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Books, Lecture Notes
- Foundations of Deep Reinforcement Learning, Graesser and Keng (2019)
- Reinforcement Learning: An Introduction, Sutton and Barto (2nd edition, in progress, 2014-2015)
- Deep Reinforcement Learning Hands-On, Maxim Lapan (2018)
- Algorithms of Reinforcement Learning, C. Szepesvári (2015), recommended by David Silver as more mathematical and faster paced than the Sutton and Barto book
Courses
- Nathan Lambert: Reinforcement Learning from Human Feedback (2026), accompanying rlhfbook.com / arXiv
Videos
- Offline Reinforcement Learning - Sergey Levine, 2020
- MIT 6.S091: Introduction to Deep Reinforcement Learning, Lex Fridman (2019)
- RL Course by David Silver, youtube (2015)
Posts
- Introduction to Reinforcement Learning, Andrei Radulescu-Banu (2021)
- A (Long) Peek into Reinforcement Learning, L. Weng (2018)
- SuperVize Me: What’s the Difference Between Supervised, Unsupervised, Semi-Supervised and Reinforcement Learning? by Isha Salian (2018)
Surveys
- A Brief Survey of Deep Reinforcement Learning, K. Arulkumaran et al (2017)
- Offline Reinforcement Learning: Tutorial, Review,and Perspectives on Open Problems, Sergey Levine et al (2020). Explains how RL is modified for offline learning.
- How to Train Your Robot with DeepReinforcement Learning – LessonsWe’ve Learned, J. Ibarz et al (2021)
Articles
- Generative Adversarial Imitation Learning, J. Ho, S. Ermon (2016)
- Schema Networks: Zero-shot Transfer with a Generative Causal Model ofIntuitive Physics, K. Kansky et al (2017)
- Value Prediction Network, J. Oh et al (2017)
- Offline Reinforcement Learning: Tutorial, Review,and Perspectives on Open Problems, Sergey Levine et al (2020)
- Conservative Q-Learning for Offline Reinforcement Learning, Aviral Kumar et al (2020)
- Simple Statistical Gradient-Following Algorithms for Connectionist Reinforcement Learning, Ronald J. Williams (1992), describes REINFORCE algorithm
- If MaxEnt RL is the Answer,What is the Question?, B. Eysenbach, S. Levine (2019)
- MaxEnt RL and Robust Control, B. Eysenbach, S. Levine (2020)
- Multi-Agent Generative Adversarial Imitation Learning, J. Song et al (2018)
- Learning Robust Rewards with Adversarial Inverse Reinforcement Learning, J. Fu et al (2018)
- Adversarial Imitation via Variational Inverse Reinforcement Learning, A. Qureshi (2018)
- Benchmarking Model-Based Reinforcement Learning, T. Wang et al (2019)
- Social Influence as Intrinsic Motivationfor Multi-Agent Deep Reinforcement Learning, N. Jaques et al (2019)
- Worst Cases Policy Gradients, Y.C. Tang et al (2019)
- An Investigation of Model-Free Planning, A. Guez (2019)
- Learning to Navigate in Cities Without a Map, P.Mirowski et al (2019)
- Dream to Control: Learning Behaviors by Latent Imagination, D. Hafer et al (2019)
- Towards General and Autonomous Learning of CoreSkills: A Case Study in Locomotion, R. Hafner et al (2020)
- OpenSpiel: A Framework for ReinforcementLearning in Games, M. Lanctor et al (2020)
- Mastering Atari With Discrete World Models, D. Hafner et al (2020)