Analyzing the robustness of general video game playing agents

Diego Perez-Liebana, Spyridon Samothrakis, Julian Togelius, Tom Schaul, Simon M. Lucas

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

    Abstract

    This paper presents a study on the robustness and variability of performance of general video game-playing agents. Agents analyzed includes those that won the different legs of the 2014 and 2015 General Video Game AI Competitions, and two sample agents distributed with its framework. Initially, these agents are run in four games and ranked according to the rules of the competition. Then, different modifications to the reward signal of the games are proposed and noise is introduced in either the actions executed by the controller, their forward model, or both. Results show that it is possible to produce a significant change in the rankings by introducing the modifications proposed here. This is an important result because it enables the set of human-authored games to be automatically expanded by adding parameter-varied versions that add information and insight into the relative strengths of the agents under test. Results also show that some controllers perform well under almost all conditions, a testament to the robustness of the GVGAI benchmark.

    Original languageEnglish (US)
    Title of host publication2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
    PublisherIEEE Computer Society
    ISBN (Electronic)9781509018833
    DOIs
    StatePublished - Jul 2 2016
    Event2016 IEEE Conference on Computational Intelligence and Games, CIG 2016 - Santorini, Greece
    Duration: Sep 20 2016Sep 23 2016

    Publication series

    NameIEEE Conference on Computatonal Intelligence and Games, CIG
    Volume0
    ISSN (Print)2325-4270
    ISSN (Electronic)2325-4289

    Other

    Other2016 IEEE Conference on Computational Intelligence and Games, CIG 2016
    Country/TerritoryGreece
    CitySantorini
    Period9/20/169/23/16

    ASJC Scopus subject areas

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

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