Date of Award
3-2022
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Operational Sciences
First Advisor
Matthew J. Robbins, PhD
Abstract
We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one and two-versus-one scenarios are constructed to test whether an AUCAV can outmaneuver and destroy a superior enemy AUCAV. The performance is evaluated across offensive, defensive, and neutral starts, leading to 6 problem instances. The ADP policies outperform the position-energy benchmark policy in 4 of 6 problem instances. Results show the ADP approach mimics certain basic fighter maneuvers and section tactics.
AFIT Designator
AFIT-ENS-MS-22-M-157
DTIC Accession Number
AD1172381
Recommended Citation
Mottice, David A., "Team Air Combat using Model-based Reinforcement Learning" (2022). Theses and Dissertations. 5364.
https://scholar.afit.edu/etd/5364