The Performance Evaluation of Reinforcement Learning Algorithms for Autonomous Navigation in Simulated Environments Using NS2 and Air-Sim-DRL

This study presents an exploration into the use of Reinforcement Learning (RL), specifically Deep Q-Networks (DQN), for autonomous drone navigation within complex, obstacle-rich environments. Utilizing Microsoft’s AirSim simulator and an open-source DRL integration framework (AirsimDRL), the research trains a drone to intelligently reach target destinations while avoiding collisions. The agent interacts with a dynamic simulated world, learning optimal control strategies from scratch. The study aims to bridge the gap between traditional UAV path planning and intelligent, learning- based navigation systems, laying the foundation for real-world autonomous drone applications.