Publication Date
2025
Document Type
Thesis
Committee Members
Xiaodong Zhang, Ph.D. (Advisor); Trevor Bihl, Ph.D. (Committee Member); Weisong Wang, Ph.D. (Committee Member)
Degree Name
Master of Science in Electrical Engineering (MSEE)
Abstract
This thesis investigates the integration of relational observations with the reinforcement learning (RL) framework for improved generalization capability. A hide-and-seek simulation environment is designed in Unity for proof-of-concept demonstration. Two observation representations—relational (analogical) and standard positional—are designed to evaluate agent learning and generalization capabilities. Agents are trained using the Proximal Policy Optimization (PPO) and Soft Actor Critic (SAC) algorithms in a random-room environment and tested in both the random-room environment and a novel environment with greater spatial complexity and path obstructions. Comparative studies indicate that relational representation of objects in the adversarial environment could potentially improve the generalization capability of RL agents to novel and complex environments. Cross-testing results also suggest that relational observations may enhance agents’ effectiveness in pursuit and evasion tasks in adversarial environments.
Page Count
58
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2025
Copyright
Copyright 2025, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0009-4653-9806
