In recent years, reinforcement learning (RL) has acquired a prominent position in the space of health-related sequential decision-making, becoming an increasingly popular tool for delivering adaptive interventions (AIs). However, despite potential benefits, its real-life application is still limited, partly due to a poor synergy between the methodological and the applied communities. In this work, we provide the first unified survey on RL methods for learning AIs, using the common methodological umbrella of RL to bridge the two AI areas of dynamic treatment regimes and just-in-time adaptive interventions in mobile health. We outline similarities and differences between these two AI domains and discuss their implications for using RL. Finally, we leverage our experience in designing case studies in both areas to illustrate the tremendous collaboration opportunities between statistical, RL, and healthcare researchers in the space of AIs.

翻译：近年来，强化学习在健康相关序贯决策领域占据了重要地位，成为提供自适应干预日益普及的工具。然而，尽管潜力巨大，其实际应用仍受到限制，部分原因在于方法学界与应用社群之间的协同不足。本文首次以强化学习这一统一方法论为框架，系统梳理了基于强化学习学习自适应干预的研究，桥接了动态治疗方案与移动健康中即时自适应干预这两大自适应干预领域。我们概述了这两个自适应干预领域的异同，并探讨了其对强化学习应用的影响。最后，我们借助在两个领域设计案例研究的经验，揭示了统计学家、强化学习研究者和医疗研究人员在自适应干预领域开展合作的巨大机遇。