Deep Reinforcement Learning for Autonomous Drones
Abstract
Autonomous drones have gained significant attention for various applications, such as surveillance, package delivery, and environmental monitoring. To enable these drones to navigate complex and dynamic environments effectively, the application of deep reinforcement learning (DRL) has emerged as a promising approach. This abstract provides an overview of the state-of-the-art research on DRL for autonomous drones and its potential impact on a wide range of industries.
This paper begins by introducing the key concepts of deep reinforcement learning, emphasizing the utilization of neural networks to learn and optimize drone control policies. It then delves into the challenges of autonomous drone navigation, including obstacle avoidance, path planning, and real-time decision-making. The abstract highlights the importance of leveraging DRL techniques to address these challenges.
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References
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