A three-agent reinforcement learning framework with domain-informed surrogate rewards outperforms single-agent and random policies for personalizing dyadic medication-adherence interventions in simulation.
Dyadic Reinforcement Learning
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abstract
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.
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cs.LG 1years
2025 1verdicts
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Reinforcement Learning on Dyads to Enhance Medication Adherence
A three-agent reinforcement learning framework with domain-informed surrogate rewards outperforms single-agent and random policies for personalizing dyadic medication-adherence interventions in simulation.