There is a growing interest in using reinforcement learning (RL) to personalize sequences of treatments in digital health to support users in adopting healthier behaviors. Such sequential decision-making problems involve decisions about when to treat and how to treat based on the user's context (e.g., prior activity level, location, etc.). Online RL is a promising data-driven approach for this problem as it learns based on each user's historical responses and uses that knowledge to personalize these decisions. However, to decide whether the RL algorithm should be included in an ``optimized'' intervention for real-world deployment, we must assess the data evidence indicating that the RL algorithm is actually personalizing the treatments to its users. Due to the stochasticity in the RL algorithm, one may get a false impression that it is learning in certain states and using this learning to provide specific treatments. We use a working definition of personalization and introduce a resampling-based methodology for investigating whether the personalization exhibited by the RL algorithm is an artifact of the RL algorithm stochasticity. We illustrate our methodology with a case study by analyzing the data from a physical activity clinical trial called HeartSteps, which included the use of an online RL algorithm. We demonstrate how our approach enhances data-driven truth-in-advertising of algorithm personalization both across all users as well as within specific users in the study.

翻译：随着数字健康领域利用强化学习（RL）个性化治疗序列以支持用户养成更健康行为的兴趣日益增长，这类序贯决策问题涉及根据用户情境（如先前活动水平、位置等）来决定何时干预及如何干预。在线RL作为有前景的数据驱动方法，能够基于每位用户的历史响应进行学习，并利用该知识个性化决策。然而，在判断是否应将RL算法纳入面向真实世界部署的“优化”干预方案时，我们必须评估数据证据是否表明RL算法确实在为其用户个性化治疗方案。由于RL算法存在随机性，人们可能错误地认为它在某些状态下正在学习，并利用这种学习提供特定治疗方案。本文提出了个性化的操作性定义，并引入基于重抽样的方法论，以探究RL算法展现的个性化是否是其随机性造成的人为假象。我们通过分析名为HeartSteps的身体活动临床试验数据（该试验使用了在线RL算法）进行案例研究，论证了该方法如何从全体用户及特定用户层面增强算法个性化效果的数据驱动真实性声明。