A computationally efficient implementation of fictitious play in a distributed setting

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

    Abstract

    The paper deals with distributed learning of Nash equilibria in games with a large number of players. The classical fictitious play (FP) algorithm is impractical in large games due to demanding communication requirements and high computational complexity. A variant of FP is presented that aims to mitigate both issues. Complexity is mitigated by use of a computationally efficient Monte-Carlo based best response rule. Demanding communication problems are mitigated by implementing the algorithm in a network-based distributed setting, in which player-to-player communication is restricted to local subsets of neighboring players as determined by a (possibly sparse, but connected) preassigned communication graph. Results are demonstrated via a simulation example.

    Original languageEnglish (US)
    Title of host publication2015 23rd European Signal Processing Conference, EUSIPCO 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1043-1047
    Number of pages5
    ISBN (Electronic)9780992862633
    DOIs
    StatePublished - Dec 22 2015
    Event23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
    Duration: Aug 31 2015Sep 4 2015

    Publication series

    Name2015 23rd European Signal Processing Conference, EUSIPCO 2015

    Conference

    Conference23rd European Signal Processing Conference, EUSIPCO 2015
    Country/TerritoryFrance
    CityNice
    Period8/31/159/4/15

    All Science Journal Classification (ASJC) codes

    • Media Technology
    • Computer Vision and Pattern Recognition
    • Signal Processing

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