Dyadic Reinforcement Learning
This work addresses the challenge of enhancing social support in mobile health for individuals with burdensome medical conditions, though it is incremental as it adapts existing RL methods to a dyadic context.
The paper tackles the problem of personalizing mobile health interventions for dyads (a target person and their care partner) by developing dyadic RL, an online reinforcement learning algorithm, which is shown to perform well in simulation studies including a realistic test bed from real data.
Mobile health aims to enhance health outcomes by delivering interventions to individuals as they go about their daily life. The involvement of care partners and social support networks often proves crucial in helping individuals managing burdensome medical conditions. This presents opportunities in mobile health to design interventions that target the dyadic relationship -- the relationship between a target person and their care partner -- with the aim of enhancing social support. In this paper, we develop dyadic RL, an online reinforcement learning algorithm designed to personalize intervention delivery based on contextual factors and past responses of a target person and their care partner. Here, multiple sets of interventions impact the dyad across multiple time intervals. The developed dyadic RL is Bayesian and hierarchical. We formally introduce the problem setup, develop dyadic RL and establish a regret bound. We demonstrate dyadic RL's empirical performance through simulation studies on both toy scenarios and on a realistic test bed constructed from data collected in a mobile health study.