Assessing the Impact of Context Inference Error and Partial Observability on RL Methods for Just-In-Time Adaptive Interventions
This work addresses the challenge of personalizing health interventions for behavioral science applications, but it is incremental as it focuses on specific factors like context error and observability.
The paper tackled the problem of learning effective intervention policies for Just-in-Time Adaptive Interventions (JITAIs) by applying reinforcement learning methods, and found that propagating uncertainty from context inferences improves efficacy as uncertainty increases, while policy gradient algorithms offer robustness to partial observability.
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.