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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

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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 (about 2 years ago)
Last Synced: 2024-05-23T06:40:11.937Z (7 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 / Monkey C 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.