A Quantitative Comparison of Deep Reinforcement Learning Algorithms for Type 1 Diabetes Control - Federico Baldisseri; Mohab Atanasious; Valentina Becchetti; Antonio Di Paola; Giada Lops; Danilo Menegatti; Andrea Wrona; Saverio Mascolo; Francesco Priscoli (2025 11th International Conference on Control, Decision and Information Technologies (CoDIT), 2025)
Type 1 diabetes is a growing global health challenge. Standard clinical practice often relies on manual insulin injections, which can lead to suboptimal glucose regulation. Recent advancements have shifted focus towards Artificial Pancreas systems, integrating continuous glucose monitoring with automated insulin delivery. This work presents a quantitative comparison of four Deep Reinforcement Learning algorithms for autonomous glycemic regulation via insulin injection: DDPG, PPO, SAC, and TD3. The validation is conducted using the Hovorka model, in presence of uncertainties on number, time and amount of meals. Results show that all four controllers are able to maintain blood glucose levels within the target range. The TD3 algorithm outperforms the others in terms of several key performance indicators such as time in range, time in hypo/hyperglycemia and total insulin usage, while also exhibiting fewer hyperglycemic episodes compared to prior works in academic literature.
Keywords: Uncertainty; Deep reinforcement learning; Regulation; Mathematical models; Robustness; Diabetes; Pancreas; Insulin; Blood; Standards; Type 1 Diabetes; Insulin; Deep Reinforcement Learning; DDPG; PPO; TD3; SAC