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of Contents"],"sub_categories":["Free Books","Courses","Videos and Lectures","Papers","Tutorials","Libraries","Sample Code"],"readme":"# Reinforcement Learning Resources\nA curated list of awesome reinforcement courses, video lectures, books, library and many more.\n\n## Table of Contents\n* **[Free Books](#free-books)**\n\n* **[Courses](#courses)**\n\n* **[Videos and Lectures](#videos-and-lectures)**\n\n* **[Papers](#papers)**\n\n* **[Tutorials](#tutorials)**\n\n* **[Sample Code](#sample-code)**\n\n* **[Libraries](#libraries)**\n\n### Free Books\n1.\t[Reinforcement Learning: An Introduction 1st Ed by Richard Sutton and Andrew Barto](http://incompleteideas.net/book/ebook/the-book.html)\n2.\t[Reinforcement Learning: An Introduction 2nd Edition, in progress by Richard Sutton and Andrew Barto](http://incompleteideas.net/book/bookdraft2018mar11.pdf)\n3.\t[Algorithms for Reinforcement Learning by Csaba Szepesvari](http://www.ualberta.ca/~szepesva/papers/RLAlgsInMDPs.pdf)\n4.\t[Artificial Intelligence: Foundations of Computational Agents by David Poole and Alan Mackworth](http://artint.info/html/ArtInt_262.html)\n\n### Courses\n1.\t[10703: Deep Reinforcement Learning and Control, Spring 2017](https://katefvision.github.io/)\n2.\t[Reinforcement Learning](https://classroom.udacity.com/courses/ud600)\n3.\t[Practical Reinforcement Learning](https://www.coursera.org/learn/practical-rl)\n4.\t[Reinforcement Learning Explained](https://www.edx.org/course/reinforcement-learning-explained)\n5.\t[Practical Reinforcement Learning](https://github.com/yandexdataschool/Practical_RL)\n\n### Videos and Lectures\n1.\t[COMPM050/COMPGI13 Reinforcement Learning](http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html)\n2.\t[CS294 Deep Reinforcement Learning](https://www.youtube.com/playlist?list=PLkFD6_40KJIznC9CDbVTjAF2oyt8_VAe3)\n3.\t[CS229 Machine Learning - Lecture 16: Reinforcement Learning](https://www.youtube.com/watch?v=RtxI449ZjSc\u0026feature=relmfu)\n4.\t[Deep RL Bootcamp](https://sites.google.com/view/deep-rl-bootcamp/lectures)\n5.\t[Lecture 2: Deep Reinforcement Learning for Motion Planning](https://www.youtube.com/watch?v=QDzM8r3WgBw\u0026list=PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf)\n6.\t[Lecture 8: Markov Decision Processes 1](https://www.youtube.com/watch?v=i0o-ui1N35U)\n7.\t[Lecture 9: Markov Decision Processes 2](https://www.youtube.com/watch?v=Csiiv6WGzKM)\n8.\t[Lecture 10: Reinforcement Learning 1](https://www.youtube.com/watch?v=ifma8G7LegE)\n9.\t[Lecture 11: Reinforcement Learning 2](https://www.youtube.com/watch?v=Si1_YTw960c)\n10.\t[MIT 6.S191: Reinforcement Learning](https://www.youtube.com/watch?v=93M1l_nrhpQ)\n\n### Papers\n1.\t[Generalization in Reinforcement Learning: Successful examples using sparse coding, Richard S. Sutton](http://webdocs.cs.ualberta.ca/~sutton/papers/sutton-96.pdf) \n2.\t[Learning from Delayed Rewards, Christopher J. C. H. Watkins](https://www.cs.rhul.ac.uk/home/chrisw/new_thesis.pdf)\n3.\t[Learning to predict by the methods of temporal differences, Richard S. Sutton](http://webdocs.cs.ualberta.ca/~sutton/papers/sutton-88-with-erratum.pdf)\n4.\t[Learning from Delayed Rewards, Cambridge, Chris Watkins](http://www.cs.rhul.ac.uk/home/chrisw/thesis.html)\n5.\t[Monte Carlo Inversion and Reinforcement Learning, Andrew Barto, Michael Duff](http://papers.nips.cc/paper/865-monte-carlo-matrix-inversion-and-reinforcement-learning.pdf)\n6.\t[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)\n\n### Tutorials\n1.\t[Reinforcement Learning](http://www.cse.unsw.edu.au/~cs9417ml/RL1/)\n2.\t[Reinforcement Learning Tutorial](http://wiki.ros.org/reinforcement_learning/Tutorials/Reinforcement%20Learning%20Tutorial)\n3.\t[Let’s make a DQN: Implementation](https://jaromiru.com/2016/10/03/lets-make-a-dqn-implementation/)\n4.\t[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)\n\n### Sample Code\n1. [Reinforcement Learning: An Introduction (2nd Edition)](https://github.com/ShangtongZhang/reinforcement-learning-an-introduction)\n\n### Libraries\n1.\t[OpenAI gym](https://gym.openai.com/)\n2.\t[OpenAI Retro](https://github.com/openai/retro)\n3.\t[Deep Mind Lab](https://github.com/deepmind/lab)\n4.\t[RL-Library](http://library.rl-community.org/wiki/Main_Page)\n5.\t[RL Lab](https://github.com/rll/rllab)\n\n## Contributing\nJika anda ingin berkontribusi dalam github ini, sangat disarankan untuk `Pull Request` namun dengan resource berbahasa indonesia.","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/datascienceid%2Freinforcement-learning-resources/projects"}