\xd5?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\xf3\xb6\xdezt7\x01\xc01`-.|\x00\xe5\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?\x91\xce\x16\xf7o\xbb\xc2?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5??\x07\x95\xadPD\x02@\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\x96\xffW\xb9\xac*\xec?\xf1\xfe_\xb8\xd5\xad\xc2?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\x82o\xd3\xb2\xear\xb0\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbf\xc3U[l\xfai\xdd\xbf\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?<\x0bd\x131\x11\xf1\xbf\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"_I\xfb_\xe9\x12\xcb?\xc5\xed\x14s\x1b\xf0\xe9\xbf\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?\xafF\xf81S\xbc\xeb\xbfv\x9e\xee2\x9cz\xfe\xbf/\xf6\xcbf\xad\x1c\xf9\xbf\x1d#o\xf1J`\xd9\xbf\x08\xf1\xbb\x1e\xb9\xf0\xf1\xbf\\\xc3\xb6\x1a\xe7\xc7\xe4?\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?ME\xc2 9M\x9e\xbf\x92\xb4F\x17\xf0\x86\xd1?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | 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S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?\xea\xbe\x8c:\x14^\xe9\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?w\x04Vttu\xe6\xbft\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\x01\xd6\xae\xf4\xe4\xc8\xe1\xbf\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?#\x1e\xe2S\xe4\x8c\xe0?\x92\xb4F\x17\xf0\x86\xd1?\xea\xbe\x8c:\x14^\xe9\xbf1`-.|\x00\xe5\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/Trained_weights/weights_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"[\xb1F\x8e\xa5FG?%\xeb`\xb8\xcf3\xe7?\xfd$S\xc2'S\xe7\xbf0x\xad\xa3b.\xf0?5\xd8~\x1c\xcd\xb2\xf5?M\xc8\x93\xbc\xef\x9a\xc3?t\x89\xa0\x00\x0f\x9e\xf7\xbf\xb6F\x06V\x93\x8f\xf5?K\xa70e\x0b7\xc8?\x1d#o\xf1J`\xd9\xbfL\xaa\x8e\xa1\xee\x06\xe0\xbf\\\xc3\xb6\x1a\xe7\xc7\xe4?\x96\xffW\xb9\xac*\xec?\xd0L\x0f\xd4\x8cB\xd5?{SaP}\x81\xee\xbfC\x81\xb2B\xf67\xc8?6M\xf0\xa8\xf2\xd7\xe1\xbf\x17b\xf7\xd2\xe8\x0b\xbe\xbfUK\xa2Q\xe9\x01\xf7?\x11\x19g[?\x04\xd5\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S"\xca\xcf4E\xbf\n\xf9?\xed^\x82\x81\r\xc8\xd1?\x14'67\x0c\xee\xd3\xbf\x16\xf0j\x81t]\x02@" 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/bestScore: -------------------------------------------------------------------------------- 1 | 4148.98333336 -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/best_biases.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xce\xde9L\xbbx\xe3\xbf\xf0s5\xfea\x9b\xb1\xbfq\x1eb\x98=J\xea\xbf\xdb\x10z\xa6\x152\xc0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xfe\xeb\x877\xc4\x1b\xdd\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/best_weights.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L4L 16 | L5L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xc0P\xc7\x11\xc1\x95\xea?;\xd2i\n\xd0\xcb\xb7\xbf<\xcfL\xb6\x14\xc2\xee\xbf\x1b(\x90U\x8dY\xd8\xbf1\x06\x0e+B\x81\x07@$YCY \x1e\xf7\xbf\xa7p\xdd\x88S\x80\x02@&\x03I)\xcc\xe8\xc2\xbf\x98.\xc9\x1f\x13\xc7\xe7?\x01{b4v\xf5\xd9\xbf\xf6\xa1$\xc6\xfb\x9b\xee?\xf6\x87\xbb\x90\xc9\xa6\xef\xbf&\xd15\xa5\xdf}\xe9?n\xdc\x9d\xfb\xdf\xb6\xce\xbf\xe9\xda\x0b\x8a\x0b\x0b\xde?"\xc4]S%}\xf2\xbf\x12\x8a\xf5\x11\xd8\xa4\xc4\xbf\x83LL\xf0\xd8r\xed\xbf3\xbc|\xb2\xb6e\xea?@\x98\x98\x9b\x9d\x98\xf2\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L1L 47 | L4L 48 | tp19 49 | g11 50 | I00 51 | S'\x1c=W\xac+\xdf\xec?\xda$c\x8b\x9at\xdb\xbf#\xc9\xd0m\xc3"\x03@\xf0[\x10h\xcf\xcf\xf1?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/README.md: -------------------------------------------------------------------------------- 1 | # Self Driving Cars 2 | 3 | Implementation of A.I that tries to Drive on any random Map 4 | 5 | Starting with initial population of size 10, Each "Car" tries to think whether to Turn Right, or Turn Left or go Straight using a simple 3-layered Feed Forward Neural Network. 