Deep spectral Q-learning with application to mobile health

Yuhe Gao, Chengchun Shi and Rui Song

LSE Research Online Documents on Economics from London School of Economics and Political Science, LSE Library

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.

Keywords: dynamic treatment regimes; mixed frequency data; principal component analysis; reinforcement learning (search for similar items in EconPapers)
JEL-codes: C1 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2023-12-01
New Economics Papers: this item is included in nep-ecm and nep-hea
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Published in Stat, 1, December, 2023, 12(1). ISSN: 2049-1573

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