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：数字健康领域正日益关注利用强化学习（RL）个性化治疗方案序列，以支持用户养成更健康的行为。这类序列决策问题涉及基于用户情境（例如，先前活动水平、位置等）决定何时治疗以及如何治疗。在线RL作为一种前景广阔的数据驱动方法，能够基于每位用户的历史响应进行学习，并利用这些知识实现个性化决策。然而，为确定该RL算法是否应被纳入面向实际部署的“优化”干预措施中，我们必须评估数据证据是否表明该算法确实在为用户实现治疗个性化。由于RL算法存在随机性，研究者可能误以为其在某些状态下进行了学习，并利用这种学习提供特定治疗。本文提出了个性化的工作定义，并引入一种基于重抽样的方法，用于探究RL算法所展现的个性化特征是否仅是算法随机性的假象。我们通过一项名为HeartSteps的体力活动临床试验数据（该试验使用了在线RL算法）进行案例研究，以阐释所提方法。我们展示了该方法如何增强算法个性化在数据驱动层面的真实广告效应——既涵盖研究中所有用户，也针对特定用户个体。