Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management.
- DOI
- 10.1109/tnnls.2022.3232630
- Published
- 2024 May
- Container
- IEEE transactions on neural networks and learning systems
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2022.3232630,
title = {Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management.},
author = {Li Y and He S and Li Y and Shi Y and Zeng Z},
year = {2024},
journal = {IEEE transactions on neural networks and learning systems},
doi = {10.1109/tnnls.2022.3232630},
url = {https://doi.org/10.1109/tnnls.2022.3232630}
}RIS
TY - JOUR TI - Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management. AU - Li Y AU - He S AU - Li Y AU - Shi Y AU - Zeng Z PY - 2024 JO - IEEE transactions on neural networks and learning systems DO - 10.1109/tnnls.2022.3232630 UR - https://doi.org/10.1109/tnnls.2022.3232630 ER -
APA
Y, L., S, H., Y, L., Y, S., & Z, Z. (2024). Federated Multiagent Deep Reinforcement Learning Approach via Physics-Informed Reward for Multimicrogrid Energy Management.. IEEE transactions on neural networks and learning systems. https://doi.org/10.1109/tnnls.2022.3232630
Source records
- pubmed · retrieved 2026-09-25T10:29:10.459Z