Multi-agent hierarchical reinforcement learning for energy management

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A Multi-Agent Reinforcement Learning-Based Data-Driven Method for Home Energy Management

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

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Reinforcement Learning Approach for Optimal Distributed Energy Management in a Microgrid

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

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Multiagent Reinforcement Learning for Energy Management in Residential Buildings

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

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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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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

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

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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

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

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

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MARLISA

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