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

2020-09-23 · Liang Wang, Kezhi Wang, Cunhua Pan, Wei Xu, Nauman Aslam, Lajos Hanzo

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.

📄 PDF Abstract BibTeX arXiv:2009.11277

Code (0)

등록된 구현이 없습니다.

Tasks

Deep Reinforcement LearningEdge-computingFairness Trajectory Planning

Scene Induced Multi-Modal Trajectory Forecasting via Planning

2019-05-23 · Nachiket Deo, Mohan M. Trivedi

We address multi-modal trajectory forecasting of agents in unknown scenes by formulating it as a planning problem. We present an approach consisting of three models; a goal prediction model to identify potential goals of…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)Trajectory Forecasting

Generative Multi-Robot Motion Planning via Diffusion Modeling with Multi-Agent Reinforcement Learning Guidance

2026-05-30 · Suk Ki Lee, Venkata Sai Deepak Mutta, Hyunwoong Ko arxiv

Coordinating multiple robots in shared environments requires generating feasible trajectories for each agent while accounting for interactions among agents. Centralized planning approaches become difficult to scale as th…

Multi-agent Reinforcement Learning Trajectory Planning Motion Planning

Anticipatory Planning for Multimodal AI Agents

2026-03-17 · Yongyuan Liang, Shijie Zhou, Yu Gu, Hao Tan 외 arxiv

Recent advances in multimodal agents have improved computer-use interaction and tool-usage, yet most existing systems remain reactive, optimizing actions in isolation without reasoning about future states or long-term go…

Reinforcement Learning

Efficient UAV Trajectory-Planning using Economic Reinforcement Learning

2021-03-03 · Alvi Ataur Khalil, Alexander J Byrne, Mohammad Ashiqur Rahman, Mohammad Hossein Manshaei

Advances in unmanned aerial vehicle (UAV) design have opened up applications as varied as surveillance, firefighting, cellular networks, and delivery applications. Additionally, due to decreases in cost, systems employin…

Multi-agent Reinforcement Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)+1

TRUAV: Distributed Multi-Agent Reinforcement Learning for Trajectory Planning and Routing Enhancement in UAV-Aided IoT-Enabled VANETs

2026-07-26 · Muhammad Umar Farooq Qaisar, Lin Zhang, Zhen Chen, Wajdy Othman 외 arxiv

Unmanned aerial vehicles (UAVs) have emerged as a key enabler of next-generation Internet of Things (IoT) ecosystems, offering flexible aerial relaying to extend connectivity across dynamic vehicular ad hoc networks (VAN…

Multi-agent Reinforcement Learning Trajectory Planning