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    block this user Tom Schaul

    Post Doctorate

    Courant Institute, New York University

    A scalable neural network architecture for board games

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    This paper proposes to use multi-dimensional recurrent neural networks (MDRNNs) as a way to overcome one of the key problems in flexible-size board games: scalability. We show why this architecture is well suited to the domain and how it can be successfully trained to play those games, even without any domain-specific knowledge. We find that performance on small boards correlates well with performance on large ones, and that this property holds for networks trained by either evolution or coevolution.

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    Description

    Title : A scalable neural network architecture for board games
    Author(s) : Tom Schaul, Jurgen Schmidhuber
    Abstract : This paper proposes to use multi-dimensional recurrent neural networks (MDRNNs) as a way to overcome one of the key problems in flexible-size board games: scalability. We show why this architecture is well suited to the domain and how it can be successfully trained to play those games, even without any domain-specific knowledge. We find that performance on small boards correlates well with performance on large ones, and that this property holds for networks trained by either evolution or coevolution.
    Subject : unspecified
    Area : Other
    Language : English
    Year : 2008

    Affiliations Courant Institute, New York University
    Journal : 2008 IEEE Symposium On Computational Intelligence and Games
    Publisher : IEEE
    Pages : 357 - 364
    Url : http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5035662
    Isbn : 978-1-4244-2973-8
    Doi : 10.1109/CIG.2008.5035662

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