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Learning Robust Communication by Adversarial Training in Networked System Control
Runji, Lin1,2; Haifeng, Zhang1,2
Source PublicationLecture Notes in Electrical Engineering
2024-06
PagesChapter 52 978-981-97-3335-4
Abstract

Effective communication is paramount to achieving efficient cooperation in networked system control (NSC). Nonetheless, real-world challenges such as node failures and channel noise impede the generalization capabilities of networked agents. In this study, we seek to enhance the robustness of networked control strategies by introducing RoComm. An adaptive adversary is incorporated during the training phase to emulate potential node failures. This adversary strategically targets and attacks vulnerable nodes in the system, prompting protagonist agents to adapt and become less susceptible to node failures over time. When both policies reach convergence, it indicates that the protagonist policy can sustain robust performance, even amidst the most detrimental noise. Empirical results from two distinct NSC scenarios—traffic signal control and adaptive cruise control—demonstrate that RoComm not only augments the resilience of networked control policies against node failures but also amplifies their generalization capacities across varying environmental conditions, subsequently elevating their overall performance.

KeywordNetworked System Control Robustness Communicative Multi-Agent Reinforcement Learning
Indexed ByEI
Language英语
IS Representative Paper
Sub direction classification决策智能理论与方法
planning direction of the national heavy laboratory认知决策知识体系
Paper associated data
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57354
Collection复杂系统认知与决策实验室_群体决策智能团队
Corresponding AuthorHaifeng, Zhang
Affiliation1.School of Artificial Intelligence, University of Chinese Academy of Sciences
2.Institute of Automation, Chinese Academy of Sciences
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Runji, Lin,Haifeng, Zhang. Learning Robust Communication by Adversarial Training in Networked System Control[J]. Lecture Notes in Electrical Engineering,2024:Chapter 52 978-981-97-3335-4.
APA Runji, Lin,&Haifeng, Zhang.(2024).Learning Robust Communication by Adversarial Training in Networked System Control.Lecture Notes in Electrical Engineering,Chapter 52 978-981-97-3335-4.
MLA Runji, Lin,et al."Learning Robust Communication by Adversarial Training in Networked System Control".Lecture Notes in Electrical Engineering (2024):Chapter 52 978-981-97-3335-4.
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