Available at: https://digitalcommons.calpoly.edu/theses/2905
Date of Award
3-2023
Degree Name
MS in Computer Science
Department/Program
Computer Science
College
College of Engineering
Advisor
Rodrigo Canaan
Advisor Department
Computer Science
Advisor College
College of Engineering
Abstract
This dissertation addresses the need for using machine learning-based methods rather than traditional rule-based methods for controlling non-playable characters (NPCs). The goal of the Reinforcement Learning Framework for the Unreal Engine is to enable game development studios to create, train, and more easily implement smarter, more compelling AI characters in major video game releases. The framework contains three distinct software libraries: an Unreal Engine reinforcement learning library whose purpose is to enable Unreal Engine levels to act as reinforcement learning environments, a python library which provides convenient abstractions and implementations to the reinforcement learning process, and a flexible connection system responsible for the communication between the two sides of the framework. In this dissertation, I describe the framework in detail, demonstrate the framework’s capability by implementing, training, and evaluating on the cartpole benchmark, and prove the system’s viability by comparing it to similar tools already on the market.