Adaptive average arterial pressure control by multi-agent on-policy reinforcement learning
Abstract The current research introduces a model-free ultra-local model (MFULM) controller that utilizes the multi-agent on-policy reinforcement learning (MAOPRL) technique for remotely regulating blood pressure through precise drug dosing in a closed-loop system. Within the closed-loop system, ther...
Saved in:
| Main Authors: | Xiaofeng Hong, Walid Ayadi, Khalid A. Alattas, Ardashir Mohammadzadeh, Mohamad Salimi, Chunwei Zhang |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84791-5 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Reliability of remote at-home oscillometric blood pressure monitoring in community-dwelling children aged 3–17
by: Emily H. Ho, et al.
Published: (2025-06-01) -
Usefulness of the CDC/AAP and the EFP/AAP Criteria to Detect Subclinical Atherosclerosis in Subjects with Diabetes and Severe Periodontal Disease
by: Greicy C. Montenegro-González, et al.
Published: (2025-04-01) -
An Analytical Model for Aerostatic Thrust Bearings Based on the Average Pressure of the Area Surrounded by Orifice
by: Jian Zheng, et al.
Published: (2025-03-01) -
Genetic Improvement and Functional Characterization of AAP1 Gene for Enhancing Nitrogen Use Efficiency in Maize
by: Mo Zhu, et al.
Published: (2025-07-01) -
On Determinants of Exchange Market Pressure in Turkey: The Role of Model Uncertainty
by: Gülden Poyraz, et al.
Published: (2021-06-01)