I'm currently studying this topic (preparing an online course about it actually) and here are the best resources I found so far: Full Courses - From David Sil

I'm currently studying this topic (preparing an online course about it actually) and here are the best resources I found so far: Full Courses - From David Silver: http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html - From Yandex: https://yandexdataschool.com/edu-process/rl https://yandexdataschool.com/edu-process/rl - From Sergey Levine: http://rail.eecs.berkeley.edu/deeprlcoursesp17/index.html 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-learning-scratch-python-openai-gym/ https://www.learndatasci.com/tutorials/reinforcement-q-learn... - Q-learning and DQN, goes a bit further: https://neuro.cs.ut.ee/demystifying-deep-reinforcement-learning/ 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/ https://karpathy.github.io/2016/05/31/rl/ Baseline implementations: - "RL-Adventures", super clean Pytorch implementations: https://github.com/higgsfield/RL-Adventure https://github.com/higgsfield/RL-Adventure (DQN) and https://github.com/higgsfield/RL-Adventure-2 https://github.com/higgsfield/RL-Adventure-2 (PG) - Repo for the Deep Reinforcement Learning Nanodegree: https://github.com/udacity/deep-reinforcement-learning https://github.com/udacity/deep-reinforcement-learning - stable-baselines, a better documented fork of OpenAI baselines: https://github.com/hill-a/stable-baselines https://github.com/hill-a/stable-baselines If you have more high-quality resources please share! :D