Deep Reinforcement Learning Based Dynamic Trajectory Control for UAV-assisted Mobile Edge Computing

In this paper, we consider a platform of flying mobile edge computing (F-MEC), where unmanned aerial vehicles (UA Vs) serve as equipment providing computation resource, and they enable task offload-ing from user equipment (UE). We aim to minimize energy consumption of all the UEs via optimizing the user association, resource allocation and the trajectory of UA Vs. To this end, we first propose a Convex optimizAtion based Trajectory control algorithm (CA T), which solves the problem in an iterative way by using block coordinate descent (BCD) method. Then, to make the real-time decision while taking into account the dynamics of the environment (i.e., UA V may take off from different locations), we propose a deep Reinforcement leArning based Trajectory control algorithm (RA T). In RA T, we apply the Prioritized Experience Replay (PER) to improve the convergence of the training procedure. Different from the convex optimization based algorithm which may be susceptible to the initial points and requires iterations, RA T can be adapted to any taking off points of the UA Vs and can obtain the solution more rapidly than CA T once training process has been completed. Simulation results show that the proposed CA T and RA T achieve the similar performance and both outperform traditional algorithms. Liang, Kezhi and Nauman are with the Department of Computer and Informantion Science, Northumbria University, Newcastle upon Tyne, UK, NE1 8ST. Cunhua and Arumugam are with School of Electronic Engineering and Computer Science, Queen Mary University of London, E1 4NS, U.K. Wei is with National Mobile Communications Research Lab, Southeast University, China. I NTRODUCTION With the popularity of computationally-intensive tasks, e.g., smart navigation and augmented reality, people are expecting to enjoy more convenient life than ever before. However, current smart devices and user equipments (UEs), due to small size and limited resource, e.g., computation and battery, may not be able to provide satisfactory Quality of Service (QoS) and Quality of Experience (QoE) in executing those highly demanding tasks. Mobile edge computing (MEC) has been proposed by moving the computation resource to the network edge and it has been proved to greatly enhance UE's ability in executing computation-hungry tasks [1].