  Public health programs often provide interventions to encourage beneficiary
adherence,and effectively allocating interventions is vital for producing the
greatest overall health outcomes. Such resource allocation problems are often
modeled as restless multi-armed bandits (RMABs) with unknown underlying
transition dynamics, hence requiring online reinforcement learning (RL). We
present Bayesian Learning for Contextual RMABs (BCoR), an online RL approach
for RMABs that novelly combines techniques in Bayesian modeling with Thompson
sampling to flexibly model the complex RMAB settings present in public health
program adherence problems, such as context and non-stationarity. BCoR's key
strength is the ability to leverage shared information within and between arms
to learn the unknown RMAB transition dynamics quickly in intervention-scarce
settings with relatively short time horizons, which is common in public health
applications. Empirically, BCoR achieves substantially higher finite-sample
performance over a range of experimental settings, including an example based
on real-world adherence data that was developed in collaboration with ARMMAN,
an NGO in India which runs a large-scale maternal health program, showcasing
BCoR practical utility and potential for real-world deployment.
