reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use

TL;DR

reBandit is an online reinforcement learning algorithm designed for personalized mobile health interventions to reduce cannabis use among emerging adults, effectively adapting to diverse populations by leveraging random effects and Bayesian methods.

Contribution

The paper introduces reBandit, a novel RL algorithm that incorporates random effects and Bayesian priors for efficient learning in noisy, heterogeneous mobile health settings.

Findings

reBandit performs as well or better than baseline algorithms.

It adapts effectively to increasing population heterogeneity.

The performance gap widens with more diverse populations.

Abstract

The escalating prevalence of cannabis use, and associated cannabis-use disorder (CUD), poses a significant public health challenge globally. With a notably wide treatment gap, especially among emerging adults (EAs; ages 18-25), addressing cannabis use and CUD remains a pivotal objective within the 2030 United Nations Agenda for Sustainable Development Goals (SDG). In this work, we develop an online reinforcement learning (RL) algorithm called reBandit which will be utilized in a mobile health study to deliver personalized mobile health interventions aimed at reducing cannabis use among EAs. reBandit utilizes random effects and informative Bayesian priors to learn quickly and efficiently in noisy mobile health environments. Moreover, reBandit employs Empirical Bayes and optimization techniques to autonomously update its hyper-parameters online. To evaluate the performance of our algorithm, we construct a simulation testbed using data from a prior study, and compare against commonly used algorithms in mobile health studies. We show that reBandit performs equally well or better than all the baseline algorithms, and the performance gap widens as population heterogeneity increases in the simulation environment, proving its adeptness to adapt to diverse population of study participants.