Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games

Jeppe Theiss Kristensen, Paolo Burelli

    Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review

    Abstract

    While traditionally a labour intensive task, the testing of game content is progressively becoming more automated.
    Among the many directions in which this automation is taking shape, automatic play-testing is one of the most promising thanks also to advancements of many supervised and reinforcement learning (RL) algorithms.
    However these type of algorithms, while extremely powerful, often suffer in production environments due to issues with reliability and transparency in their training and usage.

    In this research work we are investigating and evaluating strategies to apply the popular RL method Proximal Policy Optimization (PPO) in a casual mobile puzzle game with a specific focus on improving its reliability in training and generalization during game playing.

    We have implemented and tested a number of different strategies against a real-world mobile puzzle game (Lily's Garden from Tactile Games).
    We isolated the conditions that lead to a failure in either training or generalization during testing and we identified a few strategies to ensure a more stable behaviour of the algorithm in this game genre.
    Original languageEnglish
    Title of host publicationProceedings of the International Conference on the Foundations of Digital Games
    PublisherAssociation for Computing Machinery
    Publication date2020
    ISBN (Electronic)9781450388078
    DOIs
    Publication statusPublished - 2020
    EventFDG 2020: Foundations of Digitale Games - Malta, Malta
    Duration: 16 Sept 202018 Sept 2020
    Conference number: 2020
    http://fdg2020.org/
    http://fdg2020.org

    Conference

    ConferenceFDG 2020: Foundations of Digitale Games
    Number2020
    LocationMalta
    Country/TerritoryMalta
    Period16/09/202018/09/2020
    Internet address

    Keywords

    • Game content testing
    • Automatic play-testing
    • Supervised learning
    • Reinforcement learning
    • Proximal Policy Optimization (PPO)
    • Mobile puzzle games
    • Reliability in training
    • Algorithm generalization
    • Machine learning algorithms
    • Game AI

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