A Review of Research on the Application of Deep Reinforcement Learning in Unmanned Aerial Vehicle Resource Allocation and Trajectory Planning

Abstract

In recent years, Unmanned Aerial Vehicle (UAV) has played an important role in the field of wireless communication with its high mobility and high controllability. In this paper, we focus on the application of deep reinforcement learning (DRL) in UAV resource allocation and trajectory planning. DRL is used to design the decision deployment of UAVs, optimize data transfer rate, throughput, energy efficiency and other metrics, and learn better path planning strategies to make UAVs’ decisions more intelligent. We summarize the reinforcement learning (RL) methods, describe the relevant research in the field of UAV wireless communication in recent years, and investigate the algorithmic mechanisms of DRL such as deep Q network (DQN) and deep deterministic policy gradient (DDPG) algorithm.