Designing Reinforcement Learning Algorithms for Digital Interventions: Pre-Implementation Guidelines

• Published in: Algorithms Vol. 15; no. 8; p. 255
• Main Authors: Trella, Anna L, Zhang, Kelly W, Nahum-Shani, Inbal, Shetty, Vivek, Doshi-Velez, Finale, Murphy, Susan A
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.08.2022
• Subjects: algorithm design; algorithm evaluation; Algorithms; Analysis; Best practice; CAI; Case studies; Computer assisted instruction; Data mining; Design; Guidelines; Intervention; Machine learning; mobile health; Online education; online learning; reinforcement learning (RL); Telemedicine; Germany; algorithm evaluation; mobile health; reinforcement learning (RL); algorithm design; online learning
• DOI: 10.3390/a15080255
• ISSN: 1999-4893

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.