Lops, Giada (Polytechnic of Bari), Manfredi, Gioacchino (Politecnico di Bari), Racanelli, Vito Andrea (Politecnico di Bari), De Cicco, Luca (Politecnico di Bari), Mascolo, Saverio (Politecnico di Bari)

A Safety Aware Deep Reinforcement Learning Technique for Automated Insulin Delivery

Scheduled for presentation during the Regular Session "Genetic and evolutionary computation" (WeAD), Wednesday, June 11, 2025, 11:10−11:30, Room C

33rd Mediterranean Conference on Control and Automation, June 10-13, 2025, Tangier, Morocco

This information is tentative and subject to change. Compiled on May 9, 2025

Abstract

Automated Insulin Delivery (AID) systems have shown great promise in managing diabetes by automating insulin administration. However, a significant challenge remains: preventing hypoglycemia or hyperglycemia during dynamic glucose fluctuations while minimizing the daily insulin dosage, or control effort. This study explores ways to enhance AID systems using a Reinforcement Learning (RL) algorithm called Maskable Proximal Policy Optimization (Maskable PPO), based on invalid action masking. Our findings demonstrate that this approach leads to a safety-aware framework for AIDs, providing highly realistic simulation scenarios for individual adult, adolescent, and child patients. The results show improved Time In Range (TIR) metrics (96.39%, 96.85%, and 54.43%), prevention of emergency bolus administration in adolescent and adult patients, and a reduction in Total Injected Insulin (TII) (17.32 U, 13.52 U, and 4.35 U per day).