mlsanityData to 14 Oct 2026

A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare

arXiv 2310.18354 ↗First posted 23 Oct 2023cs.CLcited by 1

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Abstract (arXiv)

Reinforcement learning (RL) has emerged as a powerful approach for tackling complex medical decision-making problems such as treatment planning, personalized medicine, and optimizing the scheduling of surgeries and appointments. It has gained significant attention in the field of Natural Language Processing (NLP) due to its ability to learn optimal strategies for tasks such as dialogue systems, machine translation, and question-answering. This paper presents a review of the RL techniques in NLP, highlighting key advancements, challenges, and applications in healthcare. The review begins by visualizing a roadmap of machine learning and its applications in healthcare. And then it explores the integration of RL with NLP tasks. We examined dialogue systems where RL enables the learning of conversational strategies, RL-based machine translation models, question-answering systems, text summarization, and information extraction. Additionally, ethical considerations and biases in RL-NLP systems are addressed.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

18 of 43 references.

References · 18

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 785
  4. 2020MedDialog: Two Large-scale Medical Dialogue Datasetscited by 161
  5. 2019Better Rewards Yield Better Summaries: Learning to Summarise Without Referencescited by 77
  6. 2018A Study of Reinforcement Learning for Neural Machine Translationcited by 74
  7. 2019Truly Proximal Policy Optimizationcited by 69
  8. 2018Learning to Extract Coherent Summary via Deep Reinforcement Learningcited by 64
  9. 2018Reinforcement Learning for Relation Classification from Noisy Datacited by 46
  10. 2019How to Build User Simulators to Train RL-based Dialog Systemscited by 46
  11. 2017Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learningcited by 44
  12. 2020Refine and Imitate: Reducing Repetition and Inconsistency in Persuasion Dialogues via Reinforcement Learning and Human Demonstrationcited by 20
6 more
  1. 2020Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learningcited by 16
  2. 2023How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domaincited by 14
  3. 2018Goal-Oriented Chatbot Dialog Management Bootstrapping with Transfer Learningcited by 11
  4. 2020Aspect and Opinion Aware Abstractive Review Summarization with Reinforced Hard Typed Decodercited by 4
  5. 2023W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognitioncited by 4
  6. 2020Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learningcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026RADS: Reinforcement Learning-Based Sample Selection Improves Transfer Learning in Low-resource and Imbalanced Clinical Settingscited by 0