论文标题
将移动健康用户建模为强化学习者
Modeling Mobile Health Users as Reinforcement Learning Agents
论文作者
论文摘要
移动健康（MHealth）技术通过提供针对用户需求量身定制的干预措施（例如推送通知），使患者能够在日常生活中采用/维持健康的行为。在这些环境中，如果没有干预，人类决策可能会受到损害（例如，以自己的长期目标对近期愉悦的估值）。在这项工作中，我们使用一个框架正式化了这种关系，在该框架中，用户优化了（潜在受损的）马尔可夫决策过程（MDP），而MHealth代理会介入用户的MDP参数上。我们表明，不同类型的损害意味着不同类型的最佳干预措施。我们还提供了这些差异的分析和经验探索。
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the user's needs. In these settings, without intervention, human decision making may be impaired (e.g. valuing near term pleasure over own long term goals). In this work, we formalize this relationship with a framework in which the user optimizes a (potentially impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the user's MDP parameters. We show that different types of impairments imply different types of optimal intervention. We also provide analytical and empirical explorations of these differences.
