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Implicit Q-Learning for Offline Reinforcement Learning in Blood Glucose Management: A Cross-Dataset Evaluation Study

Year: 2026

Type: article

Abstract: Offline reinforcement learning enables learning insulin dosing policies from historical data without risky patient interactions. This study evaluates Implicit Q-Learning (IQL) on three real-world continuous glucose monitoring datasets: OhioT1DM (USA, type 1), ShanghaiT1DM (China, type 1), and ShanghaiT2DM (China, type 2). IQL achieved time in range of 68.5%, 54.3%, and 78.9% respectively. Cross-dataset transfer experiments demonstrated exceptional generalization with over 98% performance retention across geographic regions and diabetes types, suggesting that IQL captures fundamental glucose-insulin dynamics rather than dataset-specific patterns. Ablation studies validated our clinically-motivated reward function design, while sensitivity and robustness analyses confirmed algorithm stability across hyperparameter choices and data quality perturbations.

Source: Sensors and AI

DOI: 10.53941/sai.2026.100002

Authors: Bailing Zhang, Yuwei Mi

Institutions: NingboTech University, Ningbo University

Language: English

Topic: Diabetes Management and Research

Subfield: Endocrinology, Diabetes and Metabolism

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

OpenAlex ID: W7136103792