Who to Blame? learning and control strategies with information asymmetry

Changliu Liu, Wenlong Zhang, Masayoshi Tomizuka

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

    3 Scopus citations

    Abstract

    The rise of robot-robot interactions (RRI) is pushing for novel controller design techniques. Instead of using fixed control laws, robots should choose actions to minimize some cost functions specified by the designer. However, since the cost function of one robot may not be known to other robots (information asymmetry), special reasoning strategies are needed for multiple robots to learn to cooperate. Analysis shows that conventional learning and control strategies can lead to instability in a multi-agent system since the imperfection of other agents is not considered. In this paper, a new learning and control strategy that deals with interactions among imperfect agents is proposed. Analysis and simulation results show that the proposed strategy improves the performance of the system.

    Original languageEnglish (US)
    Title of host publication2016 American Control Conference, ACC 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4859-4864
    Number of pages6
    Volume2016-July
    ISBN (Electronic)9781467386821
    DOIs
    StatePublished - Jul 28 2016
    Event2016 American Control Conference, ACC 2016 - Boston, United States
    Duration: Jul 6 2016Jul 8 2016

    Other

    Other2016 American Control Conference, ACC 2016
    CountryUnited States
    CityBoston
    Period7/6/167/8/16

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

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  • Cite this

    Liu, C., Zhang, W., & Tomizuka, M. (2016). Who to Blame? learning and control strategies with information asymmetry. In 2016 American Control Conference, ACC 2016 (Vol. 2016-July, pp. 4859-4864). [7526122] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7526122