Efficient Reinforcement Learning for Control

The landscape of control systems has evolved rapidly with the emergence of Reinforcement Learning (RL), offering promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a lack of structure in learning mech- anisms. This thesis explores a central question: How can we design reinforcement learning algorithms that are not only effective but also computationally effi- cient and scalable for control systems of increasing complexity? To address this, we present a progression of approaches—starting with time-scale decomposition in small- scale systems and moving towards structured and adaptive learning strategies for large-scale, multi-agent control problems. Each chapter builds upon the previous one by introducing new methods tailored to the complexity and scale of the environment, culminating in a unified framework for efficient RL-driven control

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Computer Science & Applications

Department dc:contributor.department

Computer Science and#38; Applications

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Baddam, Vasanth Reddy

Chairs dc:contributor.committeechair

• Eldardiry, Hoda Mohamed
• Boker, Almuatazbellah M.

Committee members dc:contributor.committeemember

• Gumussoy, Suat
• Cho, Jin-Hee
• Watson, Layne T.

• Optimal Control; Reinforcement Learning

Statement dc:rights

• Creative Commons Attribution-NonCommercial 4.0 International

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc/4.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:43548

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/135747