mlsanityData to 14 Oct 2026

Reinforcement Learning in Healthcare: A Survey

arXiv 1908.08796 ↗First posted 22 Aug 2019cs.LG cs.AIcited by 310

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Cited by 81 in Deep reinforcement learning methods, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.

Key facts. Cited by 310 papers in the map (data through 14 Oct 2026). First posted on arXiv 22 Aug 2019.

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Citing papers per year: 2020: 1; 2021: 26; 2022: 53; 2023: 66; 2024: 73; 2025: 54; 2026: 37.

310 papers.

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  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  2. 2022The Effects of Reward Misspecification: Mapping and Mitigating Misaligned Modelscited by 328
  3. 2024Deep Reinforcement Learning for Robotics: A Survey of Real-World Successescited by 176
  4. 2022Pessimistic Bootstrapping for Uncertainty-Driven Offline Reinforcement Learningcited by 125
  5. 2021OptiDICE: Offline Policy Optimization via Stationary Distribution Correction Estimationcited by 121
  6. 2024Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methodscited by 116
  7. 2021Parametrized quantum policies for reinforcement learningcited by 90
  8. 2021Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare Settingscited by 68
  9. 2023Large AI Models in Health Informatics: Applications, Challenges, and the Futurecited by 61
  10. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  11. 2023A Survey on Causal Reinforcement Learningcited by 45
  12. 2024A Survey of Constraint Formulations in Safe Reinforcement Learningcited by 44
38 more
  1. 2023Behavior Proximal Policy Optimizationcited by 43
  2. 2022Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guaranteescited by 42
  3. 2022Leveraging Offline Data in Online Reinforcement Learningcited by 42
  4. 2021PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learningcited by 41
  5. 2025A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Modelscited by 38
  6. 2022Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizabilitycited by 36
  7. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  8. 2020Robust Inverse Reinforcement Learning under Transition Dynamics Mismatchcited by 33
  9. 2022Sample-Efficient Reinforcement Learning with loglog(T) Switching Costcited by 32
  10. 2022Distributionally Robust Offline Reinforcement Learning with Linear Function Approximationcited by 32
  11. 2022Counterfactual harmcited by 29
  12. 2022Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithmcited by 29
  13. 2021Medical Dead-ends and Learning to Identify High-risk States and Treatmentscited by 28
  14. 2022Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement Learningcited by 26
  15. 2022Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimalitycited by 24
  16. 2022Transfer Learning for Contextual Multi-armed Banditscited by 24
  17. 2023Neurosymbolic Reinforcement Learning and Planning: A Surveycited by 24
  18. 2022Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Beliefcited by 22
  19. 2023Deep Reinforcement Learning for Cost-Effective Medical Diagnosiscited by 21
  20. 2023Replicability in Reinforcement Learningcited by 21
  21. 2022Improving Policy Optimization with Generalist-Specialist Learningcited by 20
  22. 2023Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approachcited by 19
  23. 2021Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approachcited by 18
  24. 2022Semi-Markov Offline Reinforcement Learning for Healthcarecited by 18
  25. 2022Unsupervised Representation Learning in Deep Reinforcement Learning: A Reviewcited by 18
  26. 2021A Provably-Efficient Model-Free Algorithm for Constrained Markov Decision Processescited by 17
  27. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applicationscited by 17
  28. 2023GANterfactual-RL: Understanding Reinforcement Learning Agents' Strategies through Visual Counterfactual Explanationscited by 17
  29. 2021Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatmentcited by 16
  30. 2021Bayesian Bellman Operatorscited by 16
  31. 2022Towards Safe Mechanical Ventilation Treatment Using Deep Offline Reinforcement Learningcited by 16
  32. 2023Model-based Offline Reinforcement Learning with Count-based Conservatismcited by 16
  33. 2024The RL/LLM Taxonomy Tree: Reviewing Synergies Between Reinforcement Learning and Large Language Modelscited by 15
  34. 2023Remote patient monitoring using artificial intelligence: Current state, applications, and challengescited by 14
  35. 2024Sample Complexity of Offline Distributionally Robust Linear Markov Decision Processescited by 14
  36. 2024Manipulating Recommender Systems: A Survey of Poisoning Attacks and Countermeasurescited by 14
  37. 2021Can Q-Learning be Improved with Advice?cited by 13
  38. 2022Near-Optimal Deployment Efficiency in Reward-Free Reinforcement Learning with Linear Function Approximationcited by 13

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