reBandit: Random Effects based Online RL algorithm for Reducing Cannabis Use
This work addresses the public health challenge of cannabis-use disorder in emerging adults, but it is incremental as it builds on existing RL methods for mobile health applications.
The authors tackled the problem of reducing cannabis use among emerging adults by developing reBandit, an online reinforcement learning algorithm that uses random effects and Bayesian priors for personalized mobile health interventions, showing it performs equally well or better than baseline algorithms in simulations, with the performance gap widening as population heterogeneity increases.
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.