Multi-agent hierarchical reinforcement learning for energy management

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

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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

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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

Adaptive hierarchical energy management strategy for fuel cell/battery hybrid electric UAVs

Tian, W., Liu, L., Zhang, X., Shao, J., & Ge, J. (2024) · Aerospace Science and Technology · doi:10.1016/j.ast.2024.108938 · Cited by 51

Review of energy management technologies for unmanned aerial vehicles powered by hydrogen fuel cell

Tian, W., Zhang, X., Zhou, P., & Guo, R. (2025) · Energy · doi:10.1016/j.energy.2025.135751 · Cited by 51

Applications of Deep Reinforcement Learning for Home Energy Management Systems: A Review

Latoń, D., Grela, J., & Ożadowicz, A. (2024) · Energies · doi:10.3390/en17246420 · Cited by 44

Reinforcement learning for vehicle-to-grid: A review

Xie, H., Song, G., Shi, Z., Zhang, J., Lin, Z., Yu, Q., Fu, H., Song, X., & Zhang, H. (2025) · Advances in Applied Energy · doi:10.1016/j.adapen.2025.100214 · Cited by 53

A reinforcement learning approach using Markov decision processes for battery energy storage control within a smart contract framework

Jonban, M. S., Romeral, L., Marzband, M., & Abusorrah, A. M. (2024) · Journal of Energy Storage · doi:10.1016/j.est.2024.111342 · Cited by 40

Multi-Agent Reinforcement Learning for Smart Community Energy Management

Wilk, P., Wang, N., & Li, J. (2024) · Energies · doi:10.3390/en17205211 · Cited by 36

Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning

Wang, Q., Ti, Z., Yang, S., Yang, K., Wang, J., & Deng, X. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.124812 · Cited by 31

Community shared ES-PV system for managing electric vehicle loads via multi-agent reinforcement learning

Talihati, B., Fu, S., Zhang, B., Zhao, Y., Wang, Y., & Sun, Y. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.125039 · Cited by 32

Physics-model-free heat-electricity energy management of multiple microgrids based on surrogate model-enabled multi-agent deep reinforcement learning

Li, S., Hu, W., Cao, D., Chen, Z., Huang, Q., Blaabjerg, F., & Liao, K. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121359 · Cited by 29

Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage

Toquica, D., Agbossou, K., & Henao, N. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.01.413 · Cited by 29

Evolutionary Game Theory as a Catalyst in Smart Grids: From Theoretical Insights to Practical Strategies

Karaki, A., & Al‐Fagih, L. (2024) · IEEE Access · doi:10.1109/access.2024.3436935 · Cited by 26

Human-AI interaction in safety-critical network infrastructures

Mussi, M., Metelli, A. M., Restelli, M., Losapio, G., Bessa, R. J., Boos, D., Borst, C., Leto, G., Castagna, A., Chavarriaga, R., et al. (2025) · iScience · doi:10.1016/j.isci.2025.113400 · Cited by 33

Fast machine learning for building management systems

Mshragi, M., & Petri, I. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11226-6 · Cited by 25

Deep reinforcement learning-based multi-objective energy management system for microgrids under flexible energy market

Wu, J., Li, S., Yu, Y., Guan, Y., Wei, B., & Chen, Z. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127223 · Cited by 23

Stochastic Dynamic Power Dispatch With Human Knowledge Transfer Using Graph-GAN Assisted Inverse Reinforcement Learning

Chen, J., Yu, T., Pan, Z., Zhang, M., Lu, G., & Zhu, K. (2023) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2023.3329459 · Cited by 22

Regional-privacy-preserving operation of networked microgrids: Edge-cloud cooperative learning with differentiated policies

Xia, Q., Wang, Y., Zou, Y., Yan, Z., Zhou, N., Chi, Y., & Wang, Q. (2024) · Applied Energy · doi:10.1016/j.apenergy.2024.123611 · Cited by 22

Artificial intelligence and machine learning for distributed energy resource management systems: Applications, frameworks, and future directions

Satpathy, P. R., & Ramachandaramurthy, V. K. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.127109 · Cited by 21

Demand response model: A cooperative-competitive multi-agent reinforcement learning approach

Salazar, E. J., Rosero-Morillo, V. A., Gabrielski, J., & Samper, M. E. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108273 · Cited by 19

The role of advanced energy management strategies to operate flexibility sources in Renewable Energy Communities

Gallo, A., & Capozzoli, A. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.115043 · Cited by 17

