Learning Controllable Content Generators

Sam Earle, Maria Edwards, Ahmed Khalifa, Philip Bontrager, Julian Togelius

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


    It has recently been shown that reinforcement learning can be used to train generators capable of producing high-quality game levels, with quality defined in terms of some user-specified heuristic. To ensure that these generators' output is sufficiently diverse (that is, not amounting to the reproduction of a single optimal level configuration), the generation process is constrained such that the initial seed results in some variance in the generator's output. However, this results in a loss of control over the generated content for the human user. We propose to train generators capable of producing controllably diverse output, by making them 'goal-aware.' To this end, we add conditional inputs representing how close a generator is to some heuristic, and also modify the reward mechanism to incorporate that value. Testing on multiple domains, we show that the resulting level generators are capable of exploring the space of possible levels in a targeted, controllable manner, producing levels of comparable quality as their goal-unaware counterparts, that are diverse along designer-specified dimensions.

    Original languageEnglish (US)
    Title of host publication2021 IEEE Conference on Games, CoG 2021
    PublisherIEEE Computer Society
    ISBN (Electronic)9781665438865
    StatePublished - 2021
    Event2021 IEEE Conference on Games, CoG 2021 - Copenhagen, Denmark
    Duration: Aug 17 2021Aug 20 2021

    Publication series

    NameIEEE Conference on Computatonal Intelligence and Games, CIG
    ISSN (Print)2325-4270
    ISSN (Electronic)2325-4289


    Conference2021 IEEE Conference on Games, CoG 2021


    • conditional generation
    • game AI
    • pcgrl
    • procedural content generation
    • reinforcement learning

    ASJC Scopus subject areas

    • Artificial Intelligence
    • Computer Graphics and Computer-Aided Design
    • Computer Vision and Pattern Recognition
    • Human-Computer Interaction
    • Software


    Dive into the research topics of 'Learning Controllable Content Generators'. Together they form a unique fingerprint.

    Cite this