Off-campus UMass Amherst users: To download campus access dissertations, please use the following link to log into our proxy server with your UMass Amherst user name and password.

Non-UMass Amherst users: Please talk to your librarian about requesting this dissertation through interlibrary loan.

Dissertations that have an embargo placed on them will not be available to anyone until the embargo expires.

Author ORCID Identifier

https://orcid.org/0000-0003-4567-8627

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Computer Science

Year Degree Awarded

2023

Month Degree Awarded

February

First Advisor

Philip S. Thomas

Subject Categories

Artificial Intelligence and Robotics

Abstract

Scientific fields make advancements by leveraging the knowledge created by others to push the boundary of understanding. The primary tool in many fields for generating knowledge is empirical experimentation. Although common, generating accurate knowledge from empirical experiments is often challenging due to inherent randomness in execution and confounding variables that can obscure the correct interpretation of the results. As such, researchers must hold themselves and others to a high degree of rigor when designing experiments. Unfortunately, most reinforcement learning (RL) experiments lack this rigor, making the knowledge generated from experiments dubious. This dissertation proposes methods to address central issues in RL experimentation.

Evaluating the performance of an RL algorithm is the most common type of experiment in RL literature. Most performance evaluations are often incapable of answering a specific research question and produce misleading results. Thus, the first issue we address is how to create a performance evaluation procedure that holds up to scientific standards.

Despite the prevalence of performance evaluation, these types of experiments produce limited knowledge, e.g., they can only show how well an algorithm worked and not why, and they require significant amounts of time and computational resources. As an alternative, this dissertation proposes that scientific testing, the process of conducting carefully controlled experiments designed to further the knowledge and understanding of how an algorithm works, should be the primary form of experimentation.

Lastly, this dissertation provides a case study using policy gradient methods, showing how scientific testing can replace performance evaluation as the primary form of experimentation. As a result, this dissertation can motivate others in the field to adopt more rigorous experimental practices.

DOI

https://doi.org/10.7275/32956067

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Share

COinS