Learning to Play Efficient Coarse Correlated Equilibria

Holly P. Borowski, Jason R. Marden, Jeff S. Shamma

Research output: Contribution to journalArticlepeer-review

Abstract

The majority of the distributed learning literature focuses on convergence to Nash equilibria. Coarse correlated equilibria, on the other hand, can often characterize more efficient collective behavior than even the best Nash equilibrium. However, there are no existing distributed learning algorithms that converge to specific coarse correlated equilibria. In this paper, we provide one such algorithm, which guarantees that the agents’ collective joint strategy will constitute an efficient coarse correlated equilibrium with high probability. The key to attaining efficient correlated behavior through distributed learning involves incorporating a common random signal into the learning environment.

Original languageEnglish (US)
Pages (from-to)24-46
Number of pages23
JournalDynamic Games and Applications
Volume9
Issue number1
DOIs
StatePublished - Mar 15 2019
Externally publishedYes

Keywords

  • Distributed control
  • Game theory
  • Multiagent systems
  • Networked control

ASJC Scopus subject areas

  • Statistics and Probability
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Applied Mathematics

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