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GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes

arXiv 2604.00385 ↗First posted 1 Apr 2026cs.LGcited by 0

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Abstract (arXiv)

Type 1 diabetes (T1D) management requires continuous adjustment of insulin and lifestyle behaviors to maintain blood glucose within a safe target range. Although automated insulin delivery (AID) systems have improved glycemic outcomes, many patients still fail to achieve recommended clinical targets. Current reinforcement learning (RL)-based methods focus primarily on insulin-only treatment and do not provide behavioral recommendations for glucose control. To address this gap, we propose GUIDE, an RL-based decision-support framework designed to complement AID technologies by providing structured behavioral recommendations defined by intervention type, magnitude, and timing, including bolus insulin administration and carbohydrate intake events. GUIDE integrates a patient-specific glucose predictor trained on real-world continuous glucose monitoring data and supports offline and online RL algorithms within a unified environment. The algorithms are evaluated across 25 individuals from the AZT1D dataset and 12 individuals from the OhioT1DM dataset. Among the evaluated algorithms, CQL-BC achieved the best performance on both datasets, with mean time-in-range values of 84.18 ± 19.89% on AZT1D and 77.64 ± 8.81% on OhioT1DM. It also maintained time-below-range values of 0.43 ± 1.27% and 2.81 ± 3.56%, respectively. Behavioral analysis yielded mean cosine similarities of 0.767 ± 0.128 on AZT1D and 0.774 ± 0.162 on OhioT1DM, indicating that the learned policy preserves key structural characteristics of patient action patterns. These findings demonstrate the potential of conservative offline RL with a structured behavioral action space to provide personalized and behaviorally plausible decision support for diabetes management.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 43 references that are in the map. First posted on arXiv 1 Apr 2026.

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References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  4. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  5. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  6. 2020Deep Reinforcement Learning for Closed-Loop Blood Glucose Controlcited by 26
  7. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetescited by 22
  8. 2025Glycemic-Aware and Architecture-Agnostic Training Framework for Blood Glucose Forecasting in Type 1 Diabetescited by 5
  9. 2025AZT1D: A Real-World Dataset for Type 1 Diabetescited by 5
  10. 2020Model-Based Reinforcement Learning for Type 1Diabetes Blood Glucose Controlcited by 2

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  2. 2023End-to-end Offline Reinforcement Learning for Glycemia Control
  3. 2026Interpretable Language Model for Closed-Loop Type 1 Diabetes Control
  4. 2026Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
  5. 2026Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management
  6. 2025Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Angle
  7. 2026From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
  8. 2026The hidden risks of temporal resampling in clinical reinforcement learning