Multi-agent hierarchical reinforcement learning for energy management

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Demand Response for Home Energy Management Using Reinforcement Learning and Artificial Neural Network

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Applications of reinforcement learning in energy systems

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Real-Time Energy Management of a Microgrid Using Deep Reinforcement Learning

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Reinforcement learning in sustainable energy and electric systems: a survey

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Reinforcement Learning Techniques for Optimal Power Control in Grid-Connected Microgrids: A Comprehensive Review

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Multiagent Reinforcement Learning for Energy Management in Residential Buildings

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Deep Reinforcement Learning Solutions for Energy Microgrids Management

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A Game Theory Approach to Multi-Agent Decentralized Energy Management of Autonomous Polygeneration Microgrids

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Efficient Hyperparameter Optimization in Deep Learning Using a Variable Length Genetic AlgorithmPreprint

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Multi-agent Deep Reinforcement Learning for Zero Energy Communities

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Multi-time scale energy management framework for smart PV systems mixing fast and slow dynamics

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A convex optimization based decentralized real-time energy management model with the optimal integration of microgrid in smart grid

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Why Does Hierarchy (Sometimes) Work So Well in Reinforcement Learning?Preprint

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Challenges of Reinforcement Learning

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Demand-Side Scheduling Based on Multi-Agent Deep Actor-Critic Learning for Smart Grids

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When Waiting Is Not an Option: Learning Options With a Deliberation Cost

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Control system in the smart grid: State of the art and opportunities

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Multi-Agent Reinforcement Learning for the Energy Optimization of Cyber-Physical Production Systems

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Stochastic Optimization for Distributed Energy Resources in Smart Grids

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Review of Multi-Agent Micro-Grid Systems

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Hierarchical Reinforcement Learning

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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 · see citing works

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

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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 · see citing works

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 · see citing works

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 · see citing works

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

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

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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 · see citing works

Reinforcement learning for vehicle-to-grid: A review

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

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Multi-Agent Reinforcement Learning for Smart Community Energy Management

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

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

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

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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. P., Blaabjerg, F., & Liao, K. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.121359 · Cited by 28 · see citing works

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

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

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Human-AI interaction in safety-critical network infrastructures

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Fast machine learning for building management systems

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

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

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

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

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

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

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

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

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

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

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

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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 · see citing works

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

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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 · see citing works

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

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

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

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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 · see citing works

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

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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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 7 · see citing works

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 · see citing works

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 · see citing works

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

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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 · see citing works

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 · see citing works

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 5 · see citing works

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 · see citing works

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 · see citing works

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

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

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

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

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

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

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

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Multi-agent temporal communication framework for scheduling in hydrogen-integrated energy systems

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Multi-Agent Reinforcement Learning for Decentralized Energy Management in Smart Communities

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Reinforcement Learning-Based Landing Impact Mitigation and Stabilization Control for Lunar Quadruped Robots Under Complex Operating Conditions

Li, J., Yuan, Y., Liu, Z. J., & Sun, S. (2026) · Machines · doi:10.3390/machines14040417

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

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Hierarchical Reinforcement Learning for Power Network Topology ControlPreprint

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

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MARLISA

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