# Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation

arXiv: https://arxiv.org/abs/2005.09059 · HTML: https://mlsanity.com/paper/2005.09059/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 18 of 18)

- [Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes](https://mlsanity.com/paper/2204.03376/) (2022)
- [Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications](https://mlsanity.com/paper/2302.07549/) (2023)
- [DTR-Bench: An in silico Environment and Benchmark Platform for Reinforcement Learning Based Dynamic Treatment Regime](https://mlsanity.com/paper/2405.18610/) (2024)
- [ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine](https://mlsanity.com/paper/2111.13415/) (2021)
- [Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey](https://mlsanity.com/paper/2108.04087/) (2021)
- [Towards Real-World Applications of Personalized Anesthesia Using Policy Constraint Q Learning for Propofol Infusion Control](https://mlsanity.com/paper/2303.10180/) (2023)
- [Using Reinforcement Learning to Simplify Mealtime Insulin Dosing for People with Type 1 Diabetes: In-Silico Experiments](https://mlsanity.com/paper/2309.09125/) (2023)
- [End-to-end Offline Reinforcement Learning for Glycemia Control](https://mlsanity.com/paper/2310.10312/) (2023)
- [Safe Linear Leveling Bandits](https://mlsanity.com/paper/2112.06728/) (2021)
- [Basal-Bolus Advisor for Type 1 Diabetes (T1D) Patients Using Multi-Agent Reinforcement Learning (RL) Methodology](https://mlsanity.com/paper/2307.08897/) (2023)
- [The Role of Artificial Intelligence in Enhancing Insulin Recommendations and Therapy Outcomes](https://mlsanity.com/paper/2503.18592/) (2025)
- [Counterfactual Explanations for Continuous Action Reinforcement Learning](https://mlsanity.com/paper/2505.12701/) (2025)
- [Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning](https://mlsanity.com/paper/2307.06501/) (2023)
- [Deep Reinforcement Learning Empowered Activity-Aware Dynamic Health Monitoring Systems](https://mlsanity.com/paper/2401.10794/) (2024)
- [An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning](https://mlsanity.com/paper/2403.07566/) (2024)
- [Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks](https://mlsanity.com/paper/2405.17458/) (2024)
- [Personalised Insulin Adjustment with Reinforcement Learning: An In-Silico Validation for People with Diabetes on Intensive Insulin Treatment](https://mlsanity.com/paper/2505.14477/) (2025)
- [Diabetes Lifestyle Medicine Treatment Assistance Using Reinforcement Learning](https://mlsanity.com/paper/2510.26807/) (2025)

## Abstract (arXiv)

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n=10), percentage time in target range [70, 180] mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n=10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.