Date of Graduation
5-2018
Document Type
Thesis
Degree Name
Bachelor of Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Gauch, John
Committee Member/Third Reader
Patitz, Matthew
Committee Member/Fourth Reader
Gashler, Michael
Abstract
Artificial intelligence (AI) and video games benefit from each other. Games provide a challenging domain for testing learning algorithms, and AI provides a framework to designing and implementing intelligent behavior, which reinforces meaningful play. Medium and small studios, and independent game developers, have limited resources to design, implement, and maintain agents with reactive behavior. In this research, we trained agents using machine learning (ML), aiming to find an alternative to expensive traditional algorithms for intelligent behavior used in video games. We use Unity as a game engine to implement the environments and TensorFlow for the neural network training.
Keywords
Machine Learning
Citation
Calderon, D. (2018). Training Machine Learning Agents in a 3D Game Engine. Computer Science and Computer Engineering Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/csceuht/50