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Investigation of reward functions for controlling blood glucose level using reinforcement learning

文献类型： 会议论文

第一作者： Dénes-Fazakas Lehel

作者： Dénes-Fazakas Lehel 1 ; Máté Siket 1 ; László Szilágyi 2 ; Gyorgy Eigner 3 ; Levente Kovács 3 ;

作者机构： 1.Physiological Controls Research Center, óbuda University|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, Budapest, Bécsi street 96/B

2.Physiological Controls Research Center, óbuda University|Computational Intelligence Research Group, Sapientia University, Tg., Mure?, Romania

3.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, óbuda University

关键词： Neural networks;Medical treatment;Reinforcement learning;Regulation;Physiology;Glucose;Trajectory

会议名称： IEEE International Symposium on Applied Computational Intelligence and Informatics

主办单位：

页码： 387-392

摘要： In the present study, we investigated the effect of different reward functions in insulin regulation using reinforcement learning. An artificial pancreas system is able to deliver insulin into the body in an automated way. The control algorithm of an automated insulin delivery system is a key player in achieving personalized therapy. Neural networks provide an approach to customize insulin administration by learning the patient’s habits and administering insulin accordingly. Therefore, we conducted experiments with neural networks based on reinforcement learning. Our goal was to find a neural network-based model and reward function that could learn the patient’s behavior and administers insulin with the best time in ranges. We evaluated the method on simulated virtual patients when sensor noise occurs. The results show that the bump functions were the most efficient in providing acceptable time in ranges.

分类号： tp18-53

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