# A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare Source: [A Review of Reinforcement Learning for Natural Language Processing, and Applications in Healthcare]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:55:42.510Z Authors: Ying Liu, Haozhu Wang, Huixue Zhou, Mingchen Li, Yu Hou, Sicheng Zhou, Fang Wang, Rama Hoetzlein, Rui Zhang Published: 2023-10-23T20:26:15.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()