A Deep Deterministic Policy Gradient control algorithm for Automatic Insulin Delivery - Giada Lops; Vito Racanelli; Gioacchino Manfredi; Luca De Cicco; Saverio Mascolo (1st IFAC Workshop on Engineering Diabetes Technologies EDT 2025, 2025)
This study investigates the use of Reinforcement Learning (RL) to optimize insulin infusion in Automated Insulin Delivery (AID) systems. The Deep Deterministic Policy Gradient (DDPG) algorithm is evaluated as a potential control strategy and compared to Model Predictive Control (MPC) and PID controllers using a highly realistic in silico simulation model. Simulations for adult Type 1 Diabetes (T1D) patients demonstrate that the DDPG controller effectively reduces hypo- and hyperglycemic episodes and improves glucose stability.
1st IFAC Workshop on Engineering Diabetes Technologies EDT 2025
Keywords: New control system algorithms for Automatic Insulin Delivery; AI for Control; Optimization; Diabetes Management; Optimized Insulin Delivery; Reinforcement Learning; AID Systems; MPC; PID; DDPG