mlsanityData to 7 Oct 2026

Reinforcement Learning Textbook

arXiv 2201.09746 ↗First posted 19 Jan 2022cs.LG cs.AI cs.NEcited by 1

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Abstract (arXiv)

This textbook covers principles behind main modern deep reinforcement learning algorithms that achieved breakthrough results in many domains from game AI to robotics. All required theory is explained with proofs using unified notation and emphasize on the differences between different types of algorithms and the reasons why they are constructed the way they are.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 34 of its 83 references that are in the map. First posted on arXiv 19 Jan 2022.

Builds on

34 of 83 references.

References · 34

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  5. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  6. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 1,916
  7. 2016OpenAI Gymcited by 1,684
  8. 2018World Modelscited by 1,444
  9. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,327
  10. 2015Prioritized Experience Replaycited by 1,070
  11. 2018Exploration by Random Network Distillationcited by 966
  12. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
22 more
  1. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  2. 2017Reinforcement Learning with Deep Energy-Based Policiescited by 659
  3. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 555
  4. 2015Deep Recurrent Q-Learning for Partially Observable MDPscited by 457
  5. 2017A Tutorial on Thompson Samplingcited by 420
  6. 2016The CMA Evolution Strategy: A Tutorialcited by 364
  7. 2018Implicit Quantile Networks for Distributional Reinforcement Learningcited by 362
  8. 2017FeUdal Networks for Hierarchical Reinforcement Learningcited by 328
  9. 2020Agent57: Outperforming the Atari Human Benchmarkcited by 300
  10. 2017Noisy Networks for Explorationcited by 262
  11. 2016Safe and Efficient Off-Policy Reinforcement Learningcited by 260
  12. 2018Distributed Prioritized Experience Replaycited by 260
  13. 2013(More) Efficient Reinforcement Learning via Posterior Samplingcited by 233
  14. 2018Unity: A General Platform for Intelligent Agentscited by 232
  15. 2019Adversarial Policies: Attacking Deep Reinforcement Learningcited by 209
  16. 2018Generative Adversarial Imitation from Observationcited by 183
  17. 2017Neural Episodic Controlcited by 148
  18. 2018Simple random search provides a competitive approach to reinforcement learningcited by 75
  19. 2020Rewriting History with Inverse RL: Hindsight Inference for Policy Improvementcited by 59
  20. 2020Generalized Hindsight for Reinforcement Learningcited by 51
  21. 2019SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environmentscited by 9
  22. 2017The Intentional Unintentional Agent: Learning to Solve Many Continuous Control Tasks Simultaneouslycited by 5

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Cited by year

Citing papers per year: 2023: 1.

1 paper.

  1. 2023Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfindingcited by 5