https://github.com/applenob/rl_learn

我的强化学习笔记和学习材料:book: still updating ... ...
https://github.com/applenob/rl_learn

learning-by-doing reinforcement-learning

Last synced: about 1 year ago
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我的强化学习笔记和学习材料:book: still updating ... ...

• Host: GitHub
• URL: https://github.com/applenob/rl_learn
• Owner: applenob
• Created: 2017-09-22T02:38:40.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2019-06-09T14:29:30.000Z (about 7 years ago)
• Last Synced: 2025-03-30T10:06:44.572Z (about 1 year ago)
• Topics: learning-by-doing, reinforcement-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 74.6 MB
• Stars: 343
• Watchers: 12
• Forks: 117
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# [WIP]强化学习的学习仓库

这是我个人学习**强化学习**的时候收集的比较经典的学习资料、笔记和代码，分享给所有人。

为了直接在GitHub上用markdown文件看公式，推荐安装chrome插件：[MathJax Plugin for Github](https://chrome.google.com/webstore/detail/mathjax-plugin-for-github/ioemnmodlmafdkllaclgeombjnmnbima)

## 入门指南

- [入门指南](learning_route.md)

## 课程笔记

- [David Silver 的 Reinforcement Learning 课程学习笔记。](class_note.ipynb)

- [课程对应的所有PPT](slides)

- Sutton 的 Reinforcement Learning: An Introduction书本学习笔记

  - [1. Introduction](notes/intro_note_01.md)

  - [2. Multi-armed Bandits](notes/intro_note_02.md)

  - [3. Finite Markov DecisionProcesses](notes/intro_note_03.md)

  - [4. Dynamic Programming](notes/intro_note_04.md)

  - [5. Monte Carlo Methods](notes/intro_note_05.md)

  - [6. Temporal-Difference Learning](notes/intro_note_06.md)

  - [7. n-step Bootstrapping](notes/intro_note_07.md)

  - [8. Planning and Learning with Tabular Methods](notes/intro_note_08.md)

  - [9. On-policy Prediction with Approximation](notes/intro_note_09.md)

  - [10. On-policy Control with Approximation](notes/intro_note_10.md)

  - [11. Off-policy Methods with Approximation](notes/intro_note_11.md)

  - [12. Eligibility Traces](notes/intro_note_12.md)

  - [13. Policy Gradient Methods](notes/intro_note_13.md)

  - [14. Psychology](notes/intro_note_14.md)

  - [15. Neuroscience](notes/intro_note_15.md)

  - [16. Applications and Case Studies](notes/intro_note_16.md)

  - [17. Frontiers](notes/intro_note_17.md)

- [书本的各版本pdf](book)

  - [2017-6 draft](book/bookdraft2017june19.pdf)

  - [2018 second edition](book/bookdraft2018.pdf)

## 实验目录

所有的实验源代码都在`lib`目录下，来自[dennybritz](https://github.com/dennybritz/reinforcement-learning)。在原先代码的基础上，增加了对实验背景的具体介绍、代码和公式的对照。

- [Gridworld](exp/1_gridworld.ipynb)：对应**MDP**的**Dynamic Programming**

- [Blackjack](exp/2_blackjack.ipynb)：对应**Model Free**的**Monte Carlo**的Planning和Controlling

- [Windy Gridworld](exp/3_windy_gridworld.ipynb)：对应**Model Free**的**Temporal Difference**的**On-Policy Controlling**：**SARSA**。

- [Cliff Walking](exp/4_cliff_walking.ipynb)：对应**Model Free**的**Temporal Difference**的**Off-Policy Controlling**：**Q-learning**。

- [Mountain Car](exp/5_mountain_car.ipynb)：对应Q表格很大无法处理（state空间连续）的**Q-Learning with Linear Function Approximation**。

- [Atari](exp/6_atari.ipynb)：对应**Deep-Q Learning**。

## 其他重要学习资料：

- [WildML的博客](http://www.wildml.com/2016/10/learning-reinforcement-learning/)

- [David Silver’s Reinforcement Learning Course](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)

- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html)

- [书本的python代码实现](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)