如何解决我没有让我的 AI 玩井字游戏我怎样才能提高它的性能？
AI 是用neat-python 构建的。输入是作为字段列表的游戏板 (0:unoccupied,1:player1,2:player2)。 AI 产生 9 个输出，并且 max(output) 的索引被选为要使用的字段。它针对随机选择字段的机器人进行训练。如果要走的字段已被占用，则将第一个空闲字段设置为要走的字段。
import neat
import pygame
import os
import json
import random
import checkpoint
import winsound
import time
import matplotlib.pyplot as plt
pygame.init()
cloud = checkpoint.Checkpointer()
p = None
GEN = 0
screen = None
pygame.display.set_caption("Tic Tac Toe")
o_icon = pygame.image.load("o.png")
x_icon = pygame.image.load("x.png")
class Field:
def __init__(self):
self.field = [0,0]
self.winner = None
def reset(self):
self.field = [0,0]
self.winner = None
def display(self):
screen.fill([0,0])
self.display_pieces()
self.display_board()
pygame.display.update()
def display_pieces(self):
pos_to_pix = {
0: (0,0),1: (100,2: (200,3: (0,100),4: (100,5: (200,6: (0,200),7: (100,8: (200,200)
}
for pos in range(len(self.field)):
if self.field[pos] == 1:
screen.blit(x_icon,pos_to_pix[pos])
elif self.field[pos] == 2:
screen.blit(o_icon,pos_to_pix[pos])
@staticmethod
def pos_to_field(pos):
x = pos[0]
y = pos[1]
if x < 100:
x = 0
elif x < 200:
x = 1
else:
x = 2
if y < 100:
y = 0
elif y < 200:
y = 1
else:
y = 2
return x+y*3
@staticmethod
def display_board():
pygame.draw.rect(screen,"White",[0,99,300,2])
pygame.draw.rect(screen,199,[99,2,300])
pygame.draw.rect(screen,[199,300])
def action(self,field_id,player_id):
self.field[field_id] = player_id
def field_is_free(self,field):
if self.field[field] == 0:
return True
return False
def check_winner(self):
for line in range(3):
# check horizontal
if self.field[line*3] == self.field[line*3+1] == self.field[line*3+2] != 0:
self.winner = self.field[line*3]
return True,self.winner
if self.field[line] == self.field[line+3] == self.field[line+6] != 0:
self.winner = self.field[line]
return True,self.winner
if self.field[0] == self.field[4] == self.field[8] != 0:
self.winner = self.field[0]
return True,self.winner
if self.field[2] == self.field[4] == self.field[6] != 0:
self.winner = self.field[2]
return True,self.winner
return False
def first_free(self):
for index,value in enumerate(self.field):
if value == 0:
return index
class Player:
def __init__(self,id,being,brain=None):
self.id = id
self.being = being
self.brain = brain
class Graph:
def __init__(self):
self.gens = []
self.maxs = []
self.losts = []
self.drawns = []
self.wons = []
self.average = []
self.last_gens = 50
self.vmax = None
def add(self,gen,vmax_round,lost,drawn,won):
self.gens.append(gen)
self.maxs.append(vmax_round)
self.losts.append(lost)
self.drawns.append(drawn)
self.wons.append(won)
self.average.append(sum(self.maxs[-min(self.last_gens,gen):]) / min(self.last_gens,gen))
self.vmax = max(self.maxs)
def display(self):
plt.figure(figsize=(10,6))
plt.plot(self.gens,self.maxs,label="Best fitness")
plt.plot(self.gens,self.average,label="Average fitness last " + str(self.last_gens) + " generations")
plt.plot(self.gens,self.losts,label="Lost")
plt.plot(self.gens,self.drawns,label="Drawn")
plt.plot(self.gens,self.wons,label="Won")
plt.legend(bBox_to_anchor=(1.05,1),loc="upper left")
plt.grid("on")
plt.tight_layout()
plt.show()
def save(self):
with open('gens.txt','w') as filehandle:
json.dump(self.gens,filehandle)
with open('maxs.txt','w') as filehandle:
json.dump(self.maxs,filehandle)
with open('average.txt','w') as filehandle:
json.dump(self.average,filehandle)
with open('losts.txt','w') as filehandle:
json.dump(self.losts,filehandle)
with open('drawns.txt','w') as filehandle:
json.dump(self.drawns,filehandle)
with open('wons.txt','w') as filehandle:
json.dump(self.wons,filehandle)
def load(self):
with open('gens.txt','r') as filehandle:
self.gens = json.load(filehandle)
with open('maxs.txt','r') as filehandle:
self.maxs = json.load(filehandle)
with open('average.txt','r') as filehandle:
self.average = json.load(filehandle)
with open('losts.txt','r') as filehandle:
self.losts = json.load(filehandle)
with open('drawns.txt','r') as filehandle:
self.drawns = json.load(filehandle)
with open('wons.txt','r') as filehandle:
