arXiv AI July 7, 2026 By Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi, Joongheon Kim

Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems

arXiv:2607. 03324v1 Announce Type: cross Abstract: Eco-friendly energy management for artificial intelligence data centers (AIDCs) is crucial because of the significant increase in energy consumption-induced carbon emissions from AIDCs resulting from the rapid expansion of AI applications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 8

Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

arXiv:2607. 06489v1 Announce Type: new Abstract: The dairy industry in Ireland has a large potential for the integration of renewable energy and the reduction of carbon emissions.

By Marcos Eduardo Cruz Victorio, Karl Mason

agents reinforcement-learning

arXiv AI

Jun 16

Green AI Carbon Optimizer: Carbon-Efficient Training Location Recommendation and Global AI Energy Demand Forecasting

arXiv:2606. 14707v1 Announce Type: cross Abstract: AI training and deployment consume substantial electricity, but carbon outcomes remain weakly integrated into routine model development decisions.

By Yuxin Chen (University of Helsinki, Finland), Hao Gao (Independent Researcher), Chujie Zou (University of Helsinki, Finland)

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arXiv Machine Learning

Jun 25

Supervised Reinforcement Learning for the Coordination of Distributed Energy Resources

arXiv:2606. 24947v1 Announce Type: new Abstract: The increasing integration of distributed energy resources (DERs) is crucial for power system decarbonization, yet unlocking DERs' flexibility is challenged by their inherent uncertainties and modelling complexity.

By Haoyuan Deng, Yihong Zhou, Thomas Morstyn, Yi Wang

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 1

Smart charging of large fleets of Electric Vehicles: Independent Multi-Agent Reinforcement Learning approaches

arXiv:2606. 31347v1 Announce Type: new Abstract: The electrification of transportation through electric vehicles introduces new challenges for power grid management, such as increased peak demand, voltage fluctuations, line overloads, and the integration of variable renewable energy sources.

By Xavier Rate, Eloann Le Guern, Rapha\"el F\'eraud, Fatma Salem, Melissa Chiknoun, Eymeric Giabicani, Mehdi Feki, Patrick Maill\'e, Guy Camilleri, Anne Blavette, Hamid Benhamed

agents reinforcement-learning

arXiv Machine Learning

2d ago

Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control

arXiv:2608. 14114v1 Announce Type: new Abstract: As the integration of volatile renewable energy sources increases the strain on modern power grids, the use of Reinforcement Learning (RL) for autonomous topological reconfiguration has emerged as a promising research field to keep strained grids stable and operational.

By Lukas Zetto, Benjamin Sch\"afer, Qiong Huang

agents reinforcement-learning

arXiv AI

Jun 2

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

arXiv:2606. 02049v1 Announce Type: new Abstract: The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems.

By Hallah Shahid Butt, Qiong Huang, G\"okhan Demirel, Kevin F\"orderer, Erfan Tajalli-Ardekani, Simnon Waczowicz, Luigi Spatafora, Veit Hagenmeyer, Benjamin Sch\"afer

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Multi-Agent Deep Reinforcement Learning for Multi Objective Battery Management in Dairy Farms

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Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings