是AI就躲个飞机-纯Python实现人工智能

代码下载：Here。

很久以前微信流行过一个小游戏：打飞机，这个游戏简单又无聊。在2017年来临之际，我就实现一个超级弱智的人工智能（AI），这货可以躲避从屏幕上方飞来的飞机。本帖只使用纯Python实现，不依赖任何高级库。

本文的AI基于neuro-evolution，首先简单科普一下neuro-evolution。从neuro-evolution这个名字就可以看出它由两部分组成-neuro and evolution，它是使用进化算法（遗传算法是进化算法的一种）提升人工神经网络的机器学习技术，其实就是用进化算法改进并选出最优的神经网络。

• 使用TPOT自动选择scikit-learn机器学习模型和参数

neuro-evolution

定义一些变量：

import mathimport random # 神经网络3层, 1个隐藏层; 4个input和1个outputnetwork = [4, [16], 1]# 遗传算法相关population = 50elitism = 0.2 random_behaviour = 0.1mutation_rate = 0.5mutation_range = 2historic = 0low_historic = Falsescore_sort = -1n_child = 1

定义神经网络：

# 激活函数def sigmoid(z):return 1.0/(1.0+math.exp(-z))# random numberdef random_clamped():return random.random()*2-1 # "神经元"class Neuron():def __init__(self):self.biase = 0self.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 = indexself.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 = 0previous_neurons = 0# inputlayer = Layer(index)layer.init_neurons(input, previous_neurons)previous_neurons = inputself.layers.append(layer)index += 1# hiddensfor 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# outputlayer = 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 = 0index = 0index_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 += 1previous_neurons = iindex += 1self.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weightsif i == 0:continuefor j in range(len(self.layers[i].neurons)):sum = 0for 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)

遗传算法：

# "基因组"class Genome():def __init__(self, score, network_weights):self.score = scoreself.network_weights = network_weights class Generation():def __init__(self):self.genomes = [] def add_genome(self, genome):i = 0for i in range(len(self.genomes)):if score_sort < 0:if genome.score > self.genomes[i].score:breakelse:if genome.score < self.genomes[i].score:breakself.genomes.insert(i, genome)         # 杂交+突变def breed(self, genome1, genome2, n_child):datas = []for n in range(n_child):data = genome1for 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_rangedatas.append(data)return datas         # 生成下一代def generate_next_generation(self):nexts = []for i in range(round(elitism*population)):if len(nexts) < population:nexts.append(self.genomes[i].network_weights) for i in range(round(random_behaviour*population)):n = self.genomes[0].network_weightsfor k in range(len(n['weights'])):n['weights'][k] = random_clamped()if len(nexts) < population:nexts.append(n) max_n = 0while 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 nextsmax_n += 1if max_n >= len(self.genomes)-1:max_n = 0

NeuroEvolution：

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 outdef 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_weights(networks[i])nn.append(n) if low_historic:if len(self.generations.generations) >= 2:genomes = self.generations.generations[len(self.generations.generations) - 2].genomesfor i in range(genomes):genomes[i].network = None if historic != -1:if len(self.generations.generations) > historic+1:del self.generations.generations[0:len(self.generations.generations)-(historic+1)] return nn def network_score(self, score, network):self.generations.add_genome(Genome(score, network.get_weights()))

