A collection of videos, courses, cheet sheets, papers and everything else around Deep Reinforcement Learning
- UC Berkley CS285: Course website, Lecture recordings
- UC Berkley Bootcamp: 2017 website
- University College London (UCL): Course by David Silver
- Spinning up AI: From theory to application, with some introductions, code examples and exercises
- FreeCodeCamp: Online articles, excercises and code
- Reinforcement Learning: An Introduction Sutton and Barto 2018: all reseources including book, slides, exercises, code (MIT); PDF
- The Deep Learning Book Goodfellow et al. 2016: all recources including exercises and slides (Ian Goodfellow), PDF
- Algorithms of Reinforcement Learning Szepesa 2009 (updated 2019): all recources including slides and exercises (University of Alberta); PDF
- Grokking Deep Reinforcement Learning Morales 2010 (updated 2019): Github page has chapters and Jupyter notebooks with code
- https://pytorch.org/tutorials/intermediate/reinforcement_q_learning.html
- https://github.com/MishaLaskin/torchingup/blob/master/algos/dqn/dqn.py
- https://github.com/openai/gym/blob/38a1f630dc9815a567aaf299ae5844c8f8b9a6fa/gym/envs/robotics/robot_env.py
- https://stable-baselines.readthedocs.io/en/master/modules/dqn.html
- https://towardsdatascience.com/understanding-and-implementing-distributed-prioritized-experience-replay-horgan-et-al-2018-d2c1640e0520
- https://github.com/cyoon1729/distributedRL
- DeepMind: Prioritized Experience Replay
- DeepMind: Distributed Prioritized Experience Replay
- Low quality paper
- Standford: dirtibuted deep q-learning
- Distributed Pytorch
- Liu 2018
- https://julien-vitay.net/deeprl/Valuebased.html