Point-to-point car racing: An initial study of evolution versus temporal difference learning

Simon M. Lucas, Julian Togelius

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

    Abstract

    This paper considers variations on an extremely simple form of car racing, the challenge being to visit as many way-points as possible in a fixed amount of time. The simplicity of the models enables a very thorough evaluation of various learning algorithms and control architectures, and enables other researchers to work on the same models with relative ease. The models are used to compare the performance of various hand-programmed controllers, and neural networks trained using evolution, and using temporal difference learning. Comparisons are also made between state-based and action-based controller architectures. The best controllers were obtained using evolution to learn the weights of state-evaluation neural networks, and these were greatly superior to human drivers.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007
    Pages260-267
    Number of pages8
    DOIs
    StatePublished - 2007
    Event2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007 - Honolulu, HI, United States
    Duration: Apr 1 2007Apr 5 2007

    Publication series

    NameProceedings of the 2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007

    Other

    Other2007 IEEE Symposium on Computational Intelligence and Games, CIG 2007
    CountryUnited States
    CityHonolulu, HI
    Period4/1/074/5/07

    Keywords

    • Car racing
    • Evolving neural networks
    • Reinforcement learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Electrical and Electronic Engineering
    • Computational Mathematics
    • Theoretical Computer Science

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