Work

Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions

Year: 2023

Type: article

Abstract: Just-in-Time Adaptive Interventions (JITAIs) are a class of personalized health interventions developed within the behavioral science community. JITAIs aim to provide the right type and amount of support by iteratively selecting a sequence of intervention options from a pre-defined set of components in response to each individual's time varying state. In this work, we explore the application of reinforcement learning methods to the problem of learning intervention option selection policies. We study the effect of context inference error and partial observability on the ability to learn effective policies. Our results show that the propagation of uncertainty from context inferences is critical to improving intervention efficacy as context uncertainty increases, while policy gradient algorithms can provide remarkable robustness to partially observed behavioral state information.

Source: PubMed

DOI: 10.48550/arxiv.2305.09913

Authors: Karine, Karine, Klasnja, Predrag, Murphy, Susan A., Marlin, Benjamin M.

Institutions: University of Massachusetts Amherst, University of Michigan, Harvard University

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Drug Abuse, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering