Inspector: Pixel-Based Automated Game Testing via Exploration, Detection, and Investigation
Guoqing Liu, Mengzhang Cai, Li Zhao, Tao Qin, Adrian Brown, Jimmy, Bischoff, Tie-Yan Liu

TL;DR
This paper introduces Inspector, a pixel-based reinforcement learning agent for automated game testing that explores game space, detects key objects, and mimics human interactions without deep game integration.
Contribution
Inspector is a novel, generalizable game testing agent that operates solely on pixel inputs, enabling easy application across different games without deep integration.
Findings
Effectively explores game space using curiosity-driven rewards.
Successfully detects key objects with minimal labeled data.
Discovers potential bugs in tested games.
Abstract
Deep reinforcement learning (DRL) has attracted much attention in automated game testing. Early attempts rely on game internal information for game space exploration, thus requiring deep integration with games, which is inconvenient for practical applications. In this work, we propose using only screenshots/pixels as input for automated game testing and build a general game testing agent, Inspector, that can be easily applied to different games without deep integration with games. In addition to covering all game space for testing, our agent tries to take human-like behaviors to interact with key objects in a game, since some bugs usually happen in player-object interactions. Inspector is based on purely pixel inputs and comprises three key modules: game space explorer, key object detector, and human-like object investigator. Game space explorer aims to explore the whole game space by…
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Taxonomy
TopicsArtificial Intelligence in Games · Digital Games and Media · Reinforcement Learning in Robotics
