Reinforcement Learning Algorithms for Autonomous Drone Navigation and Control

Abstract

Autonomous drone navigation and control have garnered significant attention due to their applications in surveillance, delivery, agriculture, and disaster management. Traditional control methods often struggle with complex, dynamic, and uncertain environments. Reinforcement Learning (RL), a branch of machine learning where agents learn optimal policies through interactions with the environment, offers promising solutions for enabling drones to navigate autonomously with minimal human intervention. This paper explores the design and implementation of various reinforcement learning algorithms tailored for autonomous drone navigation and control. We review model-free and model-based RL methods, including Q-learning, Deep Q-Networks (DQN), Policy Gradient, and Actor-Critic algorithms, highlighting their applicability to the continuous state and action spaces typical in drone control. A simulation framework is developed to train and test these algorithms in scenarios involving obstacle avoidance, path planning, and velocity control under stochastic disturbances like wind gusts. The results indicate that deep reinforcement learning algorithms, particularly Actor-Critic methods, exhibit superior performance in learning robust policies that ensure safe navigation and stable flight control. Challenges such as sample inefficiency, explorationexploitation balance, and real-time computation are discussed. The study also emphasizes transfer learning and domain adaptation techniques to bridge the gap between simulated training and real-world deployment. Overall, this research contributes to advancing autonomous drone capabilities by providing an in-depth analysis of reinforcement learning methods, demonstrating their potential to revolutionize drone navigation and control, and offering a foundation for future research in adaptive, intelligent unmanned aerial systems.