MiWaves Reinforcement Learning Algorithm
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Abstract (arXiv)
The escalating prevalence of cannabis use poses a significant public health challenge globally. In the U.S., cannabis use is more prevalent among emerging adults (EAs) (ages 18-25) than any other age group, with legalization in the multiple states contributing to a public perception that cannabis is less risky than in prior decades. To address this growing concern, we developed MiWaves, a reinforcement learning (RL) algorithm designed to optimize the delivery of personalized intervention prompts to reduce cannabis use among EAs. MiWaves leverages domain expertise and prior data to tailor the likelihood of delivery of intervention messages. This paper presents a comprehensive overview of the algorithm's design, including key decisions and experimental outcomes. The finalized MiWaves RL algorithm was deployed in a clinical trial from March to May 2024.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 6 of its 17 references that are in the map. First posted on arXiv 27 Aug 2024.
Builds on
6 of 17 references.
References · 6
- 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
- 2020Inference for Batched Banditscited by 63
- 2017Accurate Inference for Adaptive Linear Modelscited by 39
- 2021A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithmscited by 29
- 2017Action Centered Contextual Banditscited by 23
- 2022Statistical Inference After Adaptive Sampling for Longitudinal Datacited by 11
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.