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

François-Lavet, V., Henderson, P., Islam, R., Bellemare, M. G., & Pineau, J. (2018) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000071 · Cited by 1,512

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

Chu, T., Wang, J., Codeca, L., & Li, Z. (2020) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2901791 · Cited by 1,046

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

Buşoniu, L., Babuška, R., & Schutter, B. D. (2010) · Studies in computational intelligence · doi:10.1007/978-3-642-14435-6_7 · Cited by 772

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

AttentionKAN-Based Multi-Agent Reinforcement Learning for Coordinated Battery Energy Storage Control in Residential Demand Response

Sajid, S., Li, B., Qi, B., Berehman, B., Liang, F., Lei, Y., & Muqtadir, A. (2026) · Sustainability · doi:10.3390/su18094536

A novel adaptive equivalent consumption minimization strategy-based energy management strategy for plug-in hybrid electric vehicles using dynamic traffic information

Zhou, G., & Gao, R. (2026) · Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering · doi:10.1177/09544070261451967

A Comprehensive Review of AI-Based Co-Optimization of Smart Energy Grids, 5G Virtualization, Edge Analytics, and Military-Resilient Critical Infrastructure: A Multi-Domain Review

Gazis, A., Pappas, S. S., Vavouras, T., Kiokes, G., & Vita, V. (2026) · Electronics · doi:10.3390/electronics15112411

Enhancing the resilience of building multi-energy systems using deep reinforcement learning: A comprehensive survey

Lei, Y., Xie, H., Zhao, J., Liu, S., Feng, D., Hengyi, X., Xie, L., Song, X., & Zhang, H. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128292

Adaptive look-ahead economic dispatch with reinforcement learning for virtual power plant considering uncertainty

Bo, Y., Yuan, M., Zhu, C., Xia, Y., & Wei, W. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.128486

Robust dynamic pricing for electricity trading under renewable uncertainty: A belief-augmented dual-timescale Bayesian Stackelberg–Markov game

Xu, S., Li, B., Wang, L., Mihaljević, M. J., Zhang, S., Shao, W., & Wang, Q. (2026) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2026.102450

AI Tools for Energy Management: Revolutionising Smart Grids, Microgrids, Homes and Renewables

Ali, S. M., Khan, B., Ullah, K., Ullah, Z., Mehmood, C. A., Gulfam, S. M., & Gruosso, G. (2026) · International Journal of Energy Research · doi:10.1155/er/7310134

Markov Chain Stochastic Dual Dynamic Programming for Intraday Dispatch of Distribution Networks via Scenario-Lattice-Based Conditional Benders Cut

Huang, Z., Xiao, W., Yu, T., Pan, Z., Wu, Y., Chen, J., & Liu, Y. (2026) · Applied Sciences · doi:10.3390/app16157679

The Deployment Feasibility Framework for Peer‐to‐Peer Community‐Level Green Energy Markets: A Comprehensive Analysis of Technical, Policy, and Social Dimensions

Nemakonde, P., Ratshitanga, M., & Nemangwele, F. (2026) · IET Blockchain · doi:10.1049/blc2.70051

Nested Learning for Continual Autonomous Smart City Optimization

Aguilar-Ortíz, J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9200-4.ch008

Adaptive Energy Management in Manufacturing: A Multi-Agent Reinforcement Learning Framework for Hydrogen-Based Decentralized Energy Supply Systems

Wagner, M., Schmitz, M., Sousa, F. J. P., & Aurich, J. C. (2026) · Procedia CIRP · doi:10.1016/j.procir.2026.03.211

A novel multi-agent deep reinforcement learning framework based on scene clustering for dynamic scheduling of integrated energy systems

Li, C., Fang, C., Wang, K., Xu, J., Han, B., & Li, G. (2026) · Energy · doi:10.1016/j.energy.2026.142319

Physics-constrained multi-agent reinforcement learning within a cognitive digital twin for scalable and cyber-resilient distribution network reconfiguration

Salman, M. D., Yousif, M. M., & Khusheef, A. S. (2026) · Electrical Engineering · doi:10.1007/s00202-026-03749-0

GIS-AHP Renewable Energy Siting with Deep Reinforcement Learning and Digital Twin Battery for 24/7 Carbon-Free Dispatch in Sri Lanka

Senarathna, W. D. J. I., & Dilpriya, T. A. H. (2026) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon71835.2026.11691570

AI-Assisted Multi-Agent Reinforcement Learning for Source-Grid-Load-Storage Coordinated Dispatch in New Power Systems

Gao, J., Zhang, J., Liu, M., Wei, J., & Chen, M. (2026) · International Conference on Artificial Intelligence, Internet of Things and Cloud Computing Technology (AIoTC) · doi:10.1109/aiotc71250.2026.11689379

Cost-effective tariff-aware HVAC coordination in Saudi residential buildings using physics-informed deep reinforcement learning

Faraj, H. (2026) · Journal of King Saud University - Engineering Sciences · doi:10.1007/s44444-026-00131-0

Reinforcement learning-based adaptive coordination of solar energy for load, battery, electric vehicle, and hydrogen storage

Rajendiran, P., & Vijayakumar, K. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.124744

Risk-Constrained Optimal Energy Management of Multi-Carrier Microgrids Under Uncertainty

Akpan, J. J., Akhikpemelo, A., Afia, E. R., & Abia, D. E. D. (2026) · International Journal of Innovative Science and Research Technology (IJISRT) · doi:10.38124/ijisrt/26sep1309

Causality-constrained hierarchical LLM agents for online rule adaptation in building HVAC control

Xin, W., Liang, W., & Chong, A. (2026) · Applied Energy · doi:10.1016/j.apenergy.2026.129012

Intelligent Building Energy Control Systems: A Review of Multi-Agent Reinforcement Learning Technologies, Challenges, and Future Directions

Kapparova, A., Galland, S., Lombard, A., Nurgaliyev, M., Dosymbetova, G., Kaniyev, Y., Saymbetov, A., Bolatbek, A., Orynbassar, S., & Жоламанов, Б. (2026) · Smart Cities · doi:10.3390/smartcities9100177

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