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中文整理的强化学习资料（Reinforcement Learning）
https://github.com/wwxFromTju/awesome-reinforcement-learning-zh
List: awesome-reinforcement-learning-zh
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中文整理的强化学习资料（Reinforcement Learning）
Host: GitHub
URL: https://github.com/wwxFromTju/awesome-reinforcement-learning-zh
Owner: wwxFromTju
Created: 2018-03-25T05:17:56.000Z (over 6 years ago)
Default Branch: master
Last Pushed: 2020-04-30T09:09:39.000Z (over 4 years ago)
Last Synced: 2024-05-21T15:04:55.415Z (6 months ago)
Size: 25.1 MB
Stars: 1,879
Watchers: 79
Forks: 353
Open Issues: 3
Metadata Files:
- Readme: readMe.md
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README

        # 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.stanford.edu/class/cs234/slides/cs234_2018_l3.pdf)

 

 

 Model-free learning to make good decisions. Q-learning. SARSA. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l4.pdf)

 

 Scaling up: value function approximation. Deep Q Learning. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l5.pdf)

 

 Deep reinforcement learning continued. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l6.pdf)

 

 Imitation Learning. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l7_annotated.pdf)

 

 Policy search. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l8.pdf)

 

 Policy search. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l9_updated.pdf)

 

 Midterm review. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_midterm_review.pdf)

 

 Fast reinforcement learning (Exploration/Exploitation) Part I. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l11.pdf)

 

 Fast reinforcement learning (Exploration/Exploitation) Part II. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l12.pdf)

 

 Batch Reinforcement Learning. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l13.pdf)

 

 Monte Carlo Tree Search. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l14.pdf)

 

 Human in the loop RL with a focus on transfer learing. [link](http://web.stanford.edu/class/cs234/slides/cs234_2018_l15.pdf)

 

### Multi-Agent Reinforcement Learning Tutorial

注：因为在阿里广告这边实习，有幸和汪老师还有张老师做了篇论文。在过程中体会到汪老师的思维真的很活跃，很强。另外，张老师感觉是国内cs冉冉升起的新星，值得follow和关注！

 课程主页 [link](http://wnzhang.net/tutorials/marl2018/index.html)

 

 Fundamentals of Reinforcement Learning  [link](http://wnzhang.net/tutorials/marl2018/docs/lecture-1-rl.pdf)

 Fundamentals of Game Theory [link](http://wnzhang.net/tutorials/marl2018/docs/lecture-2a-game-theory.pdf)

 Learning in Repeated Games [link](http://wnzhang.net/tutorials/marl2018/docs/lecture-2b-repeated-games.pdf)

 Multi-Agent Reinforcement Learning [link](http://wnzhang.net/tutorials/marl2018/docs/lecture-3a-marl-1.pdf) [link](http://wnzhang.net/tutorials/marl2018/docs/lecture-3b-marl-2.pdf)

 

## 深度DRL课程

### 台湾大学 李宏毅 （深度）强化学习

课程主页 [link](http://speech. ee.ntu.edu.tw/~tlkagk/courses/)

视频可以在B站上看到：[link](https://www.bilibili.com/video/av24724071?from=search&seid=14814651069494196110)

### UCB 深度强化学习课程

课程主页： [link](http://rll.berkeley.edu/deeprlcourse/)

update：2018 fall（2018年秋季）

对应slide（课件）：

Lecture Slides

See Syllabus for more information.

Introduction and Course Overview [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-1.pdf)

Supervised Learning and Imitation [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-2.pdf)

TensorFlow and Neural Nets Review Session (notebook) [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-3.pdf)

Reinforcement Learning Introduction [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-4.pdf)

Policy Gradients Introduction [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-5.pdf)

Actor-Critic Introduction [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-6.pdf)

Value Functions and Q-Learning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-7.pdf)

Advanced Q-Learning Algorithms [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-8.pdf)

Advanced Policy Gradients [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-9.pdf)

Optimal Control and Planning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-10.pdf)

Model-Based Reinforcement Learning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-11.pdf)

Advanced Model Learning and Images [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-12.pdf)

Learning Policies by Imitating Other Policies [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-13.pdf)

Probability and Variational Inference Primer [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-14.pdf)

Connection between Inference and Control [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-15.pdf)

Inverse Reinforcement Learning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-16.pdf)

Exploration[link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-17.pdf)，[link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-18.pdf)

Transfer Learning and Multi-Task Learning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-19.pdf)

Meta-Learning [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-20.pdf)

Parallelism and RL System Design [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-21.pdf)

Advanced Imitation Learning and Open Problems [link](http://rail.eecs.berkeley.edu/deeprlcourse/static/slides/lec-22.pdf)

### CMU 深度强化学习课程

update fall 2018

2018 fall 的课程主页 [link](http://www.andrew.cmu.edu/course/10-703/)

2017的课程主页： [link](https://katefvision.github.io/)

对应slide（课件）：

Introduction [link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/lecture1_intro.pdf)

Markov decision processes (MDPs), POMDPs [link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/lecture2_mdps.pdf)

Solving known MDPs: Dynamic Programming [link](http://www.andrew.cmu.edu/course/10-703/slides/lecture3_exactmethods-9-5-2018.pdf)

Policy iteration, Value iteration, Asynchronous DP [link](http://www.andrew.cmu.edu/course/10-703/slides/lecture4_valuePolicyDP-9-10-2018.pdf)

Monte Carlo Learning, Temporal difference learning, Q learning [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture5_MC_9-12-2018.pdf)

Temporal difference learning (Tom), Planning and learning: Dyna, Monte carlo tree search [link](http://www.andrew.cmu.edu/course/10-703/slides/TDshort-9-17-2018.pdf)

Deep NN Architectures for RL [link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/lecture_NNarchitecturesforRL_katef.pdf)

Recitation on Monte Carlo Tree Search [link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/MCTS_katef.pdf)

VF approximation, MC, TD with VF approximation, Control with VF approximation[link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/lecture_FAkatef.pdf)

Deep Q Learning : Double Q learning, replay memory[link](https://www.cs.cmu.edu/~katef/DeepRLFall2018/lecture_DQL_katef2018.pdf)	

Policy Gradients [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_PG-10-1-2018.pdf) [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_PG-10-3-2018.pdf)

Advanced Policy Gradients [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_PG-NatGrad-10-8-2018.pdf)

Evolution Methods, Natural Gradients [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_async_evolution.pdf)

Natural Policy Gradients, TRPO, PPO, ACKTR [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_NaturalPolicyGradientsTRPOPPO.pdf)

Pathwise Derivatives, DDPG, multigoal RL, HER [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_DDPGMultigoalRL.pdf)

Exploration vs. Exploitation [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_Exploration-10-22-2018.pdf) [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_exploration.pdf)

Exploration and RL in Animals [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_exploration.pdf) [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_Brains_RL.pdf)

Model-based Reinforcement Learning [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_modelbasedRL.pdf) 

Imitation Learning [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_Imitation_supervised-Nov-5-2018.pdf)

Maximum Entropy Inverse RL, Adversarial imitation learning [link](http://www.andrew.cmu.edu/course/10-703/slides/Lecture_IRL_GAIL.pdf)

Recitation: Trajectory optimization - iterative LQR [link](https://katefvision.github.io/katefSlides/RECITATIONtrajectoryoptimization_katef.pdf)

Learning to learn, one shot learning[link](Learning to learn, one shot learning)