  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.

Machine Learning;Applications;Machine Learning