Summary of Miwaves Reinforcement Learning Algorithm, by Susobhan Ghosh et al.
MiWaves Reinforcement Learning Algorithm
by Susobhan Ghosh, Yongyi Guo, Pei-Yao Hung, Lara Coughlin, Erin Bonar, Inbal Nahum-Shani, Maureen Walton, Susan Murphy
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research proposes an innovative approach to curb the rising cannabis use among emerging adults (EAs) aged 18-25 using reinforcement learning (RL). Specifically, it develops an algorithm called MiWaves that optimizes personalized intervention prompts to reduce cannabis consumption. This is achieved by leveraging domain expertise and prior data to tailor the likelihood of delivering intervention messages. The study presents a comprehensive overview of the algorithm’s design and experimental outcomes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cannabis use has become a significant public health concern, especially among young adults. To address this issue, scientists developed an AI-powered tool called MiWaves that helps people stop using cannabis. It uses machine learning to give personalized messages to reduce weed use. This new approach was tested in a clinical trial. |
Keywords
» Artificial intelligence » Likelihood » Machine learning » Reinforcement learning
