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Open Access

ARTICLE

DRL-Based Task Scheduling and Trajectory Control for UAV-Assisted MEC Systems

Sai Xu1,*, Jun Liu1,*, Shengyu Huang1, Zhi Li2
1 School of Computer Science and Engineering, Northeastern University, Shenyang, 110169, China
2 School of Information Science and Engineering, Shenyang Ligong University, Shenyang, 110159, China
* Corresponding Author: Sai Xu. Email: email; Jun Liu. Email: email
(This article belongs to the Special Issue: Intelligent Computation and Large Machine Learning Models for Edge Intelligence in industrial Internet of Things)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2025.071865

Received 13 August 2025; Accepted 28 October 2025; Published online 28 November 2025

Abstract

In scenarios where ground-based cloud computing infrastructure is unavailable, unmanned aerial vehicles (UAVs) act as mobile edge computing (MEC) servers to provide on-demand computation services for ground terminals. To address the challenge of jointly optimizing task scheduling and UAV trajectory under limited resources and high mobility of UAVs, this paper presents PER-MATD3, a multi-agent deep reinforcement learning algorithm with prioritized experience replay (PER) into the Centralized Training with Decentralized Execution (CTDE) framework. Specifically, PER-MATD3 enables each agent to learn a decentralized policy using only local observations during execution, while leveraging a shared replay buffer with prioritized sampling and centralized critic during training to accelerate convergence and improve sample efficiency. Simulation results show that PER-MATD3 reduces average task latency by up to 23%, improves energy efficiency by 21%, and enhances service coverage compared to state-of-the-art baselines, demonstrating its effectiveness and practicality in scenarios without terrestrial networks.

Keywords

Mobile edge computing; deep reinforcement learning; task offloading; resource allocation; trajectory control

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