Document Type
Thesis
Abstract
Developers create video games using Artificial Intelligence (AI) agents to provide a challenging opponent in a single-player game. However, studies show that when Reinforcement Learning (RL) agents are used, they outperform the AI agents. This project sought to test how RL agents would perform in StarClash, a video game without RL agents, using Q-Learning. This was done by creating two Q-Learning agents: a Simple agent and an Advanced (more complex) agent. These two agents were tested against each other and a Random AI agent. As expected, the Advanced agent did better than the Simple agent but only performed slightly better, which was unexpected. Also unexpected, was that both the Simple and Advanced agents only had a win rate of approximately 50% against the Random AI.
Disciplines
Artificial Intelligence and Robotics | Other Computer Sciences | Theory and Algorithms
Publication Date
12-1-2024
Language
English
Faculty Mentor of Honors Project
Kelly French
License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Recommended Citation
Pankaj, Hanani, "Q-Learning in StarClash" (2024). 2024 Fall Honors Capstone Projects. 1.
https://mavmatrix.uta.edu/honors_fall2024/1
Included in
Artificial Intelligence and Robotics Commons, Other Computer Sciences Commons, Theory and Algorithms Commons