6 | 7 | For more Information : [Mandav's Blog/A.I. Learns to Drive](https://jatinmandav.wordpress.com/2018/03/09/a-i-learns-to-drive/) 8 | 9 | Watch Learning in Action: [MANDAV/A.I. Learns to Drive](https://www.youtube.com/watch?v=_TGGbPjT7pg&feature=youtu.be) 10 | 11 | Usage and Controls of the Project: 12 | - To generate a Map, Click on boxes to add to path or click again to remove from path. After Generating the map of your choice, press 'S' to start training. NOTE: Make Sure you create an actual "path" and NOT scattered around the playground 13 | - Key L - Load the Previous Generation 14 | - Key B - Load the Best Generation Ever 15 | - Key R - Reset the Evolution/Progress 16 | - Key S - Save the Current Generation 17 | - Key Q - Quit 18 | - Keys 1, 2, 3, 4, 5, 6, 7, 8, 9 - To Speed up the Game upto the factor of the number pressed 19 | -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Self-Driving-Cars.py: -------------------------------------------------------------------------------- 1 | # ----------------------------------------------------------------------------- 2 | # 3 | # Car/A.I. Learns to Drive 4 | # 5 | # Language - Python 6 | # Modules - pygame, sys, random, numpy, pickle, cPickle, math 7 | # 8 | # By - Jatin Kumar Mandav 9 | # 10 | # Website - https://jatinmandav.wordpress.com 11 | # 12 | # YouTube Channel - https://www.youtube.com/mandav 13 | # GitHub - github.com/jatinmandav 14 | # Twitter - @jatinmandav 15 | # 16 | # ----------------------------------------------------------------------------- 17 | 18 | import pygame 19 | import sys 20 | from math import * 21 | 22 | import random 23 | import numpy as np 24 | 25 | import pickle 26 | import cPickle 27 | 28 | pygame.init() 29 | 30 | width = 1000 31 | height = 620 32 | display = pygame.display.set_mode((width, height)) 33 | clock = pygame.time.Clock() 34 | 35 | font = pygame.font.SysFont("Agency FB", 20) 36 | font2 = pygame.font.SysFont("Agency FB", 30) 37 | 38 | mutationRate = 5 39 | generation = 0 40 | 41 | bestSoFar = 0 42 | bestGeneration = 0 43 | 44 | 45 | # Maps or Tracks 46 | class Map: 47 | def __init__(self): 48 | self.path = [] 49 | self.mapWidth = 10 50 | self.mapHeight = 10 51 | self.start = (0, 0) 52 | self.blockSize = 60 53 | 54 | def createMap(self): 55 | loop = True 56 | 57 | while loop: 58 | for event in pygame.event.get(): 59 | if event.type == pygame.QUIT: 60 | close() 61 | if event.type == pygame.KEYDOWN: 62 | if event.key == pygame.K_q: 63 | close() 64 | if event.key == pygame.K_s: 65 | loop = False 66 | print(self.path) 67 | if event.type == pygame.MOUSEBUTTONDOWN: 68 | pos = pygame.mouse.get_pos() 69 | i = pos[0]/self.blockSize 70 | j = pos[1]/self.blockSize 71 | if (i, j) in self.path: 72 | self.path.remove((i, j)) 73 | else: 74 | self.path.append((i, j)) 75 | 76 | 77 | display.fill((28, 40, 51)) 78 | 79 | text = font2.render("Create Map", True, (208, 211, 212)) 80 | display.blit(text, (width - 300, 100)) 81 | 82 | text = font2.render("Press S to Start", True, (208, 211, 212)) 83 | display.blit(text, (width - 300, 150)) 84 | 85 | self.drawMap() 86 | 87 | pygame.display.update() 88 | clock.tick() 89 | 90 | 91 | 92 | def drawMap(self): 93 | pygame.draw.rect(display, (236, 240, 241), (self.start[0], self.start[1], self.blockSize*self.mapWidth, self.blockSize*self.mapHeight)) 94 | for i in xrange(self.mapHeight): 95 | for j in xrange(self.mapWidth): 96 | if (i, j) in self.path: 97 | pygame.draw.rect(display, (236, 240, 241), (self.start[0] + (i+1)*1 + i*(self.blockSize - 1), self.start[1] + (j+1)*1 + j*(self.blockSize - 1), 98 | self.blockSize - 2, self.blockSize - 2)) 99 | else: 100 | pygame.draw.rect(display, (52, 73, 94), (self.start[0] + (i+1)*1 + i*(self.blockSize - 1), self.start[1] + (j+1)*1 + j*(self.blockSize - 1), 101 | self.blockSize - 2, self.blockSize - 2)) 102 | 103 | if not self.path == []: 104 | pygame.draw.rect(display, (17, 122, 101), (self.start[0] + (self.path[0][0]+1)*1 + self.path[0][0]*(self.blockSize - 1), 105 | self.start[1] + (self.path[0][1]+1)*1 + self.path[0][1]*(self.blockSize - 1), 106 | self.blockSize - 2, self.blockSize - 2)) 107 | 108 | pygame.draw.rect(display, (203, 67, 53), (self.start[0] + (self.path[-1][0]+1)*1 + self.path[-1][0]*(self.blockSize - 1), 109 | self.start[1] + (self.path[-1][1]+1)*1 + self.path[-1][1]*(self.blockSize - 1), 110 | self.blockSize - 2, self.blockSize - 2)) 111 | 112 | # Brain or the Neural Network Implementation using Numpy 113 | class Brain: 114 | def __init__(self, size): 115 | self.size = size 116 | self.weights = [np.random.randn(y, x) for x, y in zip(size[:-1], size[1:])] 117 | self.biases = [np.random.randn(y, 1) for y in size[1:]] 118 | 119 | def feedForward(self, data): 120 | i = 0 121 | for b, w in zip(self.biases, self.weights): 122 | activation = np.dot(w, data) + b 123 | if i == 0: 124 | data = ReLU(activation) 125 | data = softmax(data) 126 | else: 127 | data = sigmoid(activation) 128 | i += 1 129 | data = softmax(data) 130 | 131 | controller = [] 132 | 133 | for i in xrange(len(data)): 134 | controller.append(np.max(data[i])) 135 | 136 | val = np.argmax(controller) 137 | 138 | if val == 0: 139 | return 1 140 | elif val == 1: 141 | return 0 142 | else: 143 | return -1 144 | 145 | # ReLU activation Function 146 | def ReLU(data): 147 | data[data < 0] = 0 148 | 149 | return data 150 | 151 | # Sigmoid Activation Function 152 | def sigmoid(data): 153 | return 1.0/(1.0 + np.exp(-data)) 154 | 155 | # Softmax or averaging the value 156 | def softmax(data): 157 | summation = np.sum(data) 158 | 159 | if summation == 0.0: 160 | summation = 1.0 161 | 162 | for i in xrange(len(data)): 163 | data[i] = data[i]/summation 164 | 165 | return data 166 | 167 | # Cars 168 | class Car: 169 | def __init__(self, track, color=(241, 196, 15)): 170 | self.x = (track.path[0][0])*track.blockSize + track.blockSize 171 | self.y = (track.path[0][1])*track.blockSize + track.blockSize/2 172 | self.w = 40 173 | self.h = 25 174 | self.angle = 0 175 | self.changeAngle = 5 176 | self.coord = [] 177 | self.speed = 2 178 | 179 | self.color = color 180 | 181 | self.sensor1 = [0, 0] 182 | self.sensor2 = [0, 0] 183 | self.sensor3 = [0, 0] 184 | self.sensor4 = [0, 0] 185 | 186 | self.brain = Brain([4, 5, 3]) 187 | self.crashed = False 188 | 189 | self.completed = False 190 | 191 | self.score = 0 192 | self.fitness = 0 193 | self.prob = 0 194 | 195 | def reset(self): 196 | self.x = 30 197 | self.y = 30 198 | self.w = 40 199 | self.h = 25 200 | self.angle = 0 201 | 202 | self.score = 0.0 203 | self.crashed = False 204 | 205 | def translate(self, coord): 206 | return [coord[0] + self.x, coord[1] + self.y] 207 | 208 | def rotate(self, coord, angle, anchor=(0, 0)): 209 | corr = 180 210 | return ((coord[0] - anchor[0])*cos(angle + radians(corr)) - (coord[1] - anchor[1])*sin(angle + radians(corr)), 211 | (coord[0] - anchor[0])*sin(angle + radians(corr)) + (coord[1] - anchor[1])*cos(angle + radians(corr))) 212 | 213 | def think(self): 214 | inputBrain = [] 215 | # Calculting the distance in sensors 216 | inputBrain.append(((self.x - self.sensor1[0])**2 + (self.y - self.sensor1[1])**2)**0.5) 217 | inputBrain.append(((self.x - self.sensor2[0])**2 + (self.y - self.sensor2[1])**2)**0.5) 218 | inputBrain.append(((self.x - self.sensor3[0])**2 + (self.y - self.sensor3[1])**2)**0.5) 219 | inputBrain.append(((self.x - self.sensor4[0])**2 + (self.y - self.sensor4[1])**2)**0.5) 220 | 221 | result = self.brain.feedForward(inputBrain) 222 | 223 | self.angle += result*self.changeAngle 224 | 225 | def move(self, track): 226 | if not self.crashed: 227 | self.x = self.x + self.speed*cos(radians(self.angle)) 228 | self.y = self.y + self.speed*sin(radians(self.angle)) 229 | 230 | self.score += 0.06 231 | 232 | self.sensor1 = [self.x, self.y] 233 | self.sensor2 = [self.x, self.y] 234 | self.sensor3 = [self.x, self.y] 235 | self.sensor4 = [self.x, self.y] 236 | 237 | # Finding the end point detected by sensors 238 | while True: 239 | self.sensor1[0] += 1*cos(radians(self.angle - 90)) 240 | self.sensor1[1] += 1*sin(radians(self.angle - 90)) 241 | if not (((int(self.sensor1[0])/track.blockSize, int(self.sensor1[1])/track.blockSize)) in track.path): 242 | break 243 | while True: 244 | self.sensor2[0] += 1*cos(radians(self.angle - 45)) 245 | self.sensor2[1] += 1*sin(radians(self.angle - 45)) 246 | if not (((int(self.sensor2[0])/track.blockSize, int(self.sensor2[1])/track.blockSize)) in track.path): 247 | break 248 | while True: 249 | self.sensor3[0] += 1*cos(radians(self.angle + 45)) 250 | self.sensor3[1] += 1*sin(radians(self.angle + 45)) 251 | if not (((int(self.sensor3[0])/track.blockSize, int(self.sensor3[1])/track.blockSize)) in track.path): 252 | break 253 | 254 | while True: 255 | self.sensor4[0] += 1*cos(radians(self.angle + 90)) 256 | self.sensor4[1] += 1*sin(radians(self.angle + 90)) 257 | if not (((int(self.sensor4[0])/track.blockSize, int(self.sensor4[1])/track.blockSize)) in track.path): 258 | break 259 | 260 | points = [(0, 0), (0, self.h), (self.w, self.h), (self.w, 0)] 261 | self.coord = [] 262 | for point in points: 263 | self.coord.append(self.translate(self.rotate(point, radians(self.angle), (self.w/2, self.h/2)))) 264 | 265 | for point in self.coord: 266 | if (int(point[0])/track.blockSize, int(point[1])/track.blockSize) == track.path[-1]: 267 | self.completed = True 268 | if not ((int(point[0])/track.blockSize, int(point[1])/track.blockSize) in track.path): 269 | self.crashed = True 270 | self.fitness = self.score 271 | 272 | self.think() 273 | 274 | def draw(self): 275 | sensorColor = (46, 64, 83) 276 | pygame.draw.line(display, sensorColor, (self.x, self.y), self.sensor1) 277 | pygame.draw.line(display, sensorColor, (self.x, self.y), self.sensor2) 278 | pygame.draw.line(display, sensorColor, (self.x, self.y), self.sensor3) 279 | pygame.draw.line(display, sensorColor, (self.x, self.y), self.sensor4) 280 | 281 | pygame.draw.ellipse(display, sensorColor, (self.sensor1[0], self.sensor1[1], 5, 5)) 282 | pygame.draw.ellipse(display, sensorColor, (self.sensor2[0], self.sensor2[1], 5, 5)) 283 | pygame.draw.ellipse(display, sensorColor, (self.sensor3[0], self.sensor3[1], 5, 5)) 284 | pygame.draw.ellipse(display, sensorColor, (self.sensor4[0], self.sensor4[1], 5, 5)) 285 | 286 | pygame.draw.polygon(display, self.color, self.coord) 287 | 288 | def displayInfo(self, pos): 289 | text = font.render("Score: " + str("{0:.2f}".format(self.score)), True, self.color) 290 | display.blit(text, pos) 291 | 292 | # Population Pool 293 | class Population: 294 | def __init__(self): 295 | self.population = [] 296 | self.crashed = [] 297 | 298 | def createPopulation(self, track): 299 | color = [(231, 76, 60), (142, 68, 173), (52, 152, 219), 300 | (22, 160, 133), (241, 196, 15), (211, 84, 0), 301 | (81, 90, 90), (100, 30, 22), (125, 102, 8), 302 | (26, 82, 118)] 303 | 304 | for i in xrange(len(color)): 305 | car = Car(track, color[i]) 306 | self.population.append(car) 307 | def loadData(self): 308 | # Loading the Previous generation Weights and Biases 309 | index = 0 310 | for i in xrange(len(self.population)): 311 | with open("Trained_Biases/biases_" + str(index) + ".pkl", "rb") as f: 312 | self.population[i].brain.biases = pickle.load(f) 313 | with open("Trained_Weights/weights_" + str(index) + ".pkl", "rb") as f: 314 | self.population[i].brain.weights = pickle.load(f) 315 | index += 1 316 | 317 | def loadBest(self): 318 | # Loading the Best Weights and Biases So Far 319 | index = 0 320 | for i in xrange(len(self.population)): 321 | with open("Trained_Biases_Best/biases_" + str(index) + ".pkl", "rb") as f: 322 | self.population[i].brain.biases = pickle.load(f) 323 | with open("Trained_Weights_Best/weights_" + str(index) + ".pkl", "rb") as f: 324 | self.population[i].brain.weights = pickle.load(f) 325 | index += 1 326 | 327 | def move(self, track): 328 | for car in self.population: 329 | car.move(track) 330 | 331 | def evolve(self, track): 332 | for car in self.population: 333 | if not car.crashed: 334 | return 335 | self.reproduction(track) 336 | 337 | def reproduction(self, track): 338 | global generation, bestGeneration, bestSoFar 339 | generation += 1 340 | 341 | self.normalizeFitness() 342 | 343 | fitness = 0 344 | for car in self.population: 345 | if car.fitness > fitness: 346 | fitness = car.fitness 347 | 348 | bestGeneration = fitness 349 | 350 | if bestGeneration > bestSoFar: 351 | bestSoFar = bestGeneration 352 | save_best_generation(self.population) 353 | 354 | newGen = Population() 355 | 356 | i = 0 357 | 358 | while len(newGen.population) < len(self.population): 359 | indexA = pickOne(self.population[:]) 360 | indexB = pickOne(self.population[:]) 361 | 362 | childA = Car(track, self.population[i].color) 363 | i += 1 364 | childB = Car(track, self.population[i].color) 365 | i += 1 366 | 367 | childA.brain, childB.brain = self.crossover(self.population[indexA].brain, self.population[indexB].brain) 368 | 369 | childA.brain = self.mutation(childA.brain) 370 | childB.brain = self.mutation(childB.brain) 371 | 372 | newGen.population.append(childA) 373 | newGen.population.append(childB) 374 | 375 | self.population = newGen.population[:] 376 | 377 | def crossover(self, brainA, brainB): 378 | newBrain1 = Brain(brainA.size) 379 | newBrain2 = Brain(brainA.size) 380 | 381 | for i in xrange(len(brainA.weights)): 382 | for j in xrange(len(brainA.weights[i])): 383 | for k in xrange(len(brainA.weights[i][j])): 384 | if random.randint(0, 1): 385 | newBrain1.weights[i][j][k] = brainA.weights[i][j][k] 386 | newBrain2.weights[i][j][k] = brainB.weights[i][j][k] 387 | else: 388 | newBrain1.weights[i][j][k] = brainB.weights[i][j][k] 389 | newBrain2.weights[i][j][k] = brainA.weights[i][j][k] 390 | 391 | return newBrain1, newBrain2 392 | 393 | def mutation(self, brain): 394 | prob = random.randint(1, 100) 395 | 396 | if prob <= mutationRate: 397 | for i in xrange(len(brain.weights)): 398 | for j in xrange(len(brain.weights[i])): 399 | k = random.randint(0, len(brain.weights[i][j]) - 1) 400 | val = np.random.randn(1)[0] 401 | brain.weights[i][j][k] = val 402 | 403 | return brain 404 | 405 | def normalizeFitness(self): 406 | summation = 0 407 | 408 | for car in self.population: 409 | summation += car.fitness 410 | 411 | for i in xrange(len(self.population)): 412 | if self.population[i].completed: 413 | self.population[i].prob = 0.9 414 | else: 415 | self.population[i].prob = self.population[i].fitness/summation 416 | 417 | def draw(self): 418 | for car in self.population: 419 | car.draw() 420 | 421 | def displayInfo(self): 422 | text = font2.render("Generation: " + str(generation), True, (240, 243, 244)) 423 | display.blit(text, (width - 375, 100)) 424 | text = font2.render("Best So Far: " + str(bestSoFar), True, (240, 243, 244)) 425 | display.blit(text, (width - 375, 150)) 426 | text = font2.render("Best Last Generation: " + str(bestGeneration), True, (240, 243, 244)) 427 | display.blit(text, (width - 375, 200)) 428 | 429 | text = font2.render("R -> Reset", True, (240, 243, 244)) 430 | display.blit(text, (width - 350, height - 250)) 431 | text = font2.render("S -> Save Current Generation", True, (240, 243, 244)) 432 | display.blit(text, (width - 350, height - 200)) 433 | text = font2.render("L -> Load Previous Best Generation", True, (240, 243, 244)) 434 | display.blit(text, (width - 350, height - 150)) 435 | text = font2.render("B -> Load The Best Generation Ever", True, (240, 243, 244)) 436 | display.blit(text, (width - 350, height - 100)) 437 | 438 | 439 | i = 0 440 | for car in self.population: 441 | car.displayInfo((width - 100, 50 + i*25)) 442 | i += 1 443 | 444 | # Picking Parents based on their fitness score 445 | def pickOne(population): 446 | prob = random.uniform(0, 1) 447 | 448 | for i in xrange(len(population)): 449 | prob -= population[i].prob 450 | if prob < 0: 451 | return i 452 | 453 | # Saving the Weights and Biases 454 | def save_best_generation(population): 455 | i = 0 456 | for species in population: 457 | with open("Trained_Biases/biases_" + str(i) + ".pkl", "wb") as f: 458 | pickle.dump(species.brain.biases, f) 459 | with open("Trained_Weights/weights_" + str(i) + ".pkl", "wb") as f: 460 | pickle.dump(species.brain.weights, f) 461 | i += 1 462 | index = 0 463 | for i in xrange(len(population)): 464 | if population[i].score > population[index].score: 465 | index = i 466 | 467 | save_best_brain(population[index].brain.biases, population[index].brain.weights, population[index].score) 468 | 469 | 470 | def save_best_brain(biases, weights, score): 471 | fp = open("bestScore", "r") 472 | prevScore = float(fp.read()) 473 | fp.close() 474 | 475 | if score > prevScore: 476 | fp = open("bestScore", "w") 477 | fp.write(str(score)) 478 | fp.close() 479 | with open("best_biases.pkl", "wb") as f: 480 | pickle.dump(biases, f) 481 | with open("best_weights.pkl", "wb") as f: 482 | pickle.dump(weights, f) 483 | 484 | 485 | def close(): 486 | pygame.quit() 487 | sys.exit() 488 | 489 | def mainLoop(): 490 | global generation, bestSoFar, bestGeneration 491 | bestSoFar = 0 492 | bestGeneration = 0 493 | generation = 0 494 | loop = True 495 | 496 | track = Map() 497 | track.createMap() 498 | 499 | population = Population() 500 | population.createPopulation(track) 501 | 502 | while loop: 503 | for event in pygame.event.get(): 504 | if event.type == pygame.QUIT: 505 | close() 506 | if event.type == pygame.KEYDOWN: 507 | if event.key == pygame.K_q: 508 | close() 509 | if event.key == pygame.K_r: 510 | mainLoop() 511 | if event.key == pygame.K_s: 512 | save_best_generation(population.population) 513 | if event.key == pygame.K_l: 514 | population.loadData() 515 | if event.key == pygame.K_b: 516 | population.loadBest() 517 | 518 | display.fill((28, 40, 51)) 519 | 520 | track.drawMap() 521 | 522 | population.move(track) 523 | population.draw() 524 | population.evolve(track) 525 | population.displayInfo() 526 | 527 | pygame.display.update() 528 | clock.tick(60) 529 | 530 | mainLoop() 531 | -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases/biases_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xd0\xdb\xa0A\xc3\xbe\xe0\xbf\xb4\xf7h\xc5\x96\x89\xf0\xbf\xd4]uTg\x1e\xd0?