Multi-Agent Deep Reinforcement Learning Based Trajectory Planning for Multi-UAV Assisted Mobile Edge Computing

by   Liang Wang, et al.

An unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) framework is proposed, where several UAVs having different trajectories fly over the target area and support the user equipments (UEs) on the ground. We aim to jointly optimize the geographical fairness among all the UEs, the fairness of each UAV' UE-load and the overall energy consumption of UEs. The above optimization problem includes both integer and continues variables and it is challenging to solve. To address the above problem, a multi-agent deep reinforcement learning based trajectory control algorithm is proposed for managing the trajectory of each UAV independently, where the popular Multi-Agent Deep Deterministic Policy Gradient (MADDPG) method is applied. Given the UAVs' trajectories, a low-complexity approach is introduced for optimizing the offloading decisions of UEs. We show that our proposed solution has considerable performance over other traditional algorithms, both in terms of the fairness for serving UEs, fairness of UE-load at each UAV and energy consumption for all the UEs.


page 2

page 4

page 5

page 6

page 7

page 8

page 10

page 11


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...

Evolutionary Multi-Objective Reinforcement Learning Based Trajectory Control and Task Offloading in UAV-Assisted Mobile Edge Computing

This paper studies the trajectory control and task offloading (TCTO) pro...

Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle

As intelligent transportation systems been implemented broadly and unman...

Personalized Federated Deep Reinforcement Learning-based Trajectory Optimization for Multi-UAV Assisted Edge Computing

In the era of 5G mobile communication, there has been a significant surg...

To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs

A deep reinforcement learning technique is presented for task offloading...

3D UAV Trajectory Design for Fair and Energy-Efficient Communication: A Deep Reinforcement Learning Technique

In different situations, like disaster communication and network connect...

Please sign up or login with your details

Forgot password? Click here to reset