The purpose of this repo is to help you start with your reinforcement learning journey. Below, I divided the resources into three section so you can easily find what you are looking for. As a general rule, easy sections have problems that can be understood without any prior knowledge of reinforcement learning. Medium might require some additional research and advance are for individuals who are more familiar with the topic.
Also, you can follow my progress and my comments here:
Contact me if you feel that my list could use some additional links.
Enjoy
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A quick introduction to Q Learning: http://mnemstudio.org/path-finding-q-learning-tutorial.htm
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Reinforcement Learning Part 1: Q-learning and Exploration: https://studywolf.wordpress.com/2012/11/25/reinforcement-learning-q-learning-and-exploration/
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Getting ready for AI based gaming agents – Overview of Open Source Reinforcement Learning Platforms https://www.analyticsvidhya.com/blog/2016/12/getting-ready-for-ai-based-gaming-agents-overview-of-open-source-reinforcement-learning-platforms/
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Simple Beginner’s guide to Reinforcement Learning & its implementation: https://www.analyticsvidhya.com/blog/2017/01/introduction-to-reinforcement-learning-implementation/
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Source 3
- Source 1
- Source 2
- Source 1
- Source 2
- UCL Course on RL ([email protected]): http://www0.cs.ucl.ac.uk/staff/D.Silver/web/Teaching.html
- Source 2
- Deep Q Learning for Video Games - The Math of Intelligence #9 https://www.youtube.com/watch?v=79pmNdyxEGo
- How to use Q Learning in Video Games Easily https://www.youtube.com/watch?v=A5eihauRQvo
- Source 1
- Source 2
- Reinforcement Learning: An Introduction (Sutton & Barto 2016) http://ufal.mff.cuni.cz/~straka/courses/npfl114/2016/sutton-bookdraft2016sep.pdf
- Learning in Complex Systems (Spring 2011) Lecture Notes Nahum Shimkin http://webee.technion.ac.il/people/shimkin/LCS11/ch4_RL1.pdf
- Amazing Resource with many examples: https://github.com/aikorea/awesome-rl
- Source 2