\x87\xa8eL=\x86\xe2\xbf\xb2^o\xaeZ\x9f\xc0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xa2\xabh\xc9\x01\xfe\x00\xc0\x0b>\xcb\xae\xafR\xf7\xbf]\x03k\x86\xa9x\xfa\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases/biases_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xe8\xe0!E\xdbM\xe1?\xa9\x11\xcf\xf6\xac\x02\xe4?(=\x9a\x86*\xfe\xe1?\x8f\xfd>\x06\x8e\x1e\xda\xbf\x0f\xbc\x14\x86\xf9\x0c\xf2?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S"\x99+\xc2\xaa1\x86\xd8\xbf\xf1a\xcf \xed'\xf6?\xb2&\x86\xd0B\xb1\xd1\xbf" 52 | p20 53 | tp21 54 | ba. 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| tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'M\xff&\x0c\xff[\xee?E2\xf6\x13~f\xe9\xbf\xb1\n/\x93\x9a\x92\xec?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases/biases_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"\x04\xf5\xcd3\xe3\xd9\xfc??}\x1e.\x11\x8c\xdc\xbfg\xc2\n'\xbc\x05\xf0?\x15\x84\x0b\xd1\xd1\x06\xf0?\xc57\xcbV\xbf\x0e\xef\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\x7f\r\xcc6\x8f\x9b\xf5\xbf\xf8\x8d\xd8@\x84\xf8\xd2?\xcd1\xf4\x92V\xee\xed\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases/biases_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'U\xedS\xf2\xb6m\xf1\xbf\xa69i3\x9e\x14\xe5\xbfBf\xfeW\xae\xee\xf7?U\xe2\xa0\xb4$\xd2\xdc\xbfY\xf0\xbf\xf3\xc2p\xf9\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 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| (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\x04\x18\xdf\xac\xb9j\xe8\xbf\xe8\xe6VH\x1a)\xf1\xbfDZ\xf5u1\xa6\xf6?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_2.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"\xa4H\x85[\x93l\xd0\xbf\xbae\xa7\xdc'V\xf3?\x07 ^\x91\x9c\x10\x04\xc0\x98j\xeaK.\xcf\xe4?\x8d\x19;\x10TX\xfd?" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\t\xa5\xb5\xd1\x01\xd0\xeb?\xb1\xca#\x03\xfds\xe0\xbfL\x8a@%\xb6q\xfa?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_3.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xb0\x96\xeeh\xf4\xe7\xd2?T-\x19\xdb\xeb\x8b\xa1?\x82\xc3\xb0i\x00\xa6\xcd?`M\x17E\xba\xbb\xb3\xbf\xbc\xeb\xf7\xd6r\x83\xe4\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xee:[\xb6\xf5\xa9\xd4\xbfL)r\xb3\xb8\xf1\x9b\xbf\xc3Q\xd3\xc6*N\xf7\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_4.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x0f\xa4\xaeGT\x8d\xd2?2\xca\xb2\xe8,\x19\x02@\xee"\x8b\x1fpg\xf7?E\xad\xdf\xe5\x86\xf1\xff?\xb8\xe3\x9f\xaa?\xbe\x00\xc0' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xc7o\x0e\xbbc\xbc\xe6?{L\xd7\xb5D\xe9\xb6\xbf\x96\x1a.\xa6%7\xf6?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xea\x04~\x06\\%\xc3\xbf\x1b\x8e\xce\xf9\x87\xd0\xf9\xbfS\xeb\x07\xdc%\xca\xff\xbf\xcd0\x9e\xf6\x9bG\xfd\xbf\xb1\xc2\xd0\x14\xaa\x86\xc4\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'xT\xe4R2\xbe\xf1\xbfVg\xeb\xc23\xf1\xc0?\xa9\xd5\xc9\x1d\xcb1\xee\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\xf1\xd3\xbd\x87\x9bQ\xf7?\xf1\xba2\x1fP\xe8\xe4?\xba\xce\x17\xf0\x17\x8c\xc0\xbf\xe7q\xbaxf\xae\xfc\xbf\xa3F\xc6\x87\x19\xe7\xd2?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'%|\x9b\xd5k\xf8\xd8?\xdf\xf6\xf4\x8d*\xc0\xc7\xbf\\\xb1\x88\xd6g;\xca\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'G\xe5\xc1\xf89\xbc\xf3\xbf\xd0\x8d\x15wRd\xe3\xbf\x94c\xbf\x1d5\xcd\xa3\xbfYJh\\\xcd\xba\x00@\xfc\xbb~k\t\xe5\x00@' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L1L 48 | tp19 49 | g11 50 | I00 51 | S'\xc8\x97\xbe\xad\x05\xe1\xe4\xbf\xc1\x80Kw\r\xe3\xd1\xbfB\x05\x0c\xc8\x80H\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Biases_Best/biases_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x0bX\xa2\xcc?g\xf8\xf8Od\xf4\xec?y\xde\xc5a\x90\xc5\xcf?\x07\xd2R\xf4\x85\xf4\xe3?\xa6\xdew5\x9a\xce\xe2\xbf\xeatl\xe4\xf1-\xf6\xbf\x8fP\x83\xd0y]m?\xc0z\x9e\xeaqD\xe8\xbf\x08\xbeC\xd4\x11Q\xce\xbf>;02\xa8\\\xf1?|Z\xb0\xa9\x16c\xd8\xbfT9\xae\x1d\xa3\xa7\xa9?M\x8f\xfb\xb4\x88\xaa\xd1\xbf\xff\x86p[\xea\x88\xeb?\x1cb\x9eHf\x12\xb8\xbf\x1b\x11\xfc\x00\xc3U\xe0?gIl\xa5\x00\xc3\xfc\xbf4\xaef\x9c\x14s\xf2?\xf6V\x91\xcc\x9d\xa0\xe4\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'\xf3\x05R\x85f\x14\xf7\xbf\xb1\xd6\x84\\i\x17\xb9\xbf\x99\xb1\x96\xa6F\xa5\xe8?\xb7\xbd\xba\x06\x18\xf7\xd4\xbf\x9b\xf7Y\xcd\x9f>\xd2?