mlsanityData to 7 Oct 2026

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

arXiv 2305.09913 ↗First posted 17 May 2023cs.LG cs.AIcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 25 references that are in the map. First posted on arXiv 17 May 2023.

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6 of 25 references.

References · 6

  1. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  2. 2015Prioritized Experience Replaycited by 1,070
  3. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  4. 2019Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activitycited by 70
  5. 2020Batch Policy Learning in Average Reward Markov Decision Processescited by 69
  6. 2016A Reinforcement Learning System to Encourage Physical Activity in Diabetes Patientscited by 38

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Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024StepCountJITAI: simulation environment for RL with application to physical activity adaptive interventioncited by 1
  2. 2025Enhancing Adaptive Behavioral Interventions with LLM Inference from Participant-Described Statescited by 1
  3. 2024BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settingscited by 0