Level QA testing with curiosity-driven AI agents
Loading...
Journal Title
Journal ISSN
Volume Title
School of Arts, Design and Architecture |
Master's thesis
Location:
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.
Author
Date
2023
Department
Major/Subject
Mcode
Degree programme
Master's Programme in New Media
Language
en
Pages
40
Series
Abstract
Advancements in reinforcement learning have allowed agents to play games of increased complexity. One potential use-case of this technology for the game industry would be for QA testing. This thesis investigates the possibility of training an autonomous agent inside a prevalent game engine, Unreal Engine 5.0, to explore a level and detect missing colliders. The focus of this thesis is the study of the performance of a curiosity-driven AI agent. Curiosity allows agents to discover novel game states through intrinsic motivation, without explicitly defined goals. Coupled with a count-based exploration of the game’s level and a visualization of the agent’s path, the solution’s coverage and speed is evaluated. It is compared to a random policy and a human tester. Results indicate that the agent’s coverage is sufficient to explore the whole level. Its performance in finding collision bugs is higher than both a random policy and a human tester.Description
Supervisor
Hämäläinen, PerttuThesis advisor
Guckelsberger, ChristianKeywords
reinforcement learning, machine learning, game testing, artificial intelligence, Unreal engine, AI
