Monte mario: Platforming with MCTS

Emil Juul Jacobsen, Rasmus Greve, Julian Togelius

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

    Abstract

    Monte Carlo Tree Search (MCTS) is applied to control the player character in a clone of the popular platform game Su- per Mario Bros. Standard MCTS is applied through search in state space with the goal of moving the furthest to the right as quickly as possible. Despite parameter tuning, only moderate success is reached. Several modifications to the algorithm are then introduced specifically to deal with the behavioural pathologies that were observed. Two of the modifications are to our best knowledge novel. A combination of these modifications is found to lead to almost perfect play on linear levels. Furthermore, when adding noise to the benchmark, MCTS outperforms the best known algorithm for these levels. The analysis and algorithmic innovations in this paper are likely to be useful when applying MCTS to other video games.

    Original languageEnglish (US)
    Title of host publicationGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference
    PublisherAssociation for Computing Machinery
    Pages293-300
    Number of pages8
    ISBN (Print)9781450326629
    DOIs
    StatePublished - 2014
    Event16th Genetic and Evolutionary Computation Conference, GECCO 2014 - Vancouver, BC, Canada
    Duration: Jul 12 2014Jul 16 2014

    Publication series

    NameGECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference

    Other

    Other16th Genetic and Evolutionary Computation Conference, GECCO 2014
    CountryCanada
    CityVancouver, BC
    Period7/12/147/16/14

    ASJC Scopus subject areas

    • Computational Theory and Mathematics
    • Applied Mathematics

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  • Cite this

    Jacobsen, E. J., Greve, R., & Togelius, J. (2014). Monte mario: Platforming with MCTS. In GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference (pp. 293-300). (GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference). Association for Computing Machinery. https://doi.org/10.1145/2576768.2598392