Date of Award

3-2025

Document Type

Thesis

Degree Name

Master of Science

Department

Department of Operational Sciences

First Advisor

Matthew A. Robbins, PhD

Abstract

The integration of automated processes in defense continues to expand, enhancing the lethality of military forces. Artificial intelligence accelerates decision-making cycles, removes the constraints of human-operated hardware, and improves coordination by enabling seamless integration across multiple systems. Suppression of Enemy Air Defenses (SEAD) missions are critical to the United States (U.S.) military, as they neutralize hostile air defense systems, ensuring air superiority and enabling safe and effective operations for aircraft in contested environments. Therefore, it is necessary to pair emerging autonomous capabilities with an important mission set in defense. This research investigates the Autonomous Unmanned Air-to-Ground Strike (AUAGS) problem, modeling it as a continuous-time Markov Decision Process (MDP) to identify optimal policies and emergent behaviors in the agent’s maneuvering and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) provides high-fidelity six-degree-of-freedom (6DOF) interactions between entities within the problem domain. A deep reinforcement learning approach, specifically an actor-critic method, is employed to address three distinct SEAD operations. These operations begin with simpler scenarios to validate the concept before progressing in complexity to assess the algorithm’s robustness. A neural network approximates the value function, refining the policy through iterative updates. The research also involves designing hyperparameter tuning experiments to enhance the likelihood of emergent behavior, followed by extended training runs to analyze policy performance and observe emergent strategies.

AFIT Designator

AFIT-ENS-MS-25-M-167

Comments

An embargo was observed for this posting.

Distribution A: Approved for public release, Distribution Unlimited. PA case number 88ABW-2025-0352

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