Date of Award
3-2023
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Matthew J. Robbins, PhD
Abstract
A one-versus-one air combat maneuvering problem is considered wherein a friendly autonomous aircraft must engage and defeat an adversary autonomous aircraft in a beyond visual range environment. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) is leveraged to model the complex and interdependent operations of aircraft, sensors, and weapons utilized in beyond visual range air combat. We formulate a Markov decision process to obtain high-quality decision policies wherein our autonomous aircraft makes maneuvering and missile firing decisions. We utilize a reinforcement learning solution procedure that implements a linear value function approximation to represent state-decision pairs due to the high dimensional and continuous nature of the state and decision variables. A representative scenario with a neutral starting state is created to train our autonomous aircraft and assess the performance of our reinforcement learning solution approach. Our results validate the feasibly of using a reinforcement learning solution procedure to train autonomous aircraft in AFSIM, and provide a pathway for future researchers.
AFIT Designator
AFIT-ENS-MS-23-M-156
Recommended Citation
Taylor, Caleb A., "A Reinforcement Learning Approach to a Beyond Visual Range Air Combat Maneuvering Problem" (2023). Theses and Dissertations. 7670.
https://scholar.afit.edu/etd/7670
Included in
Artificial Intelligence and Robotics Commons, Aviation Commons, Other Operations Research, Systems Engineering and Industrial Engineering Commons
Comments
A 12-month embargo was observed for this thesis.
Approved for public release. Clearance case number on file.