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Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines
Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881427/ https://www.ncbi.nlm.nih.gov/pubmed/36713810 http://dx.doi.org/10.3390/a15080255 |
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author | Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Doshi-Velez, Finale Murphy, Susan A. |
author_facet | Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Doshi-Velez, Finale Murphy, Susan A. |
author_sort | Trella, Anna L. |
collection | PubMed |
description | Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users’ tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022. |
format | Online Article Text |
id | pubmed-9881427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
record_format | MEDLINE/PubMed |
spelling | pubmed-98814272023-01-27 Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Doshi-Velez, Finale Murphy, Susan A. Algorithms Article Online reinforcement learning (RL) algorithms are increasingly used to personalize digital interventions in the fields of mobile health and online education. Common challenges in designing and testing an RL algorithm in these settings include ensuring the RL algorithm can learn and run stably under real-time constraints, and accounting for the complexity of the environment, e.g., a lack of accurate mechanistic models for the user dynamics. To guide how one can tackle these challenges, we extend the PCS (predictability, computability, stability) framework, a data science framework that incorporates best practices from machine learning and statistics in supervised learning to the design of RL algorithms for the digital interventions setting. Furthermore, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the PCS framework. We show how we used the PCS framework to design an RL algorithm for Oralytics, a mobile health study aiming to improve users’ tooth-brushing behaviors through the personalized delivery of intervention messages. Oralytics will go into the field in late 2022. 2022-08 2022-07-22 /pmc/articles/PMC9881427/ /pubmed/36713810 http://dx.doi.org/10.3390/a15080255 Text en https://creativecommons.org/licenses/by/4.0/This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Trella, Anna L. Zhang, Kelly W. Nahum-Shani, Inbal Shetty, Vivek Doshi-Velez, Finale Murphy, Susan A. Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title | Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title_full | Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title_fullStr | Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title_full_unstemmed | Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title_short | Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines |
title_sort | designing reinforcement learning algorithms for digital interventions: pre-implementation guidelines |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9881427/ https://www.ncbi.nlm.nih.gov/pubmed/36713810 http://dx.doi.org/10.3390/a15080255 |
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