PCGRL: Procedural content generation via reinforcement learning

Ahmed Khalifa, Philip Bontrager, Sam Earle, Julian Togelius

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

    Abstract

    We investigate how reinforcement learning can be used to train level-designing agents. This represents a new approach to procedural content generation in games, where level design is framed as a game, and the content generator itself is learned. By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action so that the expected final level quality is maximized. This approach can be used when few or no examples exist to train from, and the trained generator is very fast. We investigate three different ways of transforming two-dimensional level design problems into Markov decision processes, and apply these to three game environments.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2020
    EditorsLevi Lelis, David Thue
    PublisherThe AAAI Press
    Pages95-101
    Number of pages7
    ISBN (Electronic)9781577358497
    StatePublished - 2020
    Event16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2020 - Virtual, Online
    Duration: Oct 19 2020Oct 23 2020

    Publication series

    NameProceedings of the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2020

    Conference

    Conference16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, AIIDE 2020
    CityVirtual, Online
    Period10/19/2010/23/20

    ASJC Scopus subject areas

    • Visual Arts and Performing Arts
    • Artificial Intelligence

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