Artificial intelligence strategies applied in general and automotive air conditioning control. A review of the last 20 years

Mendes, R. D. P., Pabón, J. J. G., Pottie, D. L. F., & Machado, L. H. J. (2024) · International Journal of Refrigeration · doi:10.1016/j.ijrefrig.2024.03.009 · Cited by 17

Explainable AI and Multi-Agent Systems for Energy Management in IoT-Edge Environments: A State of the Art Review

Álvarez-López, C., González-Briones, A., & Li, T. (2026) · Electronics · doi:10.3390/electronics15020385 · Cited by 23

Multi-Agent Hierarchical Deep Reinforcement Learning for HVAC Control With Flexible DERs

Pei, Y., Yao, Y., Zhao, J., Hao, J., Ding, F., & Wang, J. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3598082 · Cited by 14

How convenience and informational tools shape waste separation behavior: A social network approach

Cheng, X., Li, W., Yang, J., & Zhang, L. (2023) · Resources Policy · doi:10.1016/j.resourpol.2023.104152 · Cited by 14

A modified modeling approach of virtual power plant via improved federated learning

Taheri, S. I., Davoodi, M., & Ali, M. H. (2024) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2024.109905 · Cited by 15

Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Fraija, A., Henao, N., Agbossou, K., Kélouwani, S., & Fournier, M. (2024) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2024.101560 · Cited by 14

Decentralized multi-agent control of a three-tank hybrid system based on twin delayed deep deterministic policy gradient reinforcement learning algorithm

Rajasekhar, N., Radhakrishnan, T. K., & Samsudeen, N. (2023) · International Journal of Dynamics and Control · doi:10.1007/s40435-023-01227-0 · Cited by 14

Long-term efficient energy management for multi-station collaborative electric vehicle charging: A transformer-based multi-agent reinforcement learning approach

Song, G., Xie, H., Zhang, J., Fu, H., Shi, Z., Feng, D., Song, X., & Zhang, H. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126315 · Cited by 12

How to Design Reinforcement Learning Methods for the Edge: An Integrated Approach toward Intelligent Decision Making

Wu, G., Zhang, D., Miao, Z., Bao, W., & Cao, J. (2024) · Electronics · doi:10.3390/electronics13071281 · Cited by 13

Emerging Trends in Machine Learning Assisted Optimization Techniques Across Intelligent Transportation Systems

Afolayan, B. I., Ghosh, A., Calderín, J. F., & Masegosa, A. D. (2024) · IEEE Access · doi:10.1109/access.2024.3501775 · Cited by 13

Evaluating energy management investments for sustainable transition of smart cities through a new intuitionistic fuzzy consensus-based CoCoSo methodology

Ecer, F., Saha, A., Dınçer, H., & Yüksel, S. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.122655 · Cited by 12

A hierarchical energy management system for a cluster of buildings: Reinforcement learning and model predictive control (RL-MPC) approach

Hajialigol, P., Papadopoulos, P., Moazami, A., & Aghaei, M. (2025) · Energy and Buildings · doi:10.1016/j.enbuild.2025.116879 · Cited by 10

Optimizing Home Energy Flows and Battery Management with Supervised and Unsupervised Learning in Renewable Systems

Alfaverh, K., Fawaier, M., & Számel, L. (2025) · Electronics · doi:10.3390/electronics14061166 · Cited by 10

Optimization and efficiency Enhancement of multi-energy systems in integrated energy Parks: A comprehensive analysis of adaptive strategies and dynamic management

Zeng, X., & Yi, J. (2025) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2025.146204 · Cited by 10

Enhanced hierarchical reinforcement learning for co-optimization of HVAC system operations

Zhou, X., Li, J., Mo, H., Yan, J., Liang, L., & Pan, D. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.112663 · Cited by 9

Power Smoothing Control for Wind-Storage Integrated Systems With Hierarchical Safe Reinforcement Learning and Curriculum Learning

Wang, S., Zhao, H. Q., Shu, T., Pan, Z., Liang, G., & Zhao, J. (2025) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2025.3593340 · Cited by 9

Energy-efficient thermal management of air-liquid-cooled data centers via deep reinforcement learning

Wu, C., Huang, Z., Yu, T., Pan, Z., Wang, K., & Wu, Y. (2025) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2025.129626 · Cited by 9

Multi-agent generalized cooperative optimization scheduling for multi-energy complementarity in microgrids

Xu, N., Mu, C., Wang, K., Ma, L., & Liu, Z. (2026) · Artificial Intelligence Review · doi:10.1007/s10462-025-11354-z · Cited by 8

Real-time optimal energy management of microgrid based on multi-agent proximal policy optimization

Wang, D., Sun, Q., & Su, H. (2025) · Neural Computing and Applications · doi:10.1007/s00521-024-10654-9 · Cited by 7

How to support decision making with reinforcement learning in hierarchical chemical process control?

