Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/datascienceid/reinforcement-learning-resources
A curated list of awesome reinforcement courses, video lectures, books, library and many more.
https://github.com/datascienceid/reinforcement-learning-resources
List: reinforcement-learning-resources
awesome awesome-list awesome-lists data-science indonesia machine machine-intelligence machine-learning reinforcement-learning reinforcement-learning-algorithms
Last synced: about 1 month ago
JSON representation
A curated list of awesome reinforcement courses, video lectures, books, library and many more.
- Host: GitHub
- URL: https://github.com/datascienceid/reinforcement-learning-resources
- Owner: datascienceid
- Created: 2018-04-14T03:25:00.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2022-11-02T05:10:11.000Z (almost 2 years ago)
- Last Synced: 2024-05-23T06:40:11.937Z (4 months ago)
- Topics: awesome, awesome-list, awesome-lists, data-science, indonesia, machine, machine-intelligence, machine-learning, reinforcement-learning, reinforcement-learning-algorithms
- Homepage:
- Size: 3.91 KB
- Stars: 54
- Watchers: 8
- Forks: 26
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - reinforcement-learning-resources - A curated list of awesome reinforcement courses, video lectures, books, library and many more. . (Other Lists / PowerShell Lists)
README
# Reinforcement Learning Resources
A curated list of awesome reinforcement courses, video lectures, books, library and many more.
## Table of Contents
* **[Free Books](#free-books)**
* **[Courses](#courses)**
* **[Videos and Lectures](#videos-and-lectures)**
* **[Papers](#papers)**
* **[Tutorials](#tutorials)**
* **[Sample Code](#sample-code)**
* **[Libraries](#libraries)**
### Free Books
1. [Reinforcement Learning: An Introduction 1st Ed by Richard Sutton and Andrew Barto](http://incompleteideas.net/book/ebook/the-book.html)
2. [Reinforcement Learning: An Introduction 2nd Edition, in progress by Richard Sutton and Andrew Barto](http://incompleteideas.net/book/bookdraft2018mar11.pdf)
3. [Algorithms for Reinforcement Learning by Csaba Szepesvari](http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf)
4. [Artificial Intelligence: Foundations of Computational Agents by David Poole and Alan Mackworth](http://artint.info/html/ArtInt_262.html)
### Courses
1. [10703: Deep Reinforcement Learning and Control, Spring 2017](https://katefvision.github.io/)
2. [Reinforcement Learning](https://classroom.udacity.com/courses/ud600)
3. [Practical Reinforcement Learning](https://www.coursera.org/learn/practical-rl)
4. [Reinforcement Learning Explained](https://www.edx.org/course/reinforcement-learning-explained)
5. [Practical Reinforcement Learning](https://github.com/yandexdataschool/Practical_RL)
### Videos and Lectures
1. [COMPM050/COMPGI13 Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)
2. [CS294 Deep Reinforcement Learning](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3)
3. [CS229 Machine Learning - Lecture 16: Reinforcement Learning](https://www.youtube.com/watch?v=RtxI449ZjSc&feature=relmfu)
4. [Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)
5. [Lecture 2: Deep Reinforcement Learning for Motion Planning](https://www.youtube.com/watch?v=QDzM8r3WgBw&list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf)
6. [Lecture 8: Markov Decision Processes 1](https://www.youtube.com/watch?v=i0o-ui1N35U)
7. [Lecture 9: Markov Decision Processes 2](https://www.youtube.com/watch?v=Csiiv6WGzKM)
8. [Lecture 10: Reinforcement Learning 1](https://www.youtube.com/watch?v=ifma8G7LegE)
9. [Lecture 11: Reinforcement Learning 2](https://www.youtube.com/watch?v=Si1_YTw960c)
10. [MIT 6.S191: Reinforcement Learning](https://www.youtube.com/watch?v=93M1l_nrhpQ)
### Papers
1. [Generalization in Reinforcement Learning: Successful examples using sparse coding, Richard S. Sutton](http://webdocs.cs.ualberta.ca/~sutton/papers/sutton-96.pdf)
2. [Learning from Delayed Rewards, Christopher J. C. H. Watkins](https://www.cs.rhul.ac.uk/home/chrisw/new_thesis.pdf)
3. [Learning to predict by the methods of temporal differences, Richard S. Sutton](http://webdocs.cs.ualberta.ca/~sutton/papers/sutton-88-with-erratum.pdf)
4. [Learning from Delayed Rewards, Cambridge, Chris Watkins](http://www.cs.rhul.ac.uk/home/chrisw/thesis.html)
5. [Monte Carlo Inversion and Reinforcement Learning, Andrew Barto, Michael Duff](http://papers.nips.cc/paper/865-monte-carlo-matrix-inversion-and-reinforcement-learning.pdf)
6. [Reinforcement Learning with Replacing Eligibility Traces, Machine Learning, Satinder P. Singh, Richard S. Sutton](http://www-all.cs.umass.edu/pubs/1995_96/singh_s_ML96.pdf)
### Tutorials
1. [Reinforcement Learning](http://www.cse.unsw.edu.au/~cs9417ml/RL1/)
2. [Reinforcement Learning Tutorial](http://wiki.ros.org/reinforcement_learning/Tutorials/Reinforcement%20Learning%20Tutorial)
3. [Let’s make a DQN: Implementation](https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/)
4. [Simple Reinforcement Learning with Tensorflow Part 4: Deep Q-Networks and Beyond](https://medium.com/@awjuliani/simple-reinforcement-learning-with-tensorflow-part-4-deep-q-networks-and-beyond-8438a3e2b8df)
### Sample Code
1. [Reinforcement Learning: An Introduction (2nd Edition)](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)
### Libraries
1. [OpenAI gym](https://gym.openai.com/)
2. [OpenAI Retro](https://github.com/openai/retro)
3. [Deep Mind Lab](https://github.com/deepmind/lab)
4. [RL-Library](http://library.rl-community.org/wiki/Main_Page)
5. [RL Lab](https://github.com/rll/rllab)
## Contributing
Jika anda ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia.