mlsanityData to 7 Oct 2026

End-to-end Offline Reinforcement Learning for Glycemia Control

arXiv 2310.10312 ↗First posted 16 Oct 2023cs.AI cs.LG q-bio.QMcited by 2

On the map: Offline RL and imitation learning from data → Safe offline RL

Abstract (arXiv)

The development of closed-loop systems for glycemia control in type I diabetes relies heavily on simulated patients. Improving the performances and adaptability of these close-loops raises the risk of over-fitting the simulator. This may have dire consequences, especially in unusual cases which were not faithfully-if at all-captured by the simulator. To address this, we propose to use offline RL agents, trained on real patient data, to perform the glycemia control. To further improve the performances, we propose an end-to-end personalization pipeline, which leverages offline-policy evaluation methods to remove altogether the need of a simulator, while still enabling an estimation of clinically relevant metrics for diabetes.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 13 of its 49 references that are in the map. First posted on arXiv 16 Oct 2023.

Builds on

13 of 49 references.

Closest ancestors · 2

Same direction, newest first

  1. 2023Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications15 Feb
  2. 2022Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes7 Apr

Other references · 11

  1. 2015Continuous control with deep reinforcement learningcited by 3,279
  2. 2015Trust Region Policy Optimizationcited by 2,503
  3. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  4. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  5. 2020Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problemscited by 1,873
  6. 2020Conservative Q-Learning for Offline Reinforcement Learningcited by 1,777
  7. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,227
  8. 2021A Minimalist Approach to Offline Reinforcement Learningcited by 882
  9. 2019Batch Policy Learning under Constraintscited by 242
  10. 2020Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validationcited by 18
  11. 2022No More Pesky Hyperparameters: Offline Hyperparameter Tuning for RLcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026The hidden risks of temporal resampling in clinical reinforcement learningcited by 1
  2. 2026CNA: An AI-Oriented Comprehensive Normalized Assessment for Healthy Status and Application to Optimize RRT Strategies by Reinforcement Learningcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2026GUIDE: Reinforcement Learning for Behavioral Action Support in Type 1 Diabetes
  2. 2025Flexible Blood Glucose Control: Offline Reinforcement Learning from Human Feedback
  3. 2026Insulin4RL: Real-Time Insulin Management in the Intensive Care Unit for Offline Reinforcement Learning
  4. 2026From Prediction to Intervention: Personalized Meal-Level Glucose Regulation via an LLM Agent
  5. 2026Learning to Compress Time-to-Control: A Reinforcement Learning Framework for Chronic Disease Management
  6. 2026Interpretable Language Model for Closed-Loop Type 1 Diabetes Control
  7. 2025Are Large Language Models Dynamic Treatment Planners? An In Silico Study from a Prior Knowledge Injection Angle
  8. 2022A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems