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.

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