Journal's Facebook pageRobust QoS-Aware Network Scheduling for Smart Substations via Multi-Agent Adversarial Reinforcement Learning
- State Grid Suzhou Power Supply Company
Suzhou Jiangsu 215004, China
{hep sz, jds sz, qibz, yang yu, js.xue }@js.sgcc.com.cn
Abstract
With the rapid development of modern power systems, traditional scheduling and reinforcement learning methods often fail to meet stringent Quality of Service (QoS) demands for low latency, high reliability, and stable bandwidth under large-scale bursty traffic. To address this problem, we propose a QoS-driven routing optimization approach based on Adversarial Reinforcement Learning, referred to as Adversarial Critic-Cooperative Actor (ACCA). By introducing adversarial agents that model worst-case perturbations, ACCA establishes a multi-agent game framework that enhances policy robustness and adaptability in dynamic network environments. Furthermore, a multi-dimensional state representation and a QoS-aware cost function are designed to capture metrics such as delay, bandwidth utilization, queue length, and packet loss. Experiments demonstrate that ACCA outperforms traditional routing protocols and standard reinforcement learning algorithms in terms of end-to-end delay, load balancing, and throughput, thereby providing an effective solution for QoS assurance in intelligent power communication networks.
Key words
Bandwidth Management, Deep Reinforcement Learning, Adversarial Learning, Quality of Service
Digital Object Identifier (DOI)
https://doi.org/10.2298/CSIS251015009H
Publication information
Volume 23, Issue 1 (January 2026)
Year of Publication: 2026
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium
Full text
Available in PDF
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How to cite
He, P., Jing, D., Qi, B., Yang, Y., Xue, J.: Robust QoS-Aware Network Scheduling for Smart Substations via Multi-Agent Adversarial Reinforcement Learning. Computer Science and Information Systems, Vol. 23, No. 1, 299-320. (2026), https://doi.org/10.2298/CSIS251015009H
