Hybrid Control Policy for Artificial Pancreas via Ensemble Deep Reinforcement Learning
The study presents novel control policies for AP systems, affirming their great potential for efficient closed-loop glucose control.
Authors
- Luolin Xiong (ORCID: https://orcid.org/0009-0001-0142-7933)
- Yang Tang (ORCID: https://orcid.org/0000-0002-2750-8029)
- Liang Wu (ORCID: https://orcid.org/0000-0002-3763-1274)
- Jian Zhou (ORCID: https://orcid.org/0000-0001-7800-881X)
- Tianyu Wu (ORCID: https://orcid.org/0009-0008-8104-3382)
- Feng Qian (ORCID: https://orcid.org/0000-0003-2781-332X)
- Wenzhou Lv (ORCID: https://orcid.org/0009-0002-3963-859X)
Institutions
- East China University of Science and Technology (CN)
- Shanghai Jiao Tong University (CN)
- East China University of Technology (CN)
- Shanghai Sixth People's Hospital (CN)
Publication Details
- Journal
- IEEE Transactions on Biomedical Engineering
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1109/tbme.2024.3451712
- Citations
- 10
- Primary Topic
- Smart Grid Energy Management
- Type
- article
- Field-Weighted Citation Impact
- 1.88