Integrating deep reinforcement learning in autonomous UAVs is a breakthrough in their navigation, control, and decision-making capabilities to be more adaptive and autonomous. In the demand for more advanced and autonomous control systems for UAVs, the increase in operation-related applications, such as surveillance, agriculture, logistics, search and rescue, among others, is directly correlated. Traditional control methodologies, though excellent in controlled environments, quite often fall short in dynamic and unpredictable scenarios. We will give an elaborate account of the application of DRL in UAVs, considering novel approaches, technical implementations, and scientific methodology in this work. We will begin with an overview of the fundamental principles of RL and deep learning in general. This sets the basis for understanding their synergy in DRL. DRL combines the reinforcement learning decision-making framework with the powerful function approximation abilities of deep learning to provide a general solution to these concerns. In this paper, a comprehensive review of the application of DRL in UAVs is presented, covering innovative approaches, technical implementations, and scientific methodology. We begin with a general overview of the fundamental principles of RL and deep learning in general, which sets the basis for understanding their. Some of the most popular DRL algorithms are Deep Q-Networks (DQN), policy gradient methods such as Proximal Policy Optimization (PPO), and actor- critic methods such as Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3). The hardware considerations, software frameworks, and the setting up of the training environment are presented in technical detail. Moreover, the chapter elaborates on the use of simulation platforms, such as AirSim and Gazebo, for training DRL models and transferring methods to real UAVs. The practical applicability of DRL for UAVs is also explained through various case studies. These include the use of DRL for navigation in cluttered environments and formation control. Finally, we present the key technical challenges associated with the use of DRL regarding sample efficiency, training time, generalization, and safety concerns. Last but not least, multi-agent DRL research, adversarial training, and integration of DRL with other AI techniques are promising and can further advance UAV autonomy and performance. In this regard, the current article is intended to provide an elaborate and accurate review of the state and future potential of DRL in autonomous UAV systems.

Enhancing Autonomous UAV Capabilities with Deep Reinforcement Learning

会议论文
发表日期：

2026年

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Sarkhan Aghadadashov

摘要

Integrating deep reinforcement learning in autonomous UAVs is a breakthrough in their navigation, control, and decision-making capabilities to be more adaptive and autonomous. In the demand for more advanced and autonomous control systems for UAVs, the increase in operation-related applications, such as surveillance, agriculture, logistics, search and rescue, among others, is directly correlated. Traditional control methodologies, though excellent in controlled environments, quite often fall short in dynamic and unpredictable scenarios. We will give an elaborate account of the application of DRL in UAVs, considering novel approaches, technical implementations, and scientific methodology in this work. We will begin with an overview of the fundamental principles of RL and deep learning in general. This sets the basis for understanding their synergy in DRL. DRL combines the reinforcement learning decision-making framework with the powerful function approximation abilities of deep learning to provide a general solution to these concerns. In this paper, a comprehensive review of the application of DRL in UAVs is presented, covering innovative approaches, technical implementations, and scientific methodology. We begin with a general overview of the fundamental principles of RL and deep learning in general, which sets the basis for understanding their. Some of the most popular DRL algorithms are Deep Q-Networks (DQN), policy gradient methods such as Proximal Policy Optimization (PPO), and actor- critic methods such as Deep Deterministic Policy Gradient (DDPG) and Twin Delayed DDPG (TD3). The hardware considerations, software frameworks, and the setting up of the training environment are presented in technical detail. Moreover, the chapter elaborates on the use of simulation platforms, such as AirSim and Gazebo, for training DRL models and transferring methods to real UAVs. The practical applicability of DRL for UAVs is also explained through various case studies. These include the use of DRL for navigation in cluttered environments and formation control. Finally, we present the key technical challenges associated with the use of DRL regarding sample efficiency, training time, generalization, and safety concerns. Last but not least, multi-agent DRL research, adversarial training, and integration of DRL with other AI techniques are promising and can further advance UAV autonomy and performance. In this regard, the current article is intended to provide an elaborate and accurate review of the state and future potential of DRL in autonomous UAV systems.