Multi-agent hierarchical reinforcement learning for energy management

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., & Mordatch, I. (2017) · arXiv · doi:10.48550/arxiv.1706.02275 · Cited by 3,377

An Introduction to Deep Reinforcement Learning

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Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications

Nguyen, T. T., Nguyen, N. D., & Nahavandi, S. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2977374 · Cited by 1,306

Recent Advances in Hierarchical Reinforcement Learning

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Multi-Agent Deep Reinforcement Learning for Large-Scale Traffic Signal Control

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The Option-Critic Architecture

Bacon, P., Harb, J., & Precup, D. (2017) · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) · doi:10.1609/aaai.v31i1.10916 · Cited by 779

Multi-agent Reinforcement Learning: An Overview

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A Review of Deep Reinforcement Learning for Smart Building Energy Management

Yu, L., Qin, S., Zhang, M., Shen, C. Q., Jiang, T., & Guan, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3078462 · Cited by 556

Peer-to-peer energy sharing through a two-stage aggregated battery control in a community Microgrid

Long, C., Wu, J., Zhou, Y., & Jenkins, N. (2018) · Applied Energy · doi:10.1016/j.apenergy.2018.05.097 · Cited by 519

Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

Chen, X., Qu, G., Tang, Y., Low, S. H., & Li, N. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3154718 · Cited by 465

Safe reinforcement learning for power system control: A review

Yu, P., Zhang, H., Song, Y., Wang, Z., Dong, H., & Ji, L. (2025) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2025.116022 · Cited by 117

Multi-agent reinforcement learning for resources allocation optimization: a survey

Hady, M. A., Hu, S., Pratama, M., Cao, Z., & Kowalczyk, R. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11340-5 · Cited by 131

Energy management of multi-mode plug-in hybrid electric vehicle using multi-agent deep reinforcement learning

Hua, M., Zhang, C., Zhang, F., Li, Z., Yu, X., Xu, H., & Zhou, Q. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121526 · Cited by 75

Adaptive multi-agent reinforcement learning for flexible resource management in a virtual power plant with dynamic participating multi-energy buildings

Wu, H., Qiu, D., Zhang, L., & Sun, M. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123998 · Cited by 73

Coordinated energy management for integrated energy system incorporating multiple flexibility measures of supply and demand sides: A deep reinforcement learning approach

Liu, J., Li, Y., Ma, Y., Qin, R., Meng, X., & Wu, J. (2023) · Energy Conversion and Management · doi:10.1016/j.enconman.2023.117728 · Cited by 69

Flexible Power Load Control Based on Hierarchical Multi-Agent Reinforcement Learning

Wang, Y., Wang, X., Zhu, Q., Yuan, Q., Luo, G., & Li, J. (2024) · International Conference on Power and Renewable Energy (ICPRE) · doi:10.1109/icpre62586.2024.10768325 · Cited by 1

Hierarchical Reinforcement Learning for Power Network Topology ControlPreprint

Manczak, B., Viebahn, J., & van Hoof, H. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2311.02129 · Cited by 14

Data-driven sustainable distributed energy resources’ control based on multi-agent deep reinforcement learning

Jendoubi, I., & Bouffard, F. (2022) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2022.100919 · Cited by 25

Enabling Transactive Microgrids: A Multi-Agent Reinforcement Learning Hierarchical Framework

Cuadrado Avila, N. M. (2023) · MBZUAI iRep · doi:10.82419/106

MARLISA

Vázquez-Canteli, J. R., Henze, G. P., & Nagy, Z. (2020) · ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys) · doi:10.1145/3408308.3427604 · Cited by 67

Multi-Agent Reinforcement Learning for Power Grid Topology OptimizationPreprint

van der Sar, E., Zocca, A., & Bhulai, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.02605 · Cited by 5

Optimization and control of integrated energy systems based on multiagent deep reinforcement learning

Shen, H., Liu, Q., Zhang, Q., Zhang, M., Liu, X., Ruan, Y., Qian, F., Xu, T., Meng, H., & Yao, Y. (2025) · International Conference on Power Electronics and Artificial Intelligence (PEAI) · doi:10.1117/12.3066781

Reinforcement learning in power system scheduling and control: A unified perspective

Ahamed, T. P. I., Jasmin, E. A., & Al‐Ammar, E. A. (2011) · IEEE Symposium on Computers & Informatics (ISCI) · doi:10.1109/isci.2011.5958993 · Cited by 7

Reinforcement Learning to Enhance Optimal Operation of Resilient Community Energy Systems

Li, Z., Han, X., Wang, J., & Zuo, W. (2024) · IBPSA-USA Building Simulation Conference (SimBuild) · doi:10.26868/30680611.2024.2157

Learning a Multi-Agent Controller for Shared Energy Storage System

Liu, R., & Chen, Y. (2023) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm52003.2023.10252866 · Cited by 2