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：在数字健康领域，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）决定何时治疗以及如何治疗。在线RL作为数据驱动的解决方案具有潜力，它基于每位用户的历史反应进行学习，并利用这些知识个性化决策。然而，为了确定RL算法是否应被纳入实际部署的“优化”干预中，我们必须评估数据证据是否表明该算法确实在为用户个性化治疗。由于RL算法的随机性，人们可能误以为它在特定状态下进行学习并利用这种学习提供特定治疗。我们给出了个性化的操作性定义，并引入了一种基于重抽样的方法，以探究RL算法表现出的个性化是否仅是算法随机性的假象。我们通过一项名为HeartSteps的体育活动临床试验数据作为案例研究来说明该方法，该试验采用了在线RL算法。我们展示了该方法如何增强算法个性化的数据驱动真实性陈述，既覆盖研究中所有用户，也针对特定用户。