Deep Reinforcement Learning Applied for Threat Engagement by Loyal Wingmen Drones

This study explores using Loyal Wingmen drones in counter- UAV systems to detect and neutralize multiple aerial threats cooperatively. We employ Curriculum Learning and Bayesian Optimization to enhance the Proximal Policy Optimization algorithm, which trains a Deep Neural Network to perform high-level decision-making. The effectiveness of this network is assessed in simulations that pit it against an increasing number of Loitering Munitions, with a comparative analysis of a Behavior Tree agent. A key finding is the effective deployment of simulated LiDAR technology for dynamic multi-target detection, which surpasses traditional systems limited to observing a fixed number of entities.