self.wons = json.load(filehandle)
graph = Graph()
board = Field()
def place_piece(player,field):
board.field[field] = player.id
def sound():
winsound.Beep(440,200)
def train(genomes,config):
global GEN
GEN += 1
best_fitness = 0
players = [None,None]
best_lost,best_drawn,best_won = 0,0
for index,genome in genomes:
index -= 1
genome.fitness = 0
net = neat.nn.FeedForwardNetwork.create(genome,config)
players[0] = Player(1,"random")
players[1] = Player(2,"ai")
lost,won = 0,0
for round in range(100):
if round == 50:
players[0] = Player(1,"ai")
players[1] = Player(2,"random")
field_to_go = -1
placed = 0
player = players[0]
while True:
if player.being == "random":
field_to_go = random.randint(0,8)
elif player.being == "ai":
output = net.activate(board.field)
field_to_go = output.index(max(output))
elif player.being == "human":
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
break
elif event.type == pygame.MOUSEBUTTONUP:
field_to_go = board.pos_to_field(pygame.mouse.get_pos())
if field_to_go > -1:
if board.field_is_free(field_to_go):
board.action(field_to_go,player.id)
else:
board.action(board.first_free(),player.id)
if player == players[0]:
player = players[1]
else:
player = players[0]
placed += 1
if shown:
board.display()
time.sleep(1)
if board.check_winner() or placed == 9:
if board.winner is None:
genome.fitness += 1
drawn += 1
elif players[board.winner-1].being == "ai":
genome.fitness += 2
won += 1
else:
genome.fitness -= 0
lost += 1
board.reset()
break
if genome.fitness > best_fitness:
best_fitness = genome.fitness
best_lost = lost
best_drawn = drawn
best_won = won
graph.add(GEN,best_fitness,best_lost,best_won)
def test(genome):
result = [0,0]
players = [None,None]
for round in range(1000):
placed = 0
if round % 2 == 0:
players[0] = Player(1,"ai")
players[1] = Player(2,"human")
else:
players[0] = Player(1,"human")
players[1] = Player(2,"ai")
player = players[0]
while True:
field_to_go = -1
if player.being == "random":
field_to_go = random.randint(0,8)
elif player.being == "ai":
output = genome.activate(board.field)
field_to_go = output.index(max(output))
elif player.being == "human":
for event in pygame.event.get():
if event.type == pygame.QUIT:
pygame.quit()
break
elif event.type == pygame.MOUSEBUTTONUP:
field_to_go = board.pos_to_field(pygame.mouse.get_pos())
if field_to_go > -1:
if board.field_is_free(field_to_go):
board.action(field_to_go,player.id)
else:
board.action(board.first_free(),player.id)
if player == players[0]:
player = players[1]
else:
player = players[0]
placed += 1
if shown:
pygame.event.get()
board.display()
time.sleep(0.75)
if board.check_winner() or placed == 9:
if board.winner is None:
result[1] += 1
elif players[board.winner-1].being == "ai":
result[2] += 1
else:
result[0] += 1
if shown:
if board.winner is None:
print("None")
else:
print(players[board.winner-1].being + " " + str(player.id))
board.reset()
break
print(result)
def run(config_path):
global shown
global GEN
global screen
config = neat.config.Config(neat.DefaultGenome,neat.DefaultReproduction,neat.DefaultSpeciesSet,neat.DefaultStagnation,config_path)
nc_name = "population"
testing = False
shown = False
new = True
if testing:
if shown:
screen = pygame.display.set_mode((300,300))
pygame.display.set_caption("Tic Tac Toe")
test(neat.nn.FeedForwardNetwork.create(cloud.restore_checkpoint("population").population.best_genome,config))
quit()
if new:
p = neat.Population(config)
p.add_reporter(neat.StdOutReporter(True))
p.add_reporter(neat.StatisticsReporter())
else:
p = cloud.restore_checkpoint(nc_name).population
graph.load()
GEN = p.generation
p.run(train,250)
print(time.strftime("%H:%M:%s",time.localtime()))
cloud.save_checkpoint(config,p,p.species,GEN,nc_name)
graph.save()
graph.display()
sound()
if __name__ == "__main__":
local_dir = os.path.dirname(__file__)
config_path = os.path.join(local_dir,"config_Feedforward")
run(config_path)
经过 100 场比赛后，AI 的得分相对较快，达到 175。但从那时起它并没有提高。并且仍然会输掉比赛，而当您发挥完美时不会发生这种情况。我究竟做错了什么？谢谢你的帮助:)
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