强化学习相关资料(书籍，课程，网址，笔记等)

强化学习相关资料(书籍，课程，网址，笔记等)

作者：凯鲁嘎吉 - 博客园 http://www.cnblogs.com/kailugaji/

更多请看：Reinforcement Learning - 随笔分类 - 凯鲁嘎吉 - 博客园 https://www.cnblogs.com/kailugaji/category/2038931.html

1. Sutton, R. S. and Barto, A. G. Reinforcement learning: An introduction. MIT press, 2018. http://incompleteideas.net/book/the-book.html (经典必读，最全面)，中文翻译：https://rl.qiwihui.com/zh_CN/latest/

2. Hao Dong, Zihan Ding, Shanghang Zhang, et al., Deep Reinforcement Learning: Fundamentals, Research, and Applications, Springer Nature, http://www.deepreinforcementlearningbook.org, 2021. https://link.springer.com/content/pdf/10.1007%2F978-981-15-4095-0.pdf (汇总性强，但图少，更像是期末总结小笔记)，中文版：深度强化学习：基础、研究与应用 (博文视点出品) https://deepreinforcementlearningbook.org/assets/pdfs/%E6%B7%B1%E5%BA%A6%E5%BC%BA%E5%8C%96%E5%AD%A6%E4%B9%A0(%E4%B8%AD%E6%96%87%E7%89%88-%E5%BD%A9%E8%89%B2%E5%8E%8B%E7%BC%A9).pdf

3. MYKEL J. KOCHENDERFER, TIM A. WHEELER, AND KYLE H. WRAY, Algorithms for Decision Making, MIT PRESS, 2022. https://algorithmsbook.com/ or https://mykel.kochenderfer.com/textbooks/

4. Qi Wang, Yiyuan Yang, Ji Jiang, Easy RL 强化学习中文教程, 2021. https://github.com/datawhalechina/easy-rl/releases (相当于李宏毅课程《强化学习》笔记，大白话，通俗易懂，部分内容有待商榷与完善)

5. 王树森, 黎彧君, 张志华, 深度强化学习，https://github.com/wangshusen/DRL/blob/master/Notes_CN/DRL.pdf, 2021. (深度强化学习打基础必看，深入浅出，推荐阅读)

6. 邱锡鹏，神经网络与深度学习，机械工业出版社，https://nndl.github.io/, 2020. (强化学习打基础必看，深度的涉及的少，推荐阅读)

7. 王东，机器学习导论，清华大学出版社，http://166.111.134.19:7777/mlbook/release/21-01-02/book.pdf, 2021.

8. Alekh Agarwal, Nan Jiang, Sham M. Kakade, Wen Sun. Reinforcement Learning: Theory and Algorithms, https://rltheorybook.github.io/rltheorybook_AJKS.pdf, 2021. (含offline RL)
9. Aske Plaat, Deep Reinforcement Learning, a textbook, https://arxiv.org/abs/2201.02135, 2022. (2022新出的关于深度强化学习的书，含meta learning)

10. CS 885 Fall 2021 - Reinforcement Learning https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-fall21/schedule.html

11. CS330 Fall 2021 Deep Multi-Task and Meta Learning https://cs330.stanford.edu/

12. CS 234: Reinforcement Learning Winter 2021 https://web.stanford.edu/class/cs234/index.html

13. CS 285 Deep Reinforcement Learning https://rail.eecs.berkeley.edu/deeprlcourse/

14. UCL Course on RL 2015 Teaching - David Silver https://www.davidsilver.uk/teaching/

15. 10703 (Spring 2018): Deep RL and Control http://www.cs.cmu.edu/~rsalakhu/10703/lectures.html

16. Nan Jiang, CS 498 Reinforcement Learning (S21), CS 542 Statistical Reinforcement Learning (F21), https://nanjiang.cs.illinois.edu/

17. 李宏毅, 强化学习课程, https://www.bilibili.com/video/BV1UE411G78S?spm_id_from=333.999.0.0, 2020.

18. 腾讯周沫凡(莫烦Python)强化学习、教程、代码 https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/

19. Notes on Reinforcement Learning http://paulorauber.com/notes/reinforcement_learning.pdf (强化学习打基础看)

20. OpenAI Spinning Up在线学习平台，包括原理、算法、论文、代码, 英文版https://spinningup.openai.com/en/latest/, 中文版https://spinningup.readthedocs.io/zh_CN/latest/index.html, Table of environments · openai/gym Wiki · GitHub https://github.com/openai/gym/wiki/Table-of-environments

21. OpenAI Gym环境介绍，包括状态动作维度：https://gymnasium.farama.org/

22. 强化学习路线图 - 深度强化学习实验室 http://deeprl.neurondance.com/d/107 or https://github.com/NeuronDance/DeepRL/tree/master/A-Guide-Resource-For-DeepRL

23. 深度强化学习实验室 - 一个开源开放、共享共进的强化学习学术组织、线上创新实验室http://deeprl.neurondance.com/

24. RLChina 强化学习社区：http://rlchina.org/
25. 深度强化学习 - 极术社区  https://aijishu.com/blog/deeprl
26. 智源社区：https://hub.baai.ac.cn/
27. 伯克利人工智能研究 (BAIR) 实验室：https://bair.berkeley.edu/blog/

28. CampusAI https://campusai.github.io/theory/

29. 强化学习论文：https://github.com/hanjuku-kaso/awesome-offline-rl
30. 强化学习前沿 - 知乎专栏：https://www.zhihu.com/column/reinforcementlearning
31. TorchRL：PyTorch强化学习库 https://github.com/facebookresearch/rl
32. 动手强化学习：https://hrl.boyuai.com/