@article{ME580BC66,
title = "UnityPGTA: A Unity Platformer Game Testing Automation Tool Using Reinforcement Learning",
journal = "Journal of KIISE, JOK",
year = "2024",
issn = "2383-630X",
doi = "10.5626/JOK.2024.51.2.149",
author = "Se-chan Park,Deock Yeop Kim,Woo Jin Lee",
keywords = "reinforcement learning,Unity (game engine),game testing automation,Unity ML-Agents toolkit",
abstract = "The cost of game testing in the video game industry is significant, accounting for nearly half of the expenses. Research efforts are underway to automate testing processes to reduce testing costs. However, existing research on test automation often involves manual tasks such as script writing, which is costly and labor-intensive. Additionally, implementations using virtual environments like VGDL and GVG-AI pose challenges when applied to real game testing. In this paper, we propose a tool for automating game testing with the aim of system fault detection, focusing on a Unity platformer game. The proposed tool is based on a commercial game engine, autonomously analyzing the game without human intervention to establish an automated game testing environment. We compare and analyze the error detection results of the proposed tool with a random baseline model using open-source games, demonstrating the tool"s effectiveness in performing automated game analysis and testing environment setup, ultimately reducing testing costs and improving quality and stability."
}