Improving Deep Reinforcement Learning Methodology for Autonomous Defense and Escort of Military High-value Assets

Author

Joseph Liles IV

Date of Award

9-2023

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Department of Operational Sciences

First Advisor

Matthew J. Robbins, PhD

Abstract

This dissertation explores the application of machine learning to the control of autonomous unmanned combat aerial vehicles (AUCAVs). In particular, this research applies deep reinforcement learning methodologies to a defensive air combat scenario wherein a fleet of AUCAVs protects a military high-value asset (HVA). A collection of air battle management scenarios along with an original simulation environment and a set of designed computational experiments support the approximation of high-quality decision policies by employing Markov decision processes, approximate dynamic programming algorithms, and deep neural networks for value function approximation.

AFIT Designator

AFIT-ENS-DS-23-S-017

Comments

A 12-month embargo was observed for posting this dissertation on AFIT Scholar.

Approved for public release. PA case number on file.

Recommended Citation

Liles, Joseph IV, "Improving Deep Reinforcement Learning Methodology for Autonomous Defense and Escort of Military High-value Assets" (2023). Theses and Dissertations. 7667.
https://scholar.afit.edu/etd/7667