Effect of Hyperparameters of Reinforcement Learning in Blood Glucose Control
- Resource Type
- Conference
- Authors
- Lehel, Denes-Fazakas; Siket, Mate; Szilagyi, Laszlo; Eigner, Gyorgy; Kovacs, Levente
- Source
- 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC) Systems, Man, and Cybernetics (SMC), 2023 IEEE International Conference on. :1333-1340 Oct, 2023
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Computing and Processing
General Topics for Engineers
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Transportation
Measurement
Neural networks
Reinforcement learning
Control systems
Glucose
Diabetes
Blood
glucose control
reinforcement learning
artificial pancreas
t1dm
- Language
- ISSN
- 2577-1655
Reinforcement learning (RL) has shown promise in controlling blood glucose levels in a personalized way in type 1 diabetic patients. In this study, we investigate the impact of different activation functions and layer numbers on RL performance in blood glucose control. We train RL agents with various combinations of activation functions and layer numbers on a virtual patient model. The RL agents are evaluated based on their ability to maintain blood glucose levels within a target range while minimizing the frequency and magnitude of hypoglycemia and hyperglycemia events. Our results show that the choice of activation function and layer number significantly affects the RL performance. Specifically, the agents with ReLU activation functions and two or three hidden layers outperform the other agents, achieving a higher percentage of time in the target range and fewer hypoglycemia and hyperglycemia events. These findings provide valuable insights for the development of RL-based blood glucose control systems in type 1 diabetic patients.