A Metric Learning Approach to Anomaly Detection in Video Games

Abstract--With the aim of designing automated tools that assist in the video game quality assurance process, we frame the problem of identifying bugs in video games as an anomaly detection (AD) problem. We develop State-State Siamese Networks (S3N) as an efficient deep metric learning approach to AD in this context and explore how it may be used as part of an automated testing tool. Finally, we show by empirical evaluation on a series of Atari games, that S3N is able to learn a meaningful embedding, and consequently is able to identify various common types of video game bugs. Video game development companies take significant steps at all stages of development to reduce the likelihood of bugs appearing in release code. These steps range from the use of software development paradigms early in the process to heavy investment in Quality Assurance (QA) closer to release.