arXiv Machine Learning

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.

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 AI

Jun 2

Network Distributed Multi-Agent Reinforcement Learning for Consensus Control of Quadcopters

arXiv:2606. 02107v1 Announce Type: cross Abstract: This paper proposes a Network Distributed Multi-Agent Reinforcement Learning (ND-MARL) framework for quadcopter consensus control.

By Youssef Mahran, Zeyad Gamal, Aamir Ahmad, Ayman El-Badawy

agents reinforcement-learning fine-tuning

arXiv Machine Learning

Jul 22

Decentralized Multi-agent Reinforcement Learning for Resilient Critical Infrastructures

arXiv:2607. 18359v1 Announce Type: cross Abstract: Critical infrastructures are increasingly distributed, interdependent, and exposed to evolving disruptions, making resilience a central requirement for their operation and control.

By Minghui Ding, Evangelos Pournaras

agents reinforcement-learning safety

arXiv AI

Jul 7

MAD-PINN: A Decentralized Physics-Informed Machine Learning Framework for Safe and Optimal Multi-Agent Control

arXiv:2509. 23960v2 Announce Type: replace-cross Abstract: Co-optimizing safety and performance in large-scale multi-agent systems remains a fundamental challenge.

By Manan Tayal, Aditya Singh, Shishir Kolathaya, Somil Bansal

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 2

Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms

arXiv:2407. 15283v2 Announce Type: replace-cross Abstract: Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults.

By Sheila Schoepp, Mehran Taghian, Shotaro Miwa, Yoshihiro Mitsuka, Shadan Golestan, Osmar Za\"iane

agents reinforcement-learning

arXiv Machine Learning

Jun 30

Toward an Energy-Optimized Operation of Data Centers Located in Wind Farms Using Reinforcement Learning

arXiv:2606. 30316v1 Announce Type: new Abstract: This paper studies Reinforcement Learning as an online controller for curtailment-aware workload shifting in wind-turbine-integrated high-performance computing (HPC) data centers.

By Jan Stenner, Alexander Kilian, Sebastian Peitz, Hermann de Meer

reinforcement-learning benchmarks

arXiv Machine Learning

Jun 18

Model-Free Reinforcement Learning Control for Resilient Cyber-Physical Systems

arXiv:2606. 19069v1 Announce Type: cross Abstract: This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks.

By Hugo O. Garc\'es, Alejandro J. Rojas, Bernardo A. Hern\'andez, Andr\'es Escalona, Jonathan M. Palma, Md. Rezwan Parvez, Bhushan Gopaluni, Sirish L. Shah

reinforcement-learning

arXiv AI

1d ago

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 AI

Jul 20

Robustness of Reinforcement Learning-Based Congestion Management in Low-Voltage Grids

arXiv:2607. 16004v1 Announce Type: cross Abstract: Increases in photovoltaic generation, charging of electric vehicles and heat-pump demand challenge operating limits in low-voltage distribution grids.

By Josef Hoppe, Sarra Bouchkati, Farah Nasr, Jonathan Krapp, Alexander Och, Maximilian Wirth, Jan Schiefelbein-Lach, Oliver Pohl, Andreas Ulbig, Michael T. Schaub

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 Machine Learning

Aug 5

FedCritic-MIMO: Communication-Efficient Serverless Federated Critic Learning for Massive-MIMO Resource Control in Open and Disaggregated 6G RANs

arXiv:2608. 03852v1 Announce Type: new Abstract: This paper proposes FedCritic-MIMO, a communication-efficient serverless federated multi-agent reinforcement learning framework for AI-native resource control across independently deployable cell-level controllers in open and disaggregated 6G RANs.

By Amin Farajzadeh, Melike Erol-Kantarci

agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Aug 5

Operationally Feasible Synthetic Power-Grid Scenarios via Learning the AC-Operable Joint Distribution

arXiv:2608. 03878v1 Announce Type: new Abstract: Synthetic power-grid scenarios are essential for planning, resilience assessment, contingency analysis, and data-driven power-system applications.

By Chenhan Xiao, Xinyu He, Haoran Li, Hanghang Tong, Yang Weng

diffusion benchmarks

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