arXiv Machine Learning

Power Grid Control with Graph-Based Distributed Reinforcement Learning

arXiv Machine Learning

Sep 3

A Comparative Study of Graph Representations for GNN-Based Power Grid Control in L2RPN

The paper investigates how different graph representations affect graph neural network (GNN) performance in power grid control tasks within the L2RPN environment. It compares physical topology, electrical-sensitivity, and hybrid graph variants in a controlled experimental setting. The results show that aligning graph complexity with task granularity yields better outcomes than simply increasing representational richness.

By Adrian Degenkolb, Qiong Huang, Benjamin Sch\"afer

reinforcement-learning

arXiv Machine Learning

Aug 17

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 9

Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks

arXiv:2606. 07583v1 Announce Type: cross Abstract: Self-healing smart grids can quickly adjust their network configuration during outages to minimize power disruptions.

By Lihui Liu, Mucun Sun, Caisheng Wang

reinforcement-learning

arXiv AI

Oct 27

Physics-Informed Graph-Based Safe RL with Constraint-Specific Critics for Community Battery Coordination in Distribution Network

arXiv:2610.33654v1 Announce Type: new Abstract: High penetrations of distributed energy resources (DERs) and fast-varying electric vehicle (EV) charging intensify voltage and line-limit violations in...

By Chenghao Huang, Yue Yang, Hao Wang

reinforcement-learning

arXiv AI

Jul 7

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.

By Hyunsoo Lee, Panggah Prabawa, Dae-Hyun Choi, Joongheon Kim

llms agents reinforcement-learning

arXiv Machine Learning

Jun 15

Temporally Consistent Graph Q-Networks for Intelligent Network Control

arXiv:2606. 13848v1 Announce Type: cross Abstract: Mobile networks continue to grow in complexity and next generation networks are expected to support both increasing traffic loads and more diverse services.

By Zacharias Veiksaar, Maxime Bouton

agents reinforcement-learning 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 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 Machine Learning

Sep 25

Graph-Based Inference and Topology-Aware Multi-Agent Reinforcement Learning for Large-Scale Railway Network Management

The paper presents a graph-based framework for large-scale railway network management, combining a hierarchical Bayesian model with a Gaussian Process on a graph kernel to infer spatially correlated maintenance environments from Swiss Federal Railways data. It introduces a topology-aware Multi-Agent Reinforcement Learning system that uses graph neural networks and Transformers to optimize network-level policies. The approach demonstrates scalability via zero-shot transfer learning, enabling agents trained on small network segments to perform effectively on unseen large networks, outperforming heuristics and standard MARL baselines while reducing training time.

By Giacomo Arcieri, Gregory Duth\'e, Christophe Muller, Konstantinos G. Papakonstantinou, Daniel Straub, Eleni Chatzi

llms agents reinforcement-learning fine-tuning

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

agents reinforcement-learning

arXiv Machine Learning

Jul 7

Transformer-Based Multi-Agent Reinforcement Learning for Networked Systems with Long-Range Interactions

arXiv:2511. 13103v2 Announce Type: replace Abstract: Multi-agent reinforcement learning (MARL) has shown promise for large-scale network control, yet existing methods face two major limitations.

By Vidur Sinha, Muhammed Ustaomeroglu, Guannan Qu

llms agents reinforcement-learning

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

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