強化學習相關資料(書籍，課程，網址，筆記等)
作者：凱魯嘎吉 - 博客園 http://www.cnblogs.com/kailugaji/
更多請看：Reinforcement Learning - 隨筆分類 - 凱魯嘎吉 - 博客園 https://www.cnblogs.com/kailugaji/category/2038931.html
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Sutton, R. S. and Barto, A. G. Reinforcement learning: An introduction. MIT press, 2018. http://incompleteideas.net/book/the-book.html (經典必讀，最全面)，中文翻譯：https://rl.qiwihui.com/zh_CN/latest/
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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 (匯總性強，但圖少，更像是期末總結小筆記)，中文版：深度強化學習：基礎、研究與應用 (博文視點出品) 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
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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/
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Qi Wang, Yiyuan Yang, Ji Jiang, Easy RL 強化學習中文教程, 2021. https://github.com/datawhalechina/easy-rl/releases (相當於李宏毅課程《強化學習》筆記，大白話，通俗易懂，部分內容有待商榷與完善)
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王樹森, 黎彧君, 張志華, 深度強化學習，https://github.com/wangshusen/DRL/blob/master/Notes_CN/DRL.pdf, 2021. (深度強化學習打基礎必看，深入淺出，推薦閱讀)
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邱錫鵬，神經網絡與深度學習，機械工業出版社，https://nndl.github.io/, 2020. (強化學習打基礎必看，深度的涉及的少，推薦閱讀)
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王東，機器學習導論，清華大學出版社，http://166.111.134.19:7777/mlbook/release/21-01-02/book.pdf, 2021.
- 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)
- Aske Plaat, Deep Reinforcement Learning, a textbook, https://arxiv.org/abs/2201.02135, 2022. (2022新出的關於深度強化學習的書，含meta learning)
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CS 885 Fall 2021 - Reinforcement Learning https://cs.uwaterloo.ca/~ppoupart/teaching/cs885-fall21/schedule.html
- CS330 Fall 2021 Deep Multi-Task and Meta Learning https://cs330.stanford.edu/
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CS 234: Reinforcement Learning Winter 2021 https://web.stanford.edu/class/cs234/index.html
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CS 285 Deep Reinforcement Learning https://rail.eecs.berkeley.edu/deeprlcourse/
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UCL Course on RL 2015 Teaching - David Silver https://www.davidsilver.uk/teaching/
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10703 (Spring 2018): Deep RL and Control http://www.cs.cmu.edu/~rsalakhu/10703/lectures.html
- Nan Jiang, CS 498 Reinforcement Learning (S21), CS 542 Statistical Reinforcement Learning (F21), https://nanjiang.cs.illinois.edu/
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李宏毅, 強化學習課程, https://www.bilibili.com/video/BV1UE411G78S?spm_id_from=333.999.0.0, 2020.
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騰訊周沫凡(莫煩Python)強化學習、教程、代碼 https://mofanpy.com/tutorials/machine-learning/reinforcement-learning/
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Notes on Reinforcement Learning http://paulorauber.com/notes/reinforcement_learning.pdf (強化學習打基礎看)
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OpenAI Spinning Up在線學習平台，包括原理、算法、論文、代碼, 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
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強化學習路線圖 - 深度強化學習實驗室 http://deeprl.neurondance.com/d/107 or https://github.com/NeuronDance/DeepRL/tree/master/A-Guide-Resource-For-DeepRL
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深度強化學習實驗室 - 一個開源開放、共享共進的強化學習學術組織、線上創新實驗室http://deeprl.neurondance.com/
- RLChina 強化學習社區：http://rlchina.org/
- 深度強化學習 - 極術社區 https://aijishu.com/blog/deeprl
- 智源社區：https://hub.baai.ac.cn/
- 伯克利人工智能研究 (BAIR) 實驗室：https://bair.berkeley.edu/blog/
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CampusAI https://campusai.github.io/theory/
- 強化學習論文：https://github.com/hanjuku-kaso/awesome-offline-rl
- 強化學習前沿 - 知乎專欄：https://www.zhihu.com/column/reinforcementlearning
- TorchRL：PyTorch強化學習庫 https://github.com/facebookresearch/rl
- 動手強化學習：https://hrl.boyuai.com/