https://github.com/huixxi/cs234-reinforcement-learning-winter-2019

My Solutions of Assignments of CS234: Reinforcement Learning Winter 2019
https://github.com/huixxi/cs234-reinforcement-learning-winter-2019

course cs234 cs234-assignments deep-reinforcement-learning openai-gym python3 reinforcement-learning stanford-online

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My Solutions of Assignments of CS234: Reinforcement Learning Winter 2019

• Host: GitHub
• URL: https://github.com/huixxi/cs234-reinforcement-learning-winter-2019
• Owner: Huixxi
• License: mit
• Created: 2019-05-05T10:24:53.000Z (about 6 years ago)
• Default Branch: master
• Last Pushed: 2023-03-24T22:43:19.000Z (about 2 years ago)
• Last Synced: 2024-12-10T08:51:31.390Z (6 months ago)
• Topics: course, cs234, cs234-assignments, deep-reinforcement-learning, openai-gym, python3, reinforcement-learning, stanford-online
• Language: Python
• Homepage:
• Size: 1.46 MB
• Stars: 165
• Watchers: 5
• Forks: 48
• Open Issues: 6
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
![](https://github.com/Huixxi/CS234-Reinforcement-Learning-Winter-2019/blob/master/rl_images/Cover.jpg)

# CS234-Reinforcement-Learning

My Solutions of Assignments of [CS234: Reinforcement Learning Winter 2019](http://web.stanford.edu/class/cs234/index.html)

***

### Lecture Videos

This course contains 15 lecture videos, and you can watch them from youtube and bilibili(vpn free).

* [YouTube StanfordOnline](https://www.youtube.com/playlist?list=PLoROMvodv4rOSOPzutgyCTapiGlY2Nd8u)

* [Bilibili](https://www.bilibili.com/video/av47812079?from=search&seid=3645116309541169863)  

You can download the slides for each lecture from [here](http://web.stanford.edu/class/cs234/schedule.html).

***

### Assignments

* [assignment 1](https://github.com/Huixxi/CS234-Reinforcement-Learning-Winter-2019/tree/master/assignment%201)

* [assignment 2](https://github.com/Huixxi/CS234-Reinforcement-Learning-Winter-2019/tree/master/assignment%202)

* [assignment 3](https://github.com/Huixxi/CS234-Reinforcement-Learning-Winter-2019/tree/master/assignment%203)

***

### Highly Recommended RL Resources

* Resources List

  * [RL Resources](https://docs.google.com/document/d/1frWabYtrRE4_Ak2fhcNtk7U-ujtU5Aq762Nq-Ryi9A8/edit)

* TextBooks

  * [Reinforcement Learning: An Introduction 2nd Edition. (R.Sutton, G.Barto)](http://incompleteideas.net/book/the-book-2nd.html)

  * [Deep Learning. (Yoshua Bengio)](http://www.deeplearningbook.org/)

* RL Courses

  * [OpenAI Spining Up](https://spinningup.openai.com/en/latest/)

  * [RL-DeepMind Tutorials 2018 (Video)](https://www.youtube.com/playlist?list=PLTrPwBmRciYBs4a8qQVuFz3zByUqqoStG)

* Papers:  

  `Deep Q Learning`

  * [DeepMind Nature Paper](https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf)

  * [Playing Atari with Deep Reinforcement Learning](https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf)

  * [Prioritized Experience Replay](https://arxiv.org/pdf/1511.05952.pdf)  

  

  `Imitation Learning`

  * [Generative Adversarial Imitation Learning(GAIL)](https://arxiv.org/pdf/1606.03476.pdf)

  * [Maximum Entropy Inverse Reinforcement Learning](https://www.aaai.org/Papers/AAAI/2008/AAAI08-227.pdf)

  

* Blogs:

  * [Policy Gradient Algorithm](https://lilianweng.github.io/lil-log/2018/04/08/policy-gradient-algorithms.html)

  * [Meta Reinforcement Learning](https://lilianweng.github.io/lil-log/2019/06/23/meta-reinforcement-learning.html)

  * [Rainbow](https://github.com/Huixxi/TensorFlow2.0-for-Deep-Reinforcement-Learning/blob/master/tutorial_blogs/Building_Rainbow_Step_by_Step_with_TensorFlow2.0.md)

* Podcasts:

  * [TalkRL](https://www.talkrl.com/)

  * [DeepMind: The Podcast](https://podcasts.google.com/?feed=aHR0cHM6Ly9mZWVkcy5zaW1wbGVjYXN0LmNvbS9KVDZwYlBrZw%3D%3D)

***

### Author

[@Huxixi](https://github.com/Huixxi)