Deep Reinforcement Learning for Autonomous Drone Navigation in Dynamic Environments
Abstract
Autonomous drones face challenges in navigating complex and dynamic environments safely and efficiently. This paper investigates the application of deep reinforcement learning (DRL) techniques for training drones to navigate autonomously in real-world scenarios. We propose a DRL-based navigation framework and demonstrate its effectiveness through simulations and real-world experiments.
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