Szatmári, K., Chován, T., Németh, S., & Kummer, A. (2025) · Chemical Engineering Journal Advances · doi:10.1016/j.ceja.2025.100753 · Cited by 11

Exploration-guided adaptive learning from mixed-quality data for satellite multi-battery management

Zou, Y., Lin, Z., Zhang, Q., Liu, Z., & Xu, Z. (2026) · Journal of Energy Storage · doi:10.1016/j.est.2026.120546 · Cited by 7

Enhancing University Building Energy Flexibility Performance Using Reinforcement Learning Control

Friansa, K., Pradipta, J., Nanda, R. M., Haq, I. N., Mangkuto, R. A., Iskandar, R. F., Wasesa, M., & Leksono, E. (2024) · IEEE Access · doi:10.1109/access.2024.3512543 · Cited by 8

Multi-modal multi-task artificial intelligence model for active distribution network scheduling with multi-agent reinforcement learning

Ahshan, R., Abid, M. S., & Al-Abri, M. Z. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.112091 · Cited by 6

Multi-agent systems based on hierarchical voltage control in active low- and medium-voltage electrical distribution networks

Ardakan, A. G., Sedighi, A., & Mazidi, M. (2025) · Electric Power Systems Research · doi:10.1016/j.epsr.2025.112498 · Cited by 6

A model-free optimal operation strategy of diversified demands-park integrated energy system considering energy cascade utilization

Li, P., Jiang, L., Wang, J., Yao, L., Li, Y., & Wang, Z. (2023) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2023.109518 · Cited by 6

Enhanced TD3 multi-agent reinforcement learning approach for distributed energy resources coordination in active distribution network

Imtiaz, S., Yang, L., Naz, M. N., Munir, H. M., Altaf, M. W., & Mohammed, M. S. A. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.115348 · Cited by 6

Smart Home Energy Management using Reinforcement Learning

Reddy, V. S. S., Akshaya, C. S., Devi, V. S. K., & Raj, K. V. (2025) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici65217.2025.11252860 · Cited by 4

Hierarchical optimization of virtual power plants via sequential Game-Based Multi-Agent reinforcement learning

Peng, Z., & Heng, T. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.131263 · Cited by 4

A review of reinforcement learning: A tripartite framework of environment design, algorithmic innovation, and application scenarios

Liu, Y., Xiong, Z., Yang, L., & Shen, T. (2026) · Array · doi:10.1016/j.array.2026.100812 · Cited by 4

LLM-BEM: Zero-shot LLM-Agent Scheduling for Smart Building Energy Management

Liu, S., Hu, R., Yang, Y., Zhang, H., Ding, X., Sun, H., & Sun, L. (2025) · IEEE China Conference on System Simulation Technology and its Applications (CCSSTA) · doi:10.1109/ieeeconf65522.2025.11137227 · Cited by 3

Embodied Intelligence for Micro‐Integrated Energy Systems: Key Technologies and Applications

Liu, D., & Wang, Y. (2025) · IET Smart Energy Systems · doi:10.1049/ses2.70012 · Cited by 3

Coordinated Optimization of Distributed Energy Resources Based on Spatio-Temporal Transformer and Multi-Agent Reinforcement Learning

Zhao, J., Chen, N., Han, X., Li, Y., Zheng, S., & Zhou, S. (2025) · Processes · doi:10.3390/pr13103372 · Cited by 3

Energy production, storage and management for sustainable VTOL aircraft and advanced air mobility

Elmeligy, A., Safwat, N. E., Moursi, M. S. E., & Sabatini, R. (2026) · Energy Reports · doi:10.1016/j.egyr.2026.109137 · Cited by 3

From centralized monitoring to cooperative aerial intelligence: A MARL–FANET approach for smart microgrid monitoring and defect analysis

Raza, D. R., Ahmed, U. B., Muhammad, Y., Bilal, H., Aslam, M. S., Alqurashi, A., Alrajhi, H. K., & Gadekallu, T. R. (2026) · Electric Power Systems Research · doi:10.1016/j.epsr.2026.113923 · Cited by 3

