ORCID Identifier(s)

ORCID 0009-0003-1031-6121

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

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