是AI就躲个飞机

import pygameimport sysfrom pygame.locals import *import randomimport math import neuro_evolution BACKGROUND = (200, 200, 200)SCREEN_SIZE = (320, 480) class Plane():def __init__(self, plane_image):self.plane_image = plane_imageself.rect = plane_image.get_rect() self.width = self.rect[2]self.height = self.rect[3]self.x = SCREEN_SIZE[0]/2 - self.width/2self.y = SCREEN_SIZE[1] - self.height self.move_x = 0self.speed = 2 self.alive = True def update(self):self.x += self.move_x * self.speed def draw(self, screen):screen.blit(self.plane_image, (self.x, self.y, self.width, self.height)) def is_dead(self, enemes):if self.x < -self.width or self.x + self.width > SCREEN_SIZE[0]+self.width:return True for eneme in enemes:if self.collision(eneme):return Truereturn False def collision(self, eneme):if not (self.x > eneme.x + eneme.width or self.x + self.width < eneme.x or self.y > eneme.y + eneme.height or self.y + self.height < eneme.y):return Trueelse:return False def get_inputs_values(self, enemes, input_size=4):inputs = [] for i in range(input_size):inputs.append(0.0) inputs[0] = (self.x*1.0 / SCREEN_SIZE[0])index = 1for eneme in enemes:inputs[index] = eneme.x*1.0 / SCREEN_SIZE[0]index += 1inputs[index] = eneme.y*1.0 / SCREEN_SIZE[1]index += 1#if len(enemes) > 0:#distance = math.sqrt(math.pow(enemes[0].x + enemes[0].width/2 - self.x + self.width/2, 2) + math.pow(enemes[0].y + enemes[0].height/2 - self.y + self.height/2, 2));if len(enemes) > 0 and self.x < enemes[0].x:inputs[index] = -1.0index += 1else:inputs[index] = 1.0 return inputs class Enemy():def __init__(self, enemy_image):self.enemy_image = enemy_imageself.rect = enemy_image.get_rect() self.width = self.rect[2]self.height = self.rect[3]self.x = random.choice(range(0, int(SCREEN_SIZE[0] - self.width/2), 71))self.y = 0 def update(self):self.y += 6 def draw(self, screen):screen.blit(self.enemy_image, (self.x, self.y, self.width, self.height)) def is_out(self):return True if self.y >= SCREEN_SIZE[1] else False class Game():def __init__(self):pygame.init()self.screen = pygame.display.set_mode(SCREEN_SIZE)self.clock = pygame.time.Clock()pygame.display.set_caption('是AI就躲个飞机') self.ai = neuro_evolution.NeuroEvolution()self.generation = 0 self.max_enemes = 1                # 加载飞机、敌机图片self.plane_image = pygame.image.load('plane.png').convert_alpha()self.enemy_image = pygame.image.load('enemy.png').convert_alpha() def start(self):self.score = 0self.planes = []self.enemes = [] self.gen = self.ai.next_generation()for i in range(len(self.gen)):plane = Plane(self.plane_image)self.planes.append(plane) self.generation += 1self.alives = len(self.planes) def update(self, screen):for i in range(len(self.planes)):if self.planes[i].alive:inputs = self.planes[i].get_inputs_values(self.enemes)res = self.gen[i].feed_forward(inputs)if res[0] < 0.45:self.planes[i].move_x = -1elif res[0] > 0.55:self.planes[i].move_x = 1  self.planes[i].update()self.planes[i].draw(screen) if self.planes[i].is_dead(self.enemes) == True:self.planes[i].alive = Falseself.alives -= 1self.ai.network_score(self.score, self.gen[i])if self.is_ai_all_dead():self.start() self.gen_enemes() for i in range(len(self.enemes)):self.enemes[i].update()self.enemes[i].draw(screen)if self.enemes[i].is_out():del self.enemes[i]break self.score += 1 print("alive:{}, generation:{}, score:{}".format(self.alives, self.generation, self.score), end='\r') def run(self, FPS=1000):while True:for event in pygame.event.get():if event.type == QUIT:pygame.quit()sys.exit() self.screen.fill(BACKGROUND) self.update(self.screen) pygame.display.update()self.clock.tick(FPS) def gen_enemes(self):if len(self.enemes) < self.max_enemes:enemy = Enemy(self.enemy_image)self.enemes.append(enemy) def is_ai_all_dead(self):for plane in self.planes:if plane.alive:return Falsereturn True  game = Game()game.start()game.run()

AI的工作逻辑

假设你是AI，你首先繁殖一个种群（50个个体），开始的个体大都是歪瓜裂枣（上来就被敌机撞）。但是，即使是歪瓜裂枣也有表现好的，在下一代，你会使用这些表现好的再繁殖一个种群，经过代代相传，存活下来的个体会越来越优秀。其实就是仿达尔文进化论，种群->自然选择->优秀个体->杂交、变异->种群->循环n世代。

ai开始时候的表现：

图片被拉扁了 sorry

经过几百代之后，ai开始娱乐的躲飞机：

ps.祝大家新年快乐，感觉我又浪费了一年。

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