Energy Regulation-Aware Layered Control Architecture for Building Energy Systems Using Constraint-Aware Deep Reinforcement Learning and Virtual Energy Storage Modeling

Li, S., Tian, C., & Abdalla, A. N. (2025) · Energies · doi:10.3390/en18174698 · Cited by 3

Strategic coordination of drones via short-term distributed optimization and long-term reinforcement learning

Qin, C., & Pournaras, E. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114270 · Cited by 3

Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning

Zhou, L., Huo, L., Liu, L., Xu, H., Chen, R., & Chen, X. (2025) · Energies · doi:10.3390/en18071809 · Cited by 2

Networked Multi-Agent Deep Reinforcement Learning Framework for the Provision of Ancillary Services in Hybrid Power Plants

Ikram, M., Habibi, D., & Aziz, A. (2025) · Energies · doi:10.3390/en18102666 · Cited by 3

Virtual inertia regulation based high-order observer for weak microgrids using deep sequential learning

Li, S., Song, S., & Vu, M. T. (2025) · ISA Transactions · doi:10.1016/j.isatra.2025.10.005 · Cited by 2

Energy management strategy for fuel cell hybrid electric vehicles considering optimal overall cost of multi-energy systems: A heterogeneous independent multi-agent deep reinforcement learning approach

Hu, H., Hu, G., Hu, J., Zhou, H., & Chen, S. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.114517 · Cited by 2

Safety-Aware Multi-Agent Deep Reinforcement Learning for Adaptive Fault-Tolerant Control in Sensor-Lean Industrial Systems: Validation in Beverage CIP

González, A., Félix, R. A., Mena, L. J., Félix, V. G., Martínez-Peláez, R., Ostos, R., Velarde-Alvarado, P., & Ochoa-Brust, A. M. (2026) · Technologies · doi:10.3390/technologies14010044 · Cited by 2

Artificial Intelligence Framework for Simulated Multiagent Reinforcement Learning in Hybrid Microgrid Control

Rabbi, M. F. (2026) · Results in Engineering · doi:10.1016/j.rineng.2026.109744 · Cited by 4

Edge-Intelligent and Cyber-Resilient Coordination of Electric Vehicles and Distributed Energy Resources in Modern Distribution Grids

Ghofrani, M. (2026) · Energies · doi:10.3390/en19081867 · Cited by 2

Reinforcement Learning Approaches in Cyber Security

Safeer, E. (2024) · Advances in information security, privacy, and ethics book series · doi:10.4018/979-8-3693-5415-5.ch002 · Cited by 2

Characterisation and simulated evaluation of electricity management strategies for Prosumers, Prosumagers, and other end-users

Jiménez, V., González, J., Jímenez, M., Escudero-Atehortúa, A., & Nieto‐Londoño, C. (2024) · Technology Analysis and Strategic Management · doi:10.1080/09537325.2024.2405138 · Cited by 2

Multi-objective Reinforcement Learning for Energy-Efficient Industrial Control

Schäfer, G., Seliger, R., Rehrl, J., Huber, S., & Hirlaender, S. (2025) · Communications in computer and information science · doi:10.1007/978-3-032-02003-1_6 · Cited by 2

A two-level reinforcement learning based regulation strategy for dynamic operation of O2O service ecosystems

Wang, G., Liu, X., Shen, Y., Zhou, D., Zhang, M., & Xue, X. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129042 · Cited by 2

Event-driven-based adaptive control for thermostatically-controlled loads with online identification of thermal parameters

Yang, X., Qi, Q., Hu, X., Li, Z., Qi, B., Cao, X., & Shi, K. (2025) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2025.111145 · Cited by 1

STAGE: spatio-temporal attention graph-enhanced policy optimization for area defense in UAV swarm confrontations

Su, Y., Wu, Q., Jiang, L., Tong, T., & Tan, W. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-00536-6 · Cited by 1

Causality-Aware LLM-Enhanced Graph Representation Learning for Adaptive Power System Control

Yao, F., Liu, J., Tao, Y., Qiu, J., Iu, H. H.-C., Chen, G., & Dong, Z. Y. (2026) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2026.3651250 · Cited by 1

An energy trajectory-based reinforcement learning framework with meta-adaptation for robust energy management in solar-powered electricity–heat–hydrogen integrated energy system

Gao, X., Ren, Y., He, Y., Zhang, S., & Zhang, X. (2026) · Energy · doi:10.1016/j.energy.2026.140666 · Cited by 1

A Control-Centric Systematic Review of MARL for EV–Grid Coordination: From Predictive Input to Verifiable Feedback

Nazloo, H. T., & Musı́lek, P. (2026) · Electronics · doi:10.3390/electronics15091902 · Cited by 1

Multi-Agent Path Planning in Complex Multi-Obstacle Environment: A Reinforcement Learning-Based Formation Containment Method

Li, T., Shi, H., Zhu, P., Ma, Z., Zhou, Y. F., & Shi, L. (2026) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2026.3688839 · Cited by 1

Reinforcement Learning in Microgrid Energy Management: Review, Methods and Prospects

Zia, M. F., Inayat, U., Anjum, M. J., Khalid, H. M., & Liaquat, S. (2026) · IET Smart Grid · doi:10.1049/stg2.70097 · Cited by 1

Optimization of renewable-integrated energy systems via artificial intelligence: Review and perspective

Chen, S., Xiong, L., Chow, M., & Tang, Y. (2026) · Cyber-Physical Energy Systems · doi:10.1016/j.cpes.2026.04.003 · Cited by 1

Robust formation tracking of multi-agent systems via reinforcement learning-based hybrid impulsive control

Liang, Z., Gu, Y., Tao, Y., & Xu, R. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109442 · Cited by 1

Multi-timescale coordinated scheduling approach for integrated energy system based on hierarchical reinforcement learning

Liu, H., Wu, J., He, X., & Qi, Z. (2026) · Global Energy Interconnection · doi:10.1016/j.gloei.2026.07.009 · Cited by 1

Swarm‐intelligence‐based coordinated control of electric heatings for voltage stabilization with zero communication burden

Qi, Q., Guo, H., Yang, X., Zhang, D., Ai, X., Qi, B., Fu, Y., & Li, Y. (2024) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.13230 · Cited by 1

CPPER: A Controlled Partial Prioritized Experience Replay for Reinforcement Learning in its Multi- Agent extension

Sarkar, T. (2023) · IEEE International Conference on Contemporary Computing and Communications (InC4) · doi:10.1109/inc457730.2023.10263207

Intelligent secondary control for energy management in a micro-grid by using multi-agent system

Jonban, M. S. (2023) · doi:10.5821/dissertation-2117-402147

Deep Reinforcement Learning-Based Energy Management of Hydrogen-Driven ShipsPreprint

Zhao, Y., Wen, S., Zhao, Q., Zhang, B., Huang, Y., Lin, A., & Zhu, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4883827

A Two-Stage Strategy Integrating Gaussian Processes and TD3 for Leader–Follower Coordination in Multi-Agent Systems

Zhang, X., Jiang, B., Deng, F., & Zhao, M. (2025) · Journal of Sensor and Actuator Networks · doi:10.3390/jsan14030051

Robust Event-Triggered Security Control for Multi-Agent Systems Under Deception Attacks

Chen, J., Liu, D., Xing, Y., & Liu, Y. (2025) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc65474.2025.11090197

Collaborative Optimization of Electric Vehicle Charging in Hydrogen-Integrated Multi-Energy System: A Temporal-Aware Multi-Agent Reinforcement Learning ApproachPreprint

Song, G., Xie, H., Zhang, H., Zhang, J., Fu, H., Jiang, M., Zhang, L., Shi, Z., Feng, D., & Song, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5402315

Physics-aware feature learning for integrated energy systems management

Maedani, M., Foroud, A. A., & Izadfar, H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-32984-5.00001-5

A Hierarchical Energy Management System for a Cluster of Buildings: Reinforcement Learning and Model Predictive Control (RL-MPC) ApproachPreprint

Hajaligol, P., Papadopoulos, P., Moazami, A., & Aghaei, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5462362

Optimal Scheduling of Air Conditioning for Renewable Energy Accommodation

Zhang, X., Li, Z., Wang, C., Li, L., Zhang, J., & Zhou, H. (2025) · IET Generation Transmission & Distribution · doi:10.1049/gtd2.70159

A cascading policy learning framework for enhancing power grid resilience

Bensalah, A. S., Marir, T., Yaich, R., & Chebout, M. S. (2025) · PeerJ Computer Science · doi:10.7717/peerj-cs.3358

Enhanced Portfolio Management Using Hierarchical Reinforcement Learning: A Robust Framework for Real-Time Investment Strategies

Mohurley, A. I., patil, A. R. B., & Mangrulkar, N. S. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11294887

Multi-Agent Reinforcement Learning based Coordinated Power Management of Solar PV and Charging Stations in Active Distribution NetworkPreprint

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MARLISA

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