A Deep Deterministic Policy Gradient control algorithm for Automatic Insulin Delivery

Q3 Engineering

IFAC-PapersOnLine Pub Date : 2025-01-01 DOI:10.1016/j.ifacol.2025.06.003

Giada Lops, Vito Andrea Racanelli, Gioacchino Manfredi, Luca De Cicco, Saverio Mascolo

引用次数: 0

Abstract

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.

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胰岛素自动输送的深度确定性策略梯度控制算法

本研究探讨了在自动胰岛素输送（AID）系统中使用强化学习（RL）来优化胰岛素输注。深度确定性策略梯度（DDPG）算法作为一种潜在的控制策略进行了评估，并使用高度逼真的计算机仿真模型与模型预测控制（MPC）和PID控制器进行了比较。对成人1型糖尿病（T1D）患者的模拟表明，DDPG控制器有效地减少了低血糖和高血糖发作，并改善了血糖稳定性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IFAC-PapersOnLine Engineering-Control and Systems Engineering

CiteScore

1.70

自引率

0.00%

发文量

1122

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