Function approximation for large markov decision processes using self-organizing neural networks

Teng, Teck-Hou

doi:10.1109/ijcnn.2015.7280608

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2023

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“…Both papers proposed to discretize the state space for dimension reduction. However, it can lead to considerable information loss (Wang et al, 2017). Sprague (2007) proposed an iterative dimension reduction method using neighborhood components analysis.…”

Section: Introductionmentioning

confidence: 99%

Deep spectral Q‐learning with application to mobile health

Gao

Shi

Song

2023

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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.

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Section: Introductionmentioning

confidence: 99%