Deep spectral Q-learning with application to mobile health

Gao, Yuhe, Shi, Chengchun ORCID: 0000-0001-7773-2099 and Song, Rui (2023) Deep spectral Q-learning with application to mobile health. Stat, 12 (1). ISSN 2049-1573

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Abstract

Dynamic treatment regimes assign personalized treatments to patients sequentially over time based on their baseline information and time-varying covariates. In mobile health applications, these covariates are typically collected at different frequencies over a long time horizon. In this paper, we propose a deep spectral Q-learning algorithm, which integrates principal component analysis (PCA) with deep Q-learning to handle the mixed frequency data. In theory, we prove that the mean return under the estimated optimal policy converges to that under the optimal one and establish its rate of convergence. The usefulness of our proposal is further illustrated via simulations and an application to a diabetes dataset.

Item Type:	Article
Official URL:	https://onlinelibrary.wiley.com/journal/20491573
Additional Information:	© 2023 The Authors
Divisions:	Statistics
Subjects:	H Social Sciences > HA Statistics
Date Deposited:	19 Jun 2023 15:09
Last Modified:	20 Dec 2024 00:48
URI:	http://eprints.lse.ac.uk/id/eprint/119445

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