Reinforcement Learning Resources


I'll constantly post resources of reinforcement learning (RL) here. The focus will be on deep reinforcement learning methods.

[Updated on November 20, 2020: Add description.]


  • awesome-rl by dbobrenko is a repository of RL related resources grouped by RL sub-domains.
  • awesome-rl by aikorea is another repository of RL related resources grouped by resource type.


Key Papers

  • Key Papers in Deep RL by OpenAI is a list of must-read papers of classic RL algorithms selected by OpenAI researchers.
  • Deep Reinforcement Learning by Yuxi Li is a comprehensive and up-to-date RL survey paper. It can also serve as a tutorial for people who want to have a general understanding of the field.


  • CS285 Deep Reinforcement Learning at UC Berkeley by Professor Sergey Levine is the latest deep RL course. It covers more recent topics and delves deeper into each of them, so it might be difficult for people who are new to RL. [Course website] [Playlist]
  • Introduction to Reinforcement Learning with David Silver by David Silver is an introductory RL course, which can be served as a course for beginners in RL. [Course website] [Playlist]

Blog Posts

  • A (Long) Peek into Reinforcement Learning by Lilian Weng is a good blog post for beginners in RL. For most of the algorithms, it can give you a high-level intuition to help you with further systematic study.


  • pytorch-rl by bentrevett is a practical introduction to RL using PyTorch.
  • OpenAI Spinning Up by OpenAI might be the best educational resource to start with in deep RL. It covers key concepts in RL, kinds of RL algorithms, and a tutorial to the policy gradient algorithm. It also provides a resource list and algorithm documentations.


  • OpenAI Gym by OpenAI is a toolkit for benchmarking RL algorithms.