Modeling Mobile Health Users as Reinforcement Learning Agents
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Abstract (arXiv)
Mobile health (mHealth) technologies empower patients to adopt/maintain healthy behaviors in their daily lives, by providing interventions (e.g. push notifications) tailored to the user's needs. In these settings, without intervention, human decision making may be impaired (e.g. valuing near term pleasure over own long term goals). In this work, we formalize this relationship with a framework in which the user optimizes a (potentially impaired) Markov Decision Process (MDP) and the mHealth agent intervenes on the user's MDP parameters. We show that different types of impairments imply different types of optimal intervention. We also provide analytical and empirical explorations of these differences.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 8 of its 24 references that are in the map. First posted on arXiv 1 Dec 2022.
Builds on
8 of 24 references.
References · 8
- 2020Learning Latent Representations to Influence Multi-Agent Interactioncited by 92
- 2019On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inferencecited by 49
- 2015Learning the Preferences of Ignorant, Inconsistent Agentscited by 45
- 2018Where Do You Think You're Going?: Inferring Beliefs about Dynamics from Behaviorcited by 40
- 2022The Boltzmann Policy Distribution: Accounting for Systematic Suboptimality in Human Modelscited by 31
- 2016Inverse Reinforcement Learning with Simultaneous Estimation of Rewards and Dynamicscited by 24
- 2022MIRROR: Differentiable Deep Social Projection for Assistive Human-Robot Communicationcited by 7
- 2019Robots that Take Advantage of Human Trustcited by 3
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Citing papers per year: 2023: 2.
2 papers.