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尝试了博弈树Min-Max + alpha-Beta剪枝方法,并找到了更好的适用于五子棋智能的棋局评估模型和选择模型

Python 100.00%

gomoku-ai's Introduction

五子棋游戏

尝试了博弈树Min-Max + alpha-Beta剪枝方法,并找到了更好的适用于五子棋智能的棋局评估模型和选择模型

  • v1.1版本 Level 1 / Level 2 (棋的AI效果还可以), 包含人人对战、人机对战、机器对战结果和机器辅助手段

  • v1.2版本修复了许多关键bug,五子棋基础AI性能提升,并更名为 比你还6的Level (难度大于) 跟我一样6的Level

  • v1.3版本为非强化学习的正式版本 修复了棋局退出异常的bug,已移至master分支。v2.0版本将进行Q-Learning和Sarsa的强化学习构造

  • v1.4版本更新了机器防守模式

运行环境

python 2.7/3.6 (前期在2.7开发,后期强化学习在3.6开发,两个版本都可以运行游戏)

运行方式

把上面的graphics.py复制进你python安装目录下的Lib文件夹

然后直接运行Main.py即可进入游戏

v1.3

机器对战可以看出第一个level博弈性能比较均衡 输给自己写的五子棋

v1.4

我的开发思路(思维导图)

游戏基础AI

gomoku-ai's People

Contributors

kali-hac avatar

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