Obstacle tower: A generalization challenge in vision, control, and planning

Arthur Juliani, Ahmed Khalifa, Vincent Pierre Berges, Jonathan Harper, Ervin Teng, Hunter Henry, Adam Crespi, Julian Togelius, Danny Lange

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

    Abstract

    The rapid pace of recent research in AI has been driven in part by the presence of fast and challenging simulation environments. These environments often take the form of games; with tasks ranging from simple board games, to competitive video games. We propose a new benchmark - Obstacle Tower: a high fidelity, 3D, 3rd person, procedurally generated environment 1. An agent playing Obstacle Tower must learn to solve both low-level control and high-level planning problems in tandem while learning from pixels and a sparse reward signal. Unlike other benchmarks such as the Arcade Learning Environment, evaluation of agent performance in Obstacle Tower is based on an agent's ability to perform well on unseen instances of the environment. In this paper we outline the environment and provide a set of baseline results produced by current state-of-the-art Deep RL methods as well as human players. These algorithms fail to produce agents capable of performing near human level.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    EditorsSarit Kraus
    PublisherInternational Joint Conferences on Artificial Intelligence
    Pages2684-2691
    Number of pages8
    ISBN (Electronic)9780999241141
    DOIs
    StatePublished - 2019
    Event28th International Joint Conference on Artificial Intelligence, IJCAI 2019 - Macao, China
    Duration: Aug 10 2019Aug 16 2019

    Publication series

    NameIJCAI International Joint Conference on Artificial Intelligence
    Volume2019-August
    ISSN (Print)1045-0823

    Conference

    Conference28th International Joint Conference on Artificial Intelligence, IJCAI 2019
    CountryChina
    CityMacao
    Period8/10/198/16/19

    ASJC Scopus subject areas

    • Artificial Intelligence

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  • Cite this

    Juliani, A., Khalifa, A., Berges, V. P., Harper, J., Teng, E., Henry, H., Crespi, A., Togelius, J., & Lange, D. (2019). Obstacle tower: A generalization challenge in vision, control, and planning. In S. Kraus (Ed.), Proceedings of the 28th International Joint Conference on Artificial Intelligence, IJCAI 2019 (pp. 2684-2691). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2019-August). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/373