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中文整理的强化学习资料（Reinforcement Learning）
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# 强化学习从入门到放弃的资料
2018-11-10：
1. 加入OpenAI的spinningup
2. 加入台湾大学李宏毅的课
3. 加入 UCL 汪军老师 与 SJTU 张伟楠 老师 在 SJTU 做的 Multi-Agent Reinforcement Learning Tutorial
4. update UCB 与 CMU的DRL课到2018 fall
5. update Sutton 的书到 final version
- [书](#书)
- [Reinforcement Learning: An Introduction](#Reinforcement Learning: An Introduction )
- [Algorithms for Reinforcement Learning](#Algorithms for Reinforcement Learning)
- [OpenAI-spinningup](#OpenAI-spinningup)
- [课程](#课程)
- [基础课程](#基础课程)
- [Rich Sutton 强化学习课程(Alberta)](#Rich Sutton 强化学习课程（Alberta）)
- [David Silver 强化学习课程（UCL）](#David Silver 强化学习课程（UCL）)
- [Stanford 强化学习课程](#Stanford 强化学习课程)
- [UCL + STJU Multi-Agent Reinforcement Learning Tutorial](#Multi-Agent Reinforcement Learning Tutorial)
- [深度DRL课程](#深度DRL课程)
- [台湾大学 李宏毅 （深度）强化学习](#台湾大学 李宏毅 （深度）强化学习)
- [UCB 深度强化学习课程](#UCB 深度强化学习课程)
- [CMU 深度强化学习课程](#CMU 深度强化学习课程)
## 书
### Reinforcement Learning: An Introduction
Richard Sutton and Andrew Barto, Reinforcement Learning: An Introduction
update 第二版的最终版（点击obline draft）： [link](http://incompleteideas.net/book/the-book-2nd.html)，因为官方的是放在google doc上，所以我就下载了一个放在github上，需要自取 [link](https://github.com/wwxFromTju/awesome-reinforcement-learning-zh)
注：已经可以准备买实体书了，和同学各自海淘了一本，还没有到手 -- 国外亚马逊， 国内的话，可以考虑JD和国内的亚马逊--不过会贵一些
### Algorithms for Reinforcement Learning
Csaba Szepesvari, Algorithms for Reinforcement Learning [link](http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf)
### OpenAI-spinningup
这个算是比较杂的书吧，有在线doc+对应的code+对应的练习（非常建议结合UCL的一起看，我大致过了一遍，蛮不错的。 * _但是没有提到下面的UCL，UCB的课，也没有提到上面sutton的书，结合得看或许会更好_ *
在线的文档 [link](http://spinningup.openai.com/en/latest/)
关于强化学习的基础介绍 [link](http://spinningup.openai.com/en/latest/spinningup/rl_intro.html)
关于深度强化学习的建议 [link](http://spinningup.openai.com/en/latest/spinningup/spinningup.html)
代码部分 [link](https://github.com/openai/spinningup/tree/master/spinup)
## 课程
## 基础课程
### Rich Sutton 强化学习课程（Alberta）
课程主页 [link](http://incompleteideas.net/rlai.cs.ualberta.ca/RLAI/RLAIcourse/RLAIcourse2006.html)
这个比较老了，有一个比较新的在google云盘上，我找个时间整理一下。
### David Silver 强化学习课程（UCL）
注：这是David Silver大神2015在UCL开的课，现在感觉已经在DeepMind走向巅峰了，估计得等他那天想回学校培养学生才可能开出新的课吧。非常推荐入门学习，建立基础的RL概念。
课程主页：[link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
对应slide（课件）：
Lecture 1: Introduction to Reinforcement Learning [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/intro_RL.pdf)
Lecture 2: Markov Decision Processes [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MDP.pdf)
Lecture 3: Planning by Dynamic Programming [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/DP.pdf)
Lecture 4: Model-Free Prediction [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/MC-TD.pdf)
Lecture 5: Model-Free Control [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/control.pdf)
Lecture 6: Value Function Approximation [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/FA.pdf)
Lecture 7: Policy Gradient Methods [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/pg.pdf)
Lecture 8: Integrating Learning and Planning [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/dyna.pdf)
Lecture 9: Exploration and Exploitation [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/XX.pdf)
Lecture 10: Case Study: RL in Classic Games [link](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching_files/games.pdf)
### Stanford 强化学习课程
注：为2018 spring的课
课程主页： [link](http://web.stanford.edu/class/cs234/schedule.html)
对应slide（课件）：
Introduction to Reinforcement Learning [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l1.pdf)
How to act given know how the world works. Tabular setting. Markov processes. Policy search. Policy iteration. Value iteration [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l2.pdf)
Learning to evaluate a policy when don't know how the world works. [link](http://web.stExcerpt of 11,992 characters
Read on GitHub
Weixun Wang · DRL/MAS
8
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:ee5fc9cf3bf41e45, name:reinforcement learning, desc:reinforcement learning