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datawhalechina / Easy Rl

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强化学习中文教程,在线阅读地址:https://datawhalechina.github.io/easy-rl/

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EasyRL

李宏毅老师的《深度强化学习》是强化学习领域经典的中文视频之一。李老师幽默风趣的上课风格让晦涩难懂的强化学习理论变得轻松易懂,他会通过很多有趣的例子来讲解强化学习理论。比如老师经常会用玩 Atari 游戏的例子来讲解强化学习算法。此外,为了教程的完整性,我们整理了周博磊老师的《强化学习纲要》、李科浇老师的《百度强化学习》以及多个强化学习的经典资料作为补充。对于想入门强化学习又想看中文讲解的人来说绝对是非常推荐的。

使用说明

在线阅读(内容实时更新)

地址:https://datawhalechina.github.io/easy-rl/

最新版PDF下载

地址:https://github.com/datawhalechina/easy-rl/releases

国内地址(推荐国内读者使用):https://pan.baidu.com/s/1t2jw_vLwHBb15Ah5vcFRzw 提取码: vi2s

压缩版(推荐网速较差的读者使用,文件小,图片分辨率较低): https://pan.baidu.com/s/1_sbSSt0WjV2YTxepYcAe8g 提取码: c1rx

内容导航

章节 习题 相关项目
第一章 强化学习概述 第一章 习题
第二章 马尔可夫决策过程 (MDP) 第二章 习题
第三章 表格型方法 第三章 习题 Q-learning算法实战
第四章 策略梯度 第四章 习题
第五章 近端策略优化 (PPO) 算法 第五章 习题
第六章 DQN (基本概念) 第六章 习题
第七章 DQN (进阶技巧) 第七章 习题 DQN算法实战
第八章 DQN (连续动作) 第八章 习题
第九章 演员-评论家算法 第九章 习题
第十章 稀疏奖励 第十章 习题
第十一章 模仿学习 第十一章 习题
第十二章 深度确定性策略梯度 (DDPG) 算法 第十二章 习题 DDPG算法实战
第十三章 AlphaStar 论文解读

算法实战

点击或者跳转codes文件夹下进入算法实战

贡献者

pic
Qi Wang

教程设计(第1~12章)
中国科学院大学

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Yiyuan Yang

习题设计&第13章
清华大学

pic
John Jim

算法实战
北京大学

致谢

特别感谢 @Sm1les@LSGOMYP 对本项目的帮助与支持。

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