Date of Award


Document Type


Degree Name

Master of Science


Department of Operational Sciences

First Advisor

Matthew J. Robbins, PhD


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


DTIC Accession Number