arXiv Machine Learning

LOOP-MAC: Learning-Based Multi-Agent Coordination with Guaranteed Local Feasibility in a Virtual Power Plant

LOOP-MAC is a decentralized learning-based framework for coordinating distributed energy resources in a virtual power plant. It guarantees local feasibility by construction and reduces the aggregate commitment gap through a two-stage decision process: learned coordination with neural optimizers and a local adjustment step that rebalances schedules without extra communication. In a 100-agent test, five communication rounds plus adjustment lowered the commitment gap from 5.1% to 0.75% and the optimality gap from 3.93% to 3.07%, with per-agent computation around 1.1 ms.

arXiv Machine Learning

Sep 24

Optimization without Future Compromises? Decentralized Coordination via Collective and Reinforcement Learning

The paper introduces Hierarchical Reinforcement and Collective Learning (HRCL), a framework that combines multi‑agent reinforcement learning (MARL) with decentralized coordination. HRCL uses MARL at a high level to generate strategic guidance that limits the decision space for low‑level agents, enabling efficient short‑term coordination while considering long‑term effects. Experiments on synthetic, energy‑management, and drone‑swarm scenarios demonstrate faster convergence and significant reductions in system‑wide and individual costs compared to standalone MARL.

By Chuhao Qin, Evangelos Pournaras

agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 15

Constraint-Aware Aggregation for Federated Reinforcement Learning in Microgrid Energy Coordination

arXiv:2607. 12763v1 Announce Type: cross Abstract: Federated Reinforcement Learning (FedRL) enables coordination of distributed energy resources without sharing raw local data, but standard aggregation methods such as FedAvg do not account for system-level constraints, often leading to unsafe global behavior.

By Usman Haider, Karl Mason

reinforcement-learning benchmarks safety

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 Machine Learning

2d ago

Power Grid Control with Graph-Based Distributed Reinforcement Learning

arXiv:2509.02861v2 Announce Type: replace Abstract: The necessary integration of renewable energy sources, combined with the expanding scale of power networks, presents significant challenges in cont...

By Carlo Fabrizio, Gianvito Losapio, Marco Mussi, Alberto Maria Metelli, Marcello Restelli

agents reinforcement-learning

arXiv Machine Learning

Sep 15

A Game-Theoretic Framework for Incentive-Compatible AI training Under Renewable-Energy Constraints

arXiv:2609.15389v1 Announce Type: cross Abstract: As artificial intelligence systems increasingly rely on distributed and collaborative training, the energy footprint of these processes becomes a sha...

By Konstantinos Varsos, Ramin Khalili, Adamantia Stamou, George D. Stamoulis, Vasillios A. Siris

agents

arXiv AI

Jul 29

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.

By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm

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

Aug 25

Edge-AI-Driven Learning-to-Rank for Decentralized Task Allocation in Circular Smart Manufacturing

The paper presents an Edge-AI-driven decentralized task‑allocation framework for circular smart manufacturing. It combines a resource‑aware heuristic, a regression‑based Edge‑AI bid approximation, and a compact autoencoder‑regularized pairwise ranking model to evaluate tasks at the machine level. Simulation results show that the ranking method improves task completion, reduces tardiness and deadline misses, and lowers energy per completed task compared to the heuristic baseline.

By Mohammadhossein Ghahramani, Yan Qiao, Mengchu Zhou

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 Machine Learning

Jul 8

Federated Physics-Grounded Reinforcement Learning for Distributed Stability Control in Smart Grids

arXiv:2607. 05553v1 Announce Type: new Abstract: Transient stability control in smart grids requires rapid post-fault damping of generator frequency and rotor angle deviations to prevent cascading failures.

By Omar Al-Refai, Ibrahim Shahbaz, Adam Ali Husseinat, Eman Hammad

agents reinforcement-learning efficiency benchmarks

arXiv AI

Aug 18

LLM-Based Hierarchical Coordinated Control with Continuation-Aware Policy Learning

arXiv:2608. 15041v1 Announce Type: new Abstract: Coordinating multiple interacting units in complex engineering systems is challenging when system interactions are difficult to model, operational information is heterogeneous, and low-level actions must satisfy strict constraints.

By Changhong He, Jinda Gao, Xinkuan Liu, Le Zhang, Xizi Luo, Yu Mei

llms reinforcement-learning

arXiv Machine Learning

Sep 10

Emergent Charging Coordination in Electric Delivery Fleets

arXiv:2609.07689v1 Announce Type: new Abstract: In electric delivery fleets, mid-shift charging is non-trivial: each vehicle must decide when, where and how much to charge to finish on time with batt...

By Javier Vales-Alonso, Juan J. Alcaraz

agents reinforcement-learning safety

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