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Designing Reinforcement Learning Algorithms for Mobile Health
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About the presentation:
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 allow domain experts to have confidence that the RL algorithm they deploy can learn effectively in these challenging environments, we offer 1) a trustworthy and generalizable framework for designing and comprehensively evaluating RL algorithms in a principled manner and 2) reward design that generalizes the bandit algorithm to consider the impact of the current decision on the future. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the framework. We illustrate the use of the framework and reward design for developing 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 early 2023.
About the presenter:
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 allow domain experts to have confidence that the RL algorithm they deploy can learn effectively in these challenging environments, we offer 1) a trustworthy and generalizable framework for designing and comprehensively evaluating RL algorithms in a principled manner and 2) reward design that generalizes the bandit algorithm to consider the impact of the current decision on the future. Further, we provide guidelines on how to design simulation environments, a crucial tool for evaluating RL candidate algorithms using the framework. We illustrate the use of the framework and reward design for developing 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 early 2023.
About the presenter:
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