  The proliferation of unmanned aerial vehicles (UAVs) in controlled airspace
presents significant risks, including potential collisions, disruptions to air
traffic, and security threats. Ensuring the safe and efficient operation of
airspace, particularly in urban environments and near critical infrastructure,
necessitates effective methods to intercept unauthorized or non-cooperative
UAVs. This work addresses the critical need for robust, adaptive systems
capable of managing such threats through the use of Reinforcement Learning
(RL). We present a novel approach utilizing RL to train fixed-wing UAV pursuer
agents for intercepting dynamic evader targets. Our methodology explores both
model-based and model-free RL algorithms, specifically DreamerV3, Truncated
Quantile Critics (TQC), and Soft Actor-Critic (SAC). The training and
evaluation of these algorithms were conducted under diverse scenarios,
including unseen evasion strategies and environmental perturbations. Our
approach leverages high-fidelity flight dynamics simulations to create
realistic training environments. This research underscores the importance of
developing intelligent, adaptive control systems for UAV interception,
significantly contributing to the advancement of secure and efficient airspace
management. It demonstrates the potential of RL to train systems capable of
autonomously achieving these critical tasks.
