@inproceedings{04c1b4ab3fbd4a7e814c8835db07279d,

title = "No-Regret Distributed Learning in Two-Network Zero-Sum Games",

abstract = "We consider a distributed learning problem in a two-network zero-sum game with finite action sets, where the agents within each network is connected through time-varying directed graphs and the agents from distinct networks are connected by time-varying bipartite graphs. Each agent in a network has its own cost function and can receive information from its neighbors, while the networks have no global decision-making capability. We propose a distributed multiplicative weights algorithm to compute a mixed-strategy Nash equilibrium. We first establish a sublinear regret bound on the sequence of iterates for each agent. Additionally, we study the time-averaged sequence of iterates and prove its convergence to the set of mixed-strategy Nash equilibria with suitably selected diminishing step-sizes. ",

author = "Shijie Huang and Jinlong Lei and Yiguang Hong and Shanbhag, {Uday V.}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",

year = "2021",

doi = "10.1109/CDC45484.2021.9683186",

language = "English (US)",

series = "Proceedings of the IEEE Conference on Decision and Control",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "924--929",

booktitle = "60th IEEE Conference on Decision and Control, CDC 2021",

address = "United States",

}