Deep Reinforcement Learning for General Video Game AI

Ruben Rodriguez Torrado, Philip Bontrager, Julian Togelius, Jialin Liu, Diego Perez-Liebana

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

    Abstract

    The General Video Game AI (GVGAI) competition and its associated software framework provides a way of benchmarking AI algorithms on a large number of games written in a domain-specific description language. While the competition has seen plenty of interest, it has so far focused on online planning, providing a forward model that allows the use of algorithms such as Monte Carlo Tree Search. In this paper, we describe how we interface GVGAI to the OpenAI Gym environment, a widely used way of connecting agents to reinforcement learning problems. Using this interface, we characterize how widely used implementations of several deep reinforcement learning algorithms fare on a number of GVGAI games. We further analyze the results to provide a first indication of the relative difficulty of these games relative to each other, and relative to those in the Arcade Learning Environment under similar conditions.

    Original languageEnglish (US)
    Title of host publicationProceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018
    PublisherIEEE Computer Society
    Volume2018-August
    ISBN (Electronic)9781538643594
    DOIs
    Publication statusPublished - Oct 11 2018
    Event14th IEEE Conference on Computational Intelligence and Games, CIG 2018 - Maastricht, Netherlands
    Duration: Aug 14 2018Aug 17 2018

    Other

    Other14th IEEE Conference on Computational Intelligence and Games, CIG 2018
    CountryNetherlands
    CityMaastricht
    Period8/14/188/17/18

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    Keywords

    • Advantage actor critic
    • Deep Q-learning
    • Deep reinforcement learning
    • General video game AI
    • OpenAI Gym
    • Video game description language

    ASJC Scopus subject areas

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

    Cite this

    Torrado, R. R., Bontrager, P., Togelius, J., Liu, J., & Perez-Liebana, D. (2018). Deep Reinforcement Learning for General Video Game AI. In Proceedings of the 2018 IEEE Conference on Computational Intelligence and Games, CIG 2018 (Vol. 2018-August). [8490422] IEEE Computer Society. https://doi.org/10.1109/CIG.2018.8490422