Autonomous Decision System for UAV Control in 3D Simulated Environment Using deep neural networks

<ns3:p> This article presents an autonomous decision-making system for unmanned aerial vehicles (UAVs) operating in GPS-denied environments. The proposed solution is based on reinforcement learning and is intended to support mission execution when external positioning signals and continuous operator guidance are unavailable or unreliable. The system is implemented in a Unity-based 3D simulation environment and employs a PPO-trained Actor–Critic neural architecture with continuous control outputs. The primary operational scenario considered in this work is the autonomous protection of a mothership drone, where the UAV must maintain effective defensive positioning, preserve mission continuity, and react to dynamic threats and environmental constraints. In addition, a strike mission is included as a secondary task configuration to demonstrate the adaptability of the proposed framework to different UAV operational roles. The article discusses the conceptual structure of the system, the observation design, the control formulation, and the role of reward engineering in shaping autonomous mission behavior. </ns3:p>