import multiprocessing import random from neuralNetwork import NeuralNetwork from environment import Environment num_generations = 10000 networks_per_generation = 1000 steps_per_network = 100 core = 4 best_network = NeuralNetwork() best_network.generate(36, 36, 4) random.seed() best_net_steps = 0 best_net_clean = 0 def evolution_step(): possible_values = [-1, 1] change = random.random() > .7 if change: return random.choice(possible_values) return 0 def signals_to_movement(signals): movement_x = signals[0] - signals[1] movement_y = signals[2] - signals[3] if movement_x > movement_y: return 'right' if movement_x > 0 else 'left' elif movement_y > movement_x:
import multiprocessing import random from neuralNetwork import NeuralNetwork from environment import Environment num_generations = 10000 networks_per_generation = 1000 steps_per_network = 100 core = 4 best_network = NeuralNetwork() best_network.generate(36, 36, 4) random.seed() best_net_steps = 0 best_net_clean = 0 def evolution_step(): possible_values = [-1, 1] change = random.random() > .7 if change: return random.choice(possible_values) return 0 def signals_to_movement(signals): movement_x = signals[0] - signals[1] movement_y = signals[2] - signals[3] if movement_x > movement_y: return 'right' if movement_x > 0 else 'left' elif movement_y > movement_x: