Tic-Tac-Toe：井字游戏（井字棋）

本文介绍了井字游戏变种方案，可通过设置xsize、ysize指定棋盘大小，winnum指定连珠数。用两个深度学习模型分别扮演玩家和电脑自动对弈，借QLearning记录每步，依胜负判定方案好坏。代码展示了模型训练等过程，包括迭代、下棋、胜负判定及模型更新等。

Tic-Tac-Toe：井字游戏（井字棋）

是一种在3x3格子上进行的连珠游戏，和五子棋比较类似，由于棋盘一般不画边框，格线排成井字故得名。游戏需要的工具仅为纸和笔，然后由分别代表O和X的两个游戏者轮流在格子里留下标记（一般来说先手者为X）。由最先在任意一条直线上成功连接三个标记的一方获胜。

方案介绍

该方案为井字游戏的变种，可以通过设置xsize、ysize来指定棋盘大小，通过设置winnum来指定连珠数，每局结束的判定在VictoryRule.py文件中写明，QLearning.py文件是Q表格，用于记录电脑和玩家的每一步。

方案设置了两个深度学习模型扮演玩家和电脑，双方自动下棋，根据最后获胜方来判别方案的好坏

代码实现

import numpy as npimport paddlefrom Model import Modelfrom VictoryRule import Rulefrom QLearning import QLearningfrom visualdl import LogWriterlog_writer = LogWriter(logdir="./log")Max_Epoch = 200           #最大迭代次数xsize = 3                 #多少行ysize = 3                 #多少列winnum = 3                #连珠数，多少个连珠则获胜learning_rate = 1e-3      #学习率decay_rate = 0.6          #每步衰减率player=1                  #玩家是数字，非0，非负computer=2                #电脑的数字，非0，非负remain = []               #地图中剩余可下棋子位置rule = Rule(xsize,ysize,winnum) #规则Qchart = QLearning(xsize * ysize,decay_rate)#Q表格player_model = Model(xsize * ysize,xsize * ysize)player_model.train()computer_model = Model(xsize * ysize,xsize * ysize)computer_model.train()player_optimizer = paddle.optimizer.SGD(parameters=player_model.parameters(),                                  learning_rate=learning_rate)computer_optimizer = paddle.optimizer.SGD(parameters=computer_model.parameters(),                                  learning_rate=learning_rate)def restart():    "重启环境"    Qchart.clear()    remain.clear()    rule.map = np.zeros(xsize * ysize,dtype=int)    for i in range(xsize * ysize):        remain.append(i)def modelupdate(player_loss,computer_loss):    "模型更新"    log_writer.add_scalar(tag="player/loss", step=epoch, value=player_loss.numpy())    log_writer.add_scalar(tag="computer/loss", step=epoch, value=computer_loss.numpy())    # 梯度更新    player_loss.backward()    computer_loss.backward()    player_optimizer.step()    player_optimizer.clear_grad()    computer_optimizer.step()    computer_optimizer.clear_grad()    paddle.save(player_model.state_dict(),'player_model')    paddle.save(computer_model.state_dict(),'computer_model')    for i in range(xsize * ysize):    remain.append(i)for epoch in range(Max_Epoch):    while True:        player_predict = player_model(paddle.to_tensor(rule.map, dtype='float32',stop_gradient=False))#玩家方预测        for pred in np.argsort(-player_predict.numpy()):            if pred in remain:                remain.remove(pred)                break        rule.map[pred] = player        Qchart.update(pred,'player')        print('player down at {}'.format(pred))        overcode=rule.checkover(pred,player)        if overcode == player:            "获胜方为玩家"            player_loss = paddle.nn.functional.mse_loss(player_predict, paddle.to_tensor(Qchart.playerstep, dtype='float32', stop_gradient=False))            computer_loss = paddle.nn.functional.mse_loss(computer_predict, paddle.to_tensor(-1 * Qchart.computerstep, dtype='float32', stop_gradient=False))#损失计算中，失败方的label为每步的负数            print("Player Victory!")            print(rule.map.reshape(xsize,ysize))            #print("epoch:{}\tplayer loss:{}\tcomputer loss:{}".format(epoch,player_loss.numpy()[0],computer_loss.numpy()[0]))            modelupdate(player_loss,computer_loss)            restart()            break        elif overcode == 0:            player_loss = paddle.nn.functional.mse_loss(player_predict, paddle.to_tensor(Qchart.playerstep, dtype='float32', stop_gradient=False))            computer_loss = paddle.nn.functional.mse_loss(computer_predict, paddle.to_tensor(Qchart.computerstep, dtype='float32', stop_gradient=False))            print("Draw!")            print(rule.map.reshape(xsize,ysize))            #print("epoch:{}\tplayer loss:{}\tcomputer loss:{}".format(epoch,player_loss.numpy()[0],computer_loss.numpy()[0]))            modelupdate(player_loss,computer_loss)            restart()            break        computer_predict = computer_model(paddle.to_tensor(rule.map, dtype='float32',stop_gradient=False))#电脑方预测        for pred in np.argsort(-computer_predict.numpy()):            if pred in remain:                remain.remove(pred)                break        rule.map[pred] = computer        Qchart.update(pred,'computer')        print('computer down at {}'.format(pred))        overcode=rule.checkover(pred, computer)        if overcode == computer:            player_loss = paddle.nn.functional.mse_loss(player_predict, paddle.to_tensor(-1 * Qchart.playerstep, dtype='float32', stop_gradient=False))            computer_loss = paddle.nn.functional.mse_loss(computer_predict, paddle.to_tensor(Qchart.computerstep, dtype='float32', stop_gradient=False))            print("Computer Victory!")            print(rule.map.reshape(xsize,ysize))            #print("epoch:{}\tplayer loss:{}\tcomputer loss:{}".format(epoch,player_loss.numpy()[0],computer_loss.numpy()[0]))            modelupdate(player_loss,computer_loss)            restart()            break        elif overcode == 0:            player_loss = paddle.nn.functional.mse_loss(player_predict, paddle.to_tensor(Qchart.playerstep, dtype='float32', stop_gradient=False))            computer_loss = paddle.nn.functional.mse_loss(computer_predict, paddle.to_tensor(Qchart.computerstep, dtype='float32', stop_gradient=False))            print("Draw!")            print(rule.map.reshape(xsize,ysize))            #print("epoch:{}\tplayer loss:{}\tcomputer loss:{}".format(epoch,player_loss.numpy()[0],computer_loss.numpy()[0]))            modelupdate(player_loss,computer_loss)            restart()            break

输出格式

player down at 7computer down at 3player down at 1computer down at 8player down at 6computer down at 2player down at 0computer down at 5Computer Victory![[1 1 2] [2 0 2] [1 1 2]]

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