是AI就躲个飞机-纯Python实现人工智能

MachineLP

发布于 2018-01-09 11:29:22

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发布于 2018-01-09 11:29:22

文章被收录于专栏：小鹏的专栏

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 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

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)

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(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_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

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()))

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.x*1.0 / SCREEN_SIZE[0])
		index = 1
		for eneme in enemes:
			inputs[index] = eneme.x*1.0 / 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()

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：