Full Courses
- From David Silver: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
- From Yandex: https://yandexdataschool.com/edu-process/rl
- From Sergey Levine: http://rail.eecs.berkeley.edu/deeprlcoursesp17/index.html
Articles:
- Q-learning on Taxi-v2, very good basic explanation: https://www.learndatasci.com/tutorials/reinforcement-q-learn...
- Q-learning and DQN, goes a bit further: https://neuro.cs.ut.ee/demystifying-deep-reinforcement-learn...
- "Pong from Pixels from Karpathy", introduces DQN and PG: https://karpathy.github.io/2016/05/31/rl/
Baseline implementations:
- "RL-Adventures", super clean Pytorch implementations: https://github.com/higgsfield/RL-Adventure (DQN) and https://github.com/higgsfield/RL-Adventure-2 (PG)
- Repo for the Deep Reinforcement Learning Nanodegree: https://github.com/udacity/deep-reinforcement-learning
- stable-baselines, a better documented fork of OpenAI baselines: https://github.com/hill-a/stable-baselines
If you have more high-quality resources please share! :D