Honors Program
Honors in Technology
Date of Award
12-2021
Thesis Professor(s)
Jeffrey Roach
Thesis Professor Department
Computing
Thesis Reader(s)
Brian Bennett
Abstract
Artificial intelligence (AI) increases the immersion that players can have while playing games. Modern game engines, a middleware software used to create games, implement simple AI behaviors that developers can use. Advanced AI behaviors must be implemented manually by game developers, which decreases the likelihood of game developers using advanced AI due to development overhead.
A custom game engine and custom AI architecture that handled deep reinforcement learning was designed and implemented. Snake was created using the custom game engine to test the feasibility of natively implementing an AI architecture into a game engine. A snake agent was successfully trained using the AI architecture, but the learned behavior was suboptimal. Although the learned behavior was suboptimal, the AI architecture was successfully implemented into a custom game engine because a behavior was successfully learned.
Publisher
East Tennessee State University
Document Type
Honors Thesis - Open Access
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
Recommended Citation
Kincer, Austin, "Natively Implementing Deep Reinforcement Learning into a Game Engine" (2021). Undergraduate Honors Theses. Paper 653. https://dc.etsu.edu/honors/653
Copyright
Copyright by the authors.