No-Regret Distributed Learning in Two-Network Zero-Sum Games

Shijie Huang, Jinlong Lei, Yiguang Hong, Uday V. Shanbhag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


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.

Original languageEnglish (US)
Title of host publication60th IEEE Conference on Decision and Control, CDC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9781665436595
StatePublished - 2021
Event60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States
Duration: Dec 13 2021Dec 17 2021

Publication series

NameProceedings of the IEEE Conference on Decision and Control
ISSN (Print)0743-1546
ISSN (Electronic)2576-2370


Conference60th IEEE Conference on Decision and Control, CDC 2021
Country/TerritoryUnited States

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modeling and Simulation
  • Control and Optimization


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