MiWaves Reinforcement Learning Algorithm
This addresses a public health challenge for emerging adults, but it appears incremental as it applies an existing RL method to a new domain-specific dataset.
The researchers tackled the problem of reducing cannabis use among emerging adults by developing MiWaves, a reinforcement learning algorithm to optimize personalized intervention prompts, and deployed it in a clinical trial from March to May 2024.
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.