reBandit: Random Effects based Online RL algorithm for Reducing Cannabis
  Use
Susobhan Ghosh Yongyi Guo Pei-Yao Hung Lara Coughlin Erin Bonar Inbal Nahum-Shani Maureen Walton Susan Murphy
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.