基于蒙特卡洛树搜索和策略价值网络(强化学习)的AI五子棋算法

上传者: 39589455 | 上传时间: 2022-10-24 13:10:00 | 文件大小: 1.64MB | 文件类型: ZIP
python编写,即跑即用,no bugs,有训练好的model。 使用蒙特卡洛树搜索与深度神经网络来设计一种基于强化学习的AI五子棋算法,实现了从零开始学习五子棋博弈的人工智能算法。

文件下载

资源详情

[{"title":"( 24 个子文件 1.64MB ) 基于蒙特卡洛树搜索和策略价值网络(强化学习)的AI五子棋算法","children":[{"title":"reGomoku","children":[{"title":"game.py <span style='color:#111;'> 7.94KB </span>","children":null,"spread":false},{"title":"train.py <span style='color:#111;'> 8.58KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"mcts_alphaZero.cpython-37.pyc <span style='color:#111;'> 7.35KB </span>","children":null,"spread":false},{"title":"game.cpython-37.pyc <span style='color:#111;'> 6.47KB </span>","children":null,"spread":false},{"title":"mcts_pure.cpython-37.pyc <span style='color:#111;'> 7.55KB </span>","children":null,"spread":false},{"title":"policy_value_net_numpy.cpython-37.pyc <span style='color:#111;'> 3.63KB </span>","children":null,"spread":false}],"spread":true},{"title":"mcts_pure.py <span style='color:#111;'> 7.01KB </span>","children":null,"spread":false},{"title":"best_policy_8_8_5.model <span style='color:#111;'> 465.79KB </span>","children":null,"spread":false},{"title":"policy_value_net_numpy.py <span style='color:#111;'> 3.93KB </span>","children":null,"spread":false},{"title":".idea","children":[{"title":"AlphaZero_Gomoku-master.iml <span style='color:#111;'> 467B </span>","children":null,"spread":false},{"title":"misc.xml <span style='color:#111;'> 301B </span>","children":null,"spread":false},{"title":"modules.xml <span style='color:#111;'> 298B </span>","children":null,"spread":false},{"title":"workspace.xml <span style='color:#111;'> 14.79KB </span>","children":null,"spread":false},{"title":"inspectionProfiles","children":[{"title":"Project_Default.xml <span style='color:#111;'> 562B </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"policy_value_net.py <span style='color:#111;'> 5.00KB </span>","children":null,"spread":false},{"title":"best_policy_8_8_5.model2 <span style='color:#111;'> 465.79KB </span>","children":null,"spread":false},{"title":"human_play.py <span style='color:#111;'> 2.79KB </span>","children":null,"spread":false},{"title":"policy_value_net_tensorflow.py <span style='color:#111;'> 6.52KB </span>","children":null,"spread":false},{"title":"best_policy_6_6_4.model2 <span style='color:#111;'> 407.93KB </span>","children":null,"spread":false},{"title":"policy_value_net_keras.py <span style='color:#111;'> 4.77KB </span>","children":null,"spread":false},{"title":"best_policy_6_6_4.model <span style='color:#111;'> 407.93KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 2.50KB </span>","children":null,"spread":false},{"title":"mcts_alphaZero.py <span style='color:#111;'> 7.65KB </span>","children":null,"spread":false},{"title":"policy_value_net_pytorch.py <span style='color:#111;'> 6.13KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]

评论信息

免责申明

【只为小站】的资源来自网友分享,仅供学习研究,请务必在下载后24小时内给予删除,不得用于其他任何用途,否则后果自负。基于互联网的特殊性,【只为小站】 无法对用户传输的作品、信息、内容的权属或合法性、合规性、真实性、科学性、完整权、有效性等进行实质审查;无论 【只为小站】 经营者是否已进行审查,用户均应自行承担因其传输的作品、信息、内容而可能或已经产生的侵权或权属纠纷等法律责任。
本站所有资源不代表本站的观点或立场,基于网友分享,根据中国法律《信息网络传播权保护条例》第二十二条之规定,若资源存在侵权或相关问题请联系本站客服人员,zhiweidada#qq.com,请把#换成@,本站将给予最大的支持与配合,做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明