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

2024-02-27 · 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.

📄 PDF Abstract BibTeX arXiv:2402.17739

Code (1)

statisticalreinforcementlearninglab/miwaves_rl_service 공식 구현

Tasks

Reinforcement Learning (RL)

Random-Effects Algorithm for Random Objects in Metric Spaces

2026-05-04 · Marcos Matabuena, Mateo Cámara arxiv

Across many scientific disciplines, multiple observations are collected from the same experimental units, and in modern datasets these observations often arise as non-Euclidean random objects. In such settings, the incor…

Double Machine Learning for Static Panel Models with Fixed Effects

2023-12-13 · Paul S. Clarke, Annalivia Polselli

Recent advances in causal inference have seen the development of methods which make use of the predictive power of machine learning algorithms. In this paper, we develop novel double machine learning (DML) procedures for…

Causal Inference Ensemble Learning

Random Feature Maps via a Layered Random Projection (LaRP) Framework for Object Classification

2016-02-04 · A. G. Chung, M. J. Shafiee, A. Wong

The approximation of nonlinear kernels via linear feature maps has recently gained interest due to their applications in reducing the training and testing time of kernel-based learning algorithms. Current random projecti…

General Classification Object

NoLimits.jl: Flexible and Composable Nonlinear Mixed-Effects Modeling in Julia

2026-06-23 · Manuel Huth, Jonas Arruda, Nina Schmid, Roy Gusinow 외 arxiv

Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of the model structures, inference methods, machine-learning components,…

A Reinforcement Learning Approach to Estimating Long-term Treatment Effects

2022-10-14 · Ziyang Tang, Yiheng Duan, Stephanie Zhang, Lihong Li

Randomized experiments (a.k.a. A/B tests) are a powerful tool for estimating treatment effects, to inform decisions making in business, healthcare and other applications. In many problems, the treatment has a lasting eff…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)