Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2021

Abstract

In many community health settings, it is crucial to have a systematic monitoring and intervention process to ensure that the patients adhere to healthcare programs, such as periodic health checks or taking medications. When these interventions are expensive, they can be provided to only a fixed small fraction of the patients at any period of time. Hence, it is important to carefully choose the beneficiaries who should be provided with interventions and when. We model this scenario as a restless multi-armed bandit (RMAB) problem, where each beneficiary is assumed to transition from one state to another depending on the intervention provided to them. In practice, the transition probabilities are unknown a priori, and hence, we propose a mechanism for the problem of balancing the explore-exploit trade-off. Empirically, we find that our proposed mechanism outperforms the baseline intervention scheme maternal healthcare dataset.

Keywords

reinforcement learning, multi-armed bandits, unknown transition probabilities

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering; Intelligent Systems and Optimization

Publication

AAMAS '21: 20th International Conference on Autonomous Agents and Multiagent Systems

First Page

1467

Last Page

1468

ISBN

9781450383073

City or Country

London

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