基于python的AI自动躲避飞机的小游戏，神经网络，遗传算法.zip

#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Tue Jan 17 16:46:22 2017 @author: liupeng """ import math import random # 神经网络3层, 1个隐藏层; 4个input和1个output network = [4, [16], 1] # 遗传算法相关 population = 50 elitism = 0.2 random_behaviour = 0.1 mutation_rate = 0.5 mutation_range = 2 historic = 0 low_historic = False score_sort = -1 n_child = 1 # 激活函数 def sigmoid(z): return 1.0/(1.0+math.exp(-z)) # random number def random_clamped(): return random.random()*2-1 # "神经元" class Neuron(): def __init__(self): self.biase = 0 self.weights = [] def init_weights(self, n): self.weights = [] for i in range(n): self.weights.append(random_clamped()) def __repr__(self): return 'Neuron weight size:{} biase value:{}'.format(len(self.weights), self.biase) # 层 class Layer(): def __init__(self, index): self.index = index self.neurons = [] def init_neurons(self, n_neuron, n_input): self.neurons = [] for i in range(n_neuron): neuron = Neuron() neuron.init_weights(n_input) self.neurons.append(neuron) def __repr__(self): return 'Layer ID:{} Layer neuron size:{}'.format(self.index, len(self.neurons)) # 神经网络 class NeuroNetwork(): def __init__(self): self.layers = [] # input:输入层神经元数 hiddens:隐藏层 output:输出层神经元数 def init_neuro_network(self, input, hiddens , output): index = 0 previous_neurons = 0 # input layer = Layer(index) layer.init_neurons(input, previous_neurons) previous_neurons = input self.layers.append(layer) index += 1 # hiddens for i in range(len(hiddens)): layer = Layer(index) layer.init_neurons(hiddens[i], previous_neurons) previous_neurons = hiddens[i] self.layers.append(layer) index += 1 # output layer = Layer(index) layer.init_neurons(output, previous_neurons) self.layers.append(layer) def get_weights(self): data = { 'network':[], 'weights':[] } for layer in self.layers: data['network'].append(len(layer.neurons)) for neuron in layer.neurons: for weight in neuron.weights: data['weights'].append(weight) return data def set_weights(self, data): previous_neurons = 0 index = 0 index_weights = 0 self.layers = [] for i in data['network']: layer = Layer(index) layer.init_neurons(i, previous_neurons) for j in range(len(layer.neurons)): for k in range(len(layer.neurons[j].weights)): layer.neurons[j].weights[k] = data['weights'][index_weights] index_weights += 1 previous_neurons = i index += 1 self.layers.append(layer) # 输入游戏环境中的一些条件(如敌机位置), 返回要执行的操作 def feed_forward(self, inputs): for i in range(len(inputs)): self.layers[0].neurons[i].biase = inputs[i] prev_layer = self.layers[0] for i in range(len(self.layers)): # 第一层没有weights if i == 0: continue for j in range(len(self.layers[i].neurons)): sum = 0 for k in range(len(prev_layer.neurons)): sum += prev_layer.neurons[k].biase * self.layers[i].neurons[j].weights[k] self.layers[i].neurons[j].biase = sigmoid(sum) prev_layer = self.layers[i] out = [] last_layer = self.layers[-1] for i in range(len(last_layer.neurons)): out.append(last_layer.neurons[i].biase) return out def print_info(self): for layer in self.layers: print(layer) # "基因组" class Genome(): def __init__(self, score, network_weights): self.score = score self.network_weights = network_weights class Generation(): def __init__(self): self.genomes = [] def add_genome(self, genome): i = 0 for i in range(len(self.genomes)): if score_sort < 0: if genome.score > self.genomes[i].score: break else: if genome.score < self.genomes[i].score: break self.genomes.insert(i, genome) # 杂交+突变 def breed(self, genome1, genome2, n_child): datas = [] for n in range(n_child): data = genome1 for i in range(len(genome2.network_weights['weights'])): if random.random() <= 0.5: data.network_weights['weights'][i] = genome2.network_weights['weights'][i] for i in range(len(data.network_weights['weights'])): if random.random() <= mutation_rate: data.network_weights['weights'][i] += random.random() * mutation_range * 2 - mutation_range datas.append(data) return datas # 生成下一代 def generate_next_generation(self): nexts = [] for i in range(int(round(elitism*population))): if len(nexts) < population: nexts.append(self.genomes[i].network_weights) for i in range(int(round(random_behaviour*population))): n = self.genomes[0].network_weights for k in range(len(n['weights'])): n['weights'][k] = random_clamped() if len(nexts) < population: nexts.append(n) max_n = 0 while True: for i in range(max_n): childs = self.breed(self.genomes[i], self.genomes[max_n], n_child if n_child > 0 else 1) for c in range(len(childs)): nexts.append(childs[c].network_weights) if len(nexts) >= population: return nexts max_n += 1 if max_n >= len(self.genomes)-1: max_n = 0 class Generations(): def __init__(self): self.generations = [] def first_generation(self): out = [] for i in range(population): nn = NeuroNetwork() nn.init_neuro_network(network[0], network[1], network[2]) out.append(nn.get_weights()) self.generations.append(Generation()) return out def next_generation(self): if len(self.generations) == 0: return False gen = self.generations[-1].generate_next_generation() self.generations.append(Generation()) return gen def add_genome(self, genome): if len(self.generations) == 0: return False return self.generations[-1].add_genome(genome) class NeuroEvolution(): def __init__(self): self.generations = Generations() def restart(self): self.generations = Generations() def next_generation(self): networks = [] if len(self.generations.generations) == 0: networks = self.generations.first_generation() else: networks = self.generations.next_generation() nn = [] for i in range(len(networks)): n = NeuroNetwork() n.set_weigh