Analyzing the robustness of general video game playing agents

Diego Perez Liebana, Spyridon Samothrakis, J. Togelius, T. Schaul, S. Lucas
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引用次数: 18

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.
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一般视频游戏代理的鲁棒性分析
本文研究了一般视频游戏代理的鲁棒性和可变性。分析的代理包括2014年和2015年通用电子游戏人工智能竞赛中不同回合的赢家,以及与其框架一起分发的两个样本代理。最初,这些代理在四场比赛中运行,并根据比赛规则进行排名。然后,对游戏的奖励信号进行不同的修改,并在控制器执行的动作、它们的前向模型或两者中引入噪声。结果表明,通过引入本文提出的修改,有可能使排名发生重大变化。这是一个重要的结果,因为它可以通过添加参数变化版本来自动扩展人类创作的游戏集,这些版本可以添加信息并洞察被测代理的相对优势。结果还表明,一些控制器在几乎所有条件下都表现良好,证明了GVGAI基准的鲁棒性。
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