Degradation-constrained multi-agent reinforcement learning with centralized training and decentralized execution for vehicle-to-grid optimization in renewable-dominated distribution networks.
- DOI
- 10.1038/s41598-026-55471-3
- Published
- 2026 Jun 3
- Container
- Scientific reports
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-55471-3,
title = {Degradation-constrained multi-agent reinforcement learning with centralized training and decentralized execution for vehicle-to-grid optimization in renewable-dominated distribution networks.},
author = {Shiferaw Y and Kiros M and Yeneneh K and Sufe G},
year = {2026},
journal = {Scientific reports},
doi = {10.1038/s41598-026-55471-3},
url = {https://doi.org/10.1038/s41598-026-55471-3}
}RIS
TY - JOUR TI - Degradation-constrained multi-agent reinforcement learning with centralized training and decentralized execution for vehicle-to-grid optimization in renewable-dominated distribution networks. AU - Shiferaw Y AU - Kiros M AU - Yeneneh K AU - Sufe G PY - 2026 JO - Scientific reports DO - 10.1038/s41598-026-55471-3 UR - https://doi.org/10.1038/s41598-026-55471-3 ER -
APA
Y, S., M, K., K, Y., & G, S. (2026). Degradation-constrained multi-agent reinforcement learning with centralized training and decentralized execution for vehicle-to-grid optimization in renewable-dominated distribution networks.. Scientific reports. https://doi.org/10.1038/s41598-026-55471-3
Source records
- pubmed · retrieved 2026-09-25T17:24:08.431Z
- europe-pmc · retrieved 2026-09-25T17:24:08.454Z