\xb2\x06F\x107~\xe2?e\xc7\xeeg\xd5\xac\xa5\xbfd\xf2xj\x0c \xe2?\xbd\xbd\x18\x19}b\xf5\xbf"\xfe\x87\xa6X\x1d\xe1?}M%%\x8b\t\xd1\xbf\xd8a\x0b!\xcd^\xf4?\xed\x1e\xafqnw\xc4?\x87\x08\xf0\xb2\xd3\x8f\xeb\xbf\xb0\x00\x14\xa5\xa2>\xfb\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights/weights_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"P\xf2\x1c\xfc\x91T\xbc\xbf\t\x86\x8d\xdbL]\xe0\xbf7|\x17\x9f\xc9\xd0p?\xb4p\xa5]\x06j\xe0\xbfh\xbf\x7f\x9dd\xf7\xe1?\x17(\x0c\x1b\x17\xbb\xd6\xbfu~\xcd\xd1<~\xcb?\x89\xbc\xf8:\x91Z\xd3\xbfG\x08m\xa4I6\xea\xbfy\xf5\xc25\x87\xc3\xcd\xbf\xee^\xb6\x9c0&\xf9?p\xb0\xd7|K=\xef?\xcd\x95\xe8\xd6\xf8F\xe5\xbf]\xbd\xb7\xf6\x83A\xc0?&\xc6\xf9\xbfO\xca\xf2?LY\xf0\xcf\x8e\xd7\xe8\xbf\xa6\x14\xc4\x95m\xe3\xc3\xbf'\x1e\t\x9d\xa3\x19\xf0?\x150\xe3\x02\xc4O\xd8\xbf:\xad\xc4=\xb2\x1e\xf6?" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'\xa3\xf7\xf3\x02-\x19\xe6\xbf\xcc-\x18\xc2O\x03\xf9\xbf\xa1\xab2.JG\xff?\xd3\xaa1\x0bl\xf2\xd1\xbf\x909K\x9di\xb2\xdb\xbfW"\x1fHh\x1f\xf3?\xac\xebe\x17\x15>\xdf?n`\xe0\x19\x80\x93\xeb?\xaf8\x17\xca.:\xdc?\x94\xef\xfbq\xe5j\xe2\xbf\x84iG\xc9c,\xc3\xbf\x03\x87\xaf\xdb\xc4M\xe7?\xb8\x96\xb4\x0baI\xf8\xbf\x1caBN\xb8r\xe7?`\xe6\x9f\x89\xf4\xac\x01\xc0' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights/weights_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x7f\xea\x97\xbc7\xb1\xd8?\x0f\xe8\x1a\xa5me\xdd?u\xa7\x8fL\xdc\x07\xdb?\x84P\xa8(\xce\xb7\xd3?\x96\xe1\x90C\xa5\xf3\xf6?\xdb\xea\xef\xb1\xe0s\xd7\xbf\x17\x1a\xb5\xa7\x05\xb8\xe8?\xd7g\x95\xa02\x98\xcc\xbf\xaf\xdb\x02G\xa4\x98\xeb\xbf\x17\xe3\xa1\x14\x91\x97\xfd?V\xb2<\xc9\xa9P\xe2\xbf/\xa3\x9ffN\xfd\xf0\xbf}\xf4;\xeb\x1b\x1b\xfb?\xc0\xcdVM\xfdM\xd5\xbf\x87y\xf5li\r\xf3?\x08\x8b\xb3z\xf3\xf6\xeb\xbf>\x8e\xfd\xc0\xbf\x18\xf5?\xa8hT\x04mQ\xf4\xbf\xbc^\x87\xe8\xffA\xfd?N\x83\xa0Z\x06~\xf5\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'>\xb9@)\\\x9d\xe7?r`\x83x\xf5\xb1\xe7\xbf\x97!=\xae\xdd\x96\xd3?\x8d\x08c\x7f.n\xff\xbf\xb0L\x9cF\xf2\x8a\xfe?\x1a\xdc\xfcC)J\xe4\xbfw\x83\xfb\x02\xf2\x8d\xb9?2\xd0\xc0\xe5\x0c\xcc\xf0?|+\xd6%J&\xe1?M\xe69@\xfc\x0c\xbf\xbf\xfcW\xa4\x15K\xdb\xd4?wH\xa01\x0f\xc2\xf0\xbf\x19\xfeVF\xcb\xad\xd8?\x83\x8f\xce\x8c\xf9\x02\xcd?\xf7{Te\xcd%\xf4\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights/weights_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x9bFC\x84\x13A\xba?2\xed\xa3\xf8\xb3L\x02\xc0\x1f"}\xf0\xc2\x05\xf8\xbf\xaeDG\n\x14(\xf5\xbf\xd5\xc5\x8b\xe2\x96~\xe7\xbf\xf5Bt\x84\x0ef\xfc\xbf\x1anOK\xa9\xaa\xf9\xbf\xdc\x8b\xf76\x1f\x10\xf2\xbf\xfcX\x9c~i-\xed\xbf\xef\x1f\x7f\xfc\xc9N\xbd?\x94\x84\xd1j4%\xef\xbf\x8e3d\xc1g\xfe\xec\xbf^D\xee\x00\xaa\x1f\xf3?1\x9b*\xe2\xfa\xca\xa4?\xc3-\x06S\x05\xa8\n@\xfdc\x8f\xf7t\t\x01@\xa1\xa7\xd6\x99\x0bZ\xe7\xbf\xa6oj\xe7\x19\xc8\xee?5zAR\x05\x93\xf5?\xb41\xadu\t\x92\xf6\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'\xd1\xb2;\n\xbf\x8a\xeb?\x8d\xe3q\x1b\x95N\xdc?\xd4d\x9e\xd3\xf0\xbf\xf5\xbf\nl\xda\x81\xbc\x84\xe1\xbf\x8d\xa17\xc5\n%\xe9\xbfT\x99\xc6L\x91y\xd4\xbf\x14\xf9\x1a`\xa8\xf6\xd8\xbf\x9a\xa8\xb6\x03\xff\xa4\xe6\xbf\xf6\xca\xea\xbe\x8d|]\xc2\xaf\xe5?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights/weights_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'FN\xfbX\x7f\x13\xf7?bC\x9b\xacN\x18\xd5?:\x04\xcd\xbf\xdd\x15\xdf?\xf2\xa7}\xe7S\xfb\xec\xbf\t4\xd1\xdf\xdb\x17\xc0?\x82m\xba\x9e\x9a\xc8\xf9?\x18\xb21@\xea\x98\xe0?\xc0\x05\xeb\xc3u\xfe\xd0\xbf\x87\xf0v\x15\x81\x18\xee\xbf=\x1a\\\x0b\xa3\xa7\xe0?\xb5Fb\x8d\xc7F\xe3\xbf\xc0?\xd6\xe7Gd\x8a\xbf\xf4\xb1\xe4\x16\xb8\xe9\xbc?E\xe8]\xc9\x08\x1c\xec\xbf\xa6\xf6%\x19\xa8\xbd\xe9\xbfg\x1f\x1c\xcf\xa5\x9e\xf6?E+\x9fy1o\xf9?\xb6K\x9dKgS\xf0?/\x16\xe4\xe4h&m\xbf*mh\xb2E\xa0\xd3?' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'\xcc\x0c\xec\x19\xac\xea\xee\xbf\xc3F\xe1A\x95\xe3\xd6?\xe2\x11\xd0b\xbf\x19\xda?g\xf9c\xc4\xbf\xd9\xe4\xbf\xe2s\xed?\xd5\xdd\xf2?\xc8c2\xf5\xb6w\xe2?BMr\xbc`\xc5\xf0?\x03\x14mtT\xe0\xe6?\xa7\r\xe03\xf8\xeaw?\xdc\xa8\xaep\x14:\xd4?\xc8h7\x91\x80\xd0\xc6\xbf\x00\xae\xf0\xear\x13\xf5\xbf\xe9\xb6\xf7\x98\x10\xbc\xbd?W\t\xe55hl\xe0?\x8e\x0c?xm\xf1\xea\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights/weights_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S"\xaaY\xf6\x8fk\x02\xd9?\xb6\xcdY\x9a^x\x01\xc0\xb2a;\x1c\xd44\xe8\xbfR\xb9\xfc8\xb5\xb3\xec?r\xa0\x9b\x8f\x05\x86\x03\xc03\xb5\xa2\xf9\xdb\xb3\xc8\xbf*\xe1:'Z\xdb\xd0?.H?