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# Reinforcement Learning
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## Surveys
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- [Awesome Reinforcement Learning](https://github.com/aikorea/awesome-rl)
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- [Deep Reinforcement Learning survey](https://github.com/andrewliao11/Deep-Reinforcement-Learning-Survey)
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- [Awesome Explainable Reinforcement Learning](https://github.com/Plankson/awesome-explainable-reinforcement-learning)
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## Courses
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- [CS234: Reinforcement Learning Winter 2023 at Stanford](https://web.stanford.edu/class/cs234/)
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- [CS 285: Deep Reinforcement Learning at UC Berkeley](http://rail.eecs.berkeley.edu/deeprlcourse/)
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- [Introduction to Reinforcement Learning with David Silver at DeepMind](https://www.deepmind.com/learning-resources/introduction-to-reinforcement-learning-with-david-silver)
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- [Deep Reinforcement Learning](https://github.com/wangshusen/DRL) by Shusen Wang
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## Tutorials
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- [HuggingFace Deep RL Course](https://huggingface.co/learn/deep-rl-course/unit0/introduction)
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- [Open AI Spinning Up in Deep RL](https://spinningup.openai.com/en/latest/user/introduction.html)
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## Books
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- [Reinforcement Learning: An Introduction](http://incompleteideas.net/book/the-book-2nd.html) by Richard S. Sutton and Andrew G. Barto
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- [Deep Reinforcement Learning: Fundamentals, Research and Applications](https://deepreinforcementlearningbook.org/) by Hao Dong, et al.
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- [Deep Reinforcement Learning, a textbook](https://arxiv.org/abs/2201.02135) by Aske Plaat
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## Tools
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- [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/)
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- [rllab](https://github.com/rll/rllab)
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- [Gymnasium](https://gymnasium.farama.org/)
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## Talks
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- [Sergey Levine - Understanding the World Through Action @ UCL DARK](https://www.youtube.com/watch?v=yXImQEMS77g)
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