Network Learning from Best-Response Dynamics in LQ Games

Yijun Chen1; Kemi Ding2; Guodong Shi1

10.23919/ACC55779.2023.10156151

2023-05-31

2023 2ND CONFERENCE ON FULLY ACTUATED SYSTEM THEORY AND APPLICATIONS, CFASTA

0743-1619

2378-5861

978-1-6654-6952-4

2023 American Control Conference (ACC)

2023-May

1680-1685

31 May-2 June 2023

San Diego, CA, USA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

In this paper, we focus on network structure inference problem for linear-quadratic (LQ) games from best-response dynamics. An adversary is considered to have no knowledge of the game network structure but have the ability to observe all players' best-response actions and manipulate some players' actions. This work presents a comprehensive framework for network learning from best-response dynamics in LQ games. First of all, we establish theoretic results that characterize network structure identifiability and provide numerical examples to demonstrate the usefulness of our theoretic results. Next, in the face of the inherent stability and sparsity constraints for the game network structure, we propose an information-theoretic stable and sparse system identification algorithm for learning the network structure. Finally, the effectiveness of the proposed learning algorithm is tested. The connection between network structure inference problem and classical system identification theory is covered by our work, which advances the literature.

Knowledge Engineering Heuristic Algorithms Games Inference Algorithms System Identification Trajectory Numerical Stability

通讯

英语

CPCI-S ; EI

Science, Technology and Innovation Commission of ShenzhenMunicipality[ZDSYS20200811143601004]

Automation & Control Systems ; Computer Science ; Engineering

Automation & Control Systems ; Computer Science, Interdisciplinary Applications ; Engineering, Electrical & Electronic

WOS:001027160301083

20233314564676

Dynamics ; Information Theory ; Religious Buildings

Public Buildings:402.2 ; Information Theory And Signal Processing:716.1 ; Machine Learning:723.4.2

IEEE

1.Australian Centre for Field Robotics, the University of Sydney, Sydney, Australia
2.School of System Design and Intelligent Manufacturing, Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems and the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities, Southern University of Science and Technology, Shenzhen, China

Yijun Chen,Kemi Ding,Guodong Shi. Network Learning from Best-Response Dynamics in LQ Games[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2023:1680-1685.