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。如果你觉得这篇文章看起来稍微还有些吃力，或者想要系统地学习人工智能，那么推荐你去看床长人工智能教程。非常棒的大神之作，教程不仅通俗易懂，而且很风趣幽默。点击这里可以查看教程。
neuro-evolution
定义一些变量：
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
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定义神经网络：
# 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)
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遗传算法：
# "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(round(elitismpopulation)):
if len(nexts) < population:
nexts.append(self.genomes[i].network_weights)
for i in range(round(random_behaviourpopulation)):
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
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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 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_weights(networks[i])
nn.append(n)
if low_historic:
if len(self.generations.generations) >= 2:
genomes = self.generations.generations[len(self.generations.generations) - 2].genomes
for 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()))
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是AI就躲个飞机
import pygame
import sys
from pygame.locals import
import random
import math
import neuro_evolution
BACKGROUND = (200, 200, 200)
SCREEN_SIZE = (320, 480)
class Plane():
def init(self, plane_image):
self.plane_image = plane_image
self.rect = plane_image.get_rect()
self.width = self.rect[2]
self.height = self.rect[3]
self.x = SCREEN_SIZE[0]/2 - self.width/2
self.y = SCREEN_SIZE[1] - self.height
self.move_x = 0
self.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 True
return 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 True
else:
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.x1.0 / SCREEN_SIZE[0])
index = 1
for eneme in enemes:
inputs[index] = eneme.x1.0 / SCREEN_SIZE[0]
index += 1
inputs[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] = -1.0
index += 1
else:
inputs[index] = 1.0
return inputs
class Enemy():
def init(self, enemy_image):
self.enemy_image = enemy_image
self.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 = 0
self.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 += 1
self.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 = -1
elif 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 = False
self.alives -= 1
self.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 False
return True
game = Game()
game.start()
game.run()
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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：
经过几百代之后，ai开始娱乐的躲飞机.
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