Learning Controllable 3D Level Generators

Zehua Jiang, Sam Earle, Michael Green, Julian Togelius

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


    Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for large human-authored data-sets and allows agents to train explicitly on functional constraints, using computable, user-defined measures of quality instead of target output. We explore the application of PCGRL to 3D domains, in which content-generation tasks naturally have greater complexity and potential pertinence to real-world applications. Here, we introduce several PCGRL tasks for the 3D domain, Minecraft. These tasks will challenge RL-based generators using affordances often found in 3D environments, such as jumping, multiple dimensional movement, and gravity. We train agents to optimize each of these tasks to explore the capabilities of existing in PCGRL. The agents are able to generate relatively complex and diverse levels, and generalize to random initial states and control targets. Controllability tests in the presented tasks demonstrate their utility to analyze success and failure for 3D generators. We argue that these generators could serve both as co-creative tools for game designers, and as pre-trained environment generators in curriculum learning for player agents.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 17th International Conference on the Foundations of Digital Games, FDG 2022
    EditorsKostas Karpouzis, Stefano Gualeni, Allan Fowler
    PublisherAssociation for Computing Machinery
    ISBN (Electronic)9781450397957
    StatePublished - Sep 5 2022
    Event17th International Conference on the Foundations of Digital Games, FDG 2022 - Athens, Greece
    Duration: Sep 5 2022Sep 8 2022

    Publication series

    NameACM International Conference Proceeding Series


    Conference17th International Conference on the Foundations of Digital Games, FDG 2022

    ASJC Scopus subject areas

    • Software
    • Human-Computer Interaction
    • Computer Vision and Pattern Recognition
    • Computer Networks and Communications


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