Maximum entropy correlated equilibria

Luis E. Ortiz, Robert E. Schapire, Sham M. Kakade

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations


We study maximum entropy correlated equilibria (Maxent CE) in multi-player games. After motivating and deriving some interesting important properties of Maxent CE, we provide two gradient-based algorithms that are guaranteed to converge to it. The proposed algorithms have strong connections to algorithms for statistical estimation (e.g., iterative scaling), and permit a distributed learning-dynamics interpretation. We also briefly discuss possible connections of this work, and more generally of the Maximum Entropy Principle in statistics, to the work on learning in games and the problem of equilibrium selection.

Original languageEnglish (US)
Pages (from-to)347-354
Number of pages8
JournalJournal of Machine Learning Research
StatePublished - 2007
Externally publishedYes
Event11th International Conference on Artificial Intelligence and Statistics, AISTATS 2007 - San Juan, Puerto Rico
Duration: Mar 21 2007Mar 24 2007

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Statistics and Probability


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