\xb2\xe7M\xe1?D$\x04\x8d\xcfO\x8c?\xbd\xb9\xc9\xdf{\x80\xe9\xbf\x98j\x1cw\x05\x15\xd5?+5\x8aZR;\xf7\xbf\x9b$\xdbR\xe33\xd5\xbf%t\x80'\xe6\xc1\x03\xc0\x9c\r\x970J\xe4\xdd\xbf(W\xe9\x7f\x87{\xf0\xbf)\xbbK\xa5\xa0\xab\xef\xbfA\x90hS\xa5\xf8\xe9?=\xae\x8e\xf6\x0e\xf0\xe9\xbf\xe3\xfb5\xd6\xfc\x9b\xb4\xbf" 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'\x10\x88\xf2\x00\xe9L\xea?\n\'\xb6\x80L\x88\xec\xbf\xd1\x1fv\x7f\xc7\xc6\xe1?V\xdbu\xd6\xf4\x99\xf0?\x02}\xea~\xb5\xfb\xec\xbf\xaa*\xb3\xee\xa7\x8bf\xbf}\x85o\xb8\xad\x98\xe3?\x04AS\xd2\xd4\xdd\xea?\xcbSJsQp\xe7?\xea\x87\xbad\xc0\x9f\xd3?\x15\x1e\x11?W"\xe9?\xac\x10\xac\x13\n\xff\xb2\xbf\x90\xcc\xc9\x97\xdd\xdd\xe2\xbf\xa9\xb7{x\x91\x02\xc9?A\x07\xc2b\xafO\xfa\xbf' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_0.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_1.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_2.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_3.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_4.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_5.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_6.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_7.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_8.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/Trained_Weights_Best/weights_9.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L4L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'9\x94\x8ck\xbd\xf4\x01@-\xddE\xcbT\x13\xe9?\xe0S\x8a~\xf9\xba\xec?\x02\xd4\xd2S\xa3]\xad?\x07\x04\x05mtc\xe1\xbfw\xf4v\xd2\xc0\t\xa7?i\x16\x82E\x1f\x84\xf3\xbf\xe2h9m\xd5\xbb\xd5\xbfG\xee\xefl\x8a\xcf\xfa?\xee\x88\xdbN\xebO\xf1?(\xcc\xa7\x1d\x12\xac\xe2\xbfX\xe5A\xd6\xfd\x85\xb6\xbf\x00I\xd2\xf8\xad\xc4\xe1\xbfh\x172\xb6\x84@\x01\xc0\xadU\xe1R,H\xe7\xbf0`\xcd%@\x1f\xf3\xbf\xeas\xf7\xd3p\xa6\xb5\xbf5\x06\x81\xeb*\x83\xe6\xbf\x90\xa14`E\x12\xd5\xbf \x01\xfd\xd9\xca\xe1\xf0\xbf' 36 | p14 37 | tp15 38 | bag1 39 | (g2 40 | (I0 41 | tp16 42 | g4 43 | tp17 44 | Rp18 45 | (I1 46 | (L3L 47 | L5L 48 | tp19 49 | g11 50 | I00 51 | S'3\xb6k\x96!\x04\xa4\xbfbI\x08\xc5\xc9\xcb\x02@\xa0/\xb8F\xa0\x98\xe2\xbf\xcfr r9\xe4\xeb?v\xeb>R\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/bestScore: -------------------------------------------------------------------------------- 1 | 6938.46000002 -------------------------------------------------------------------------------- /Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/best_biases.pkl: -------------------------------------------------------------------------------- 1 | (lp0 2 | cnumpy.core.multiarray 3 | _reconstruct 4 | p1 5 | (cnumpy 6 | ndarray 7 | p2 8 | (I0 9 | tp3 10 | S'b' 11 | p4 12 | tp5 13 | Rp6 14 | (I1 15 | (L5L 16 | L1L 17 | tp7 18 | cnumpy 19 | dtype 20 | p8 21 | (S'f8' 22 | p9 23 | I0 24 | I1 25 | tp10 26 | Rp11 27 | (I3 28 | S'<' 29 | p12 30 | NNNI-1 31 | I-1 32 | I0 33 | tp13 34 | bI00 35 | S'\x0bR\xed\xff\x08@%\xe9@A\xa6\x0f\xd7\xbf&\xf3#\xbaT\xae\xf4\xbf\xee\x0c\xdc\xff\xfe\x07\xf8\xbf\xed6\xb7\x13\xc3\x83\xc4?W\x9b=\x13+^\xdd?\xbc^PO\xbf`\xf1?\x94L\xf0K\xaf\xbb\xb3\xbfx\x1d>d\x92\xb1\xe5\xbf\xc7\xed>}?\x10\xe0\xbfR\x96)\xae\xff\xb3\xdd?' 52 | p20 53 | tp21 54 | ba. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Intelligent-Learning 2 | 3 | Here you can find implementations of some "intelligent" systems that I programmed mostly from scratch. 4 | 5 | More information can be found on my blog: [Mandav's Blog](https://jatinmandav.wordpress.com/category/intelligent-learning/) 6 | 7 | ## Genetic Algorithm and Neural Networks 8 | Genetic Algorithm, one of the algorithm that fascinate me the most, when combined with Neural Networks, can be really powerful. 9 | 10 | - [Intelligent Flappy Bird](Genetic-Algorithm-And-Neural-Networks/Intelligent_Flappy_Bird/) 11 | - Video: [A.I. Learns to Play Flappy-Bird](https://www.youtube.com/watch?v=H9BY-xr2QBY) 12 | - More Information: [A.I. Learns to Play Flappy-Bird](https://jatinmandav.wordpress.com/2018/03/05/a-i-learns-to-play-flappy-bird/) 13 | - [Self-Driving Cars](Genetic-Algorithm-And-Neural-Networks/Self-Driving-Cars/) 14 | - Video: [Car/A.I. Learns to Drive](https://www.youtube.com/watch?v=_TGGbPjT7pg) 15 | - More Information: [A.I. Learns to Drive](https://jatinmandav.wordpress.com/2018/03/09/a-i-learns-to-drive) 16 | --------------------------------------------------------------------------------