首页|UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services:A Multi-Agent Deep Reinforcement Learning Approach

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services:A Multi-Agent Deep Reinforcement Learning Approach

扫码查看
原文链接
  • 万方数据
Avatars,as promising digital representations and service assistants of users in Metaverses,can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses.However,avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications,e.g.,augmented reality navigation,which consumes intensive computing resources.It is inefficient and impractical for vehicles to process avatar tasks locally.Fortu-nately,migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV)for execution is a promising solution to decrease computation overhead and reduce task pro-cessing latency,while the high mobility of vehicles brings chal-lenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status.To address these challenges,in this paper,we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL)to execute immersive vehicular avatar tasks dynamically.Specifically,we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms.We then design the multi-agent proximal policy optimization(MAPPO)approach as the MADRL algo-rithm for the avatar task migration problem.To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO,we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representa-tion of relationships among agents.Finally,to motivate terrestrial or non-terrestrial edge servers(e.g.,RSUs or UAVs)to share computation resources and ensure traceability of the sharing records,we apply smart contracts and blockchain technologies to achieve secure sharing management.Numerical results demon-strate that the proposed approach outperforms the MAPPO approach by around 2％and effectively reduces approximately 20％of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

Avatarblockchainmetaversesmulti-agent deep reinforcement learningtransformerUAVs

Jiawen Kang、Junlong Chen、Minrui Xu、Zehui Xiong、Yutao Jiao、Luchao Han、Dusit Niyato、Yongju Tong、Shengli Xie

展开 >

School of Automation,Guangdong University of Technology,Guangzhou 510006,China,111 Center for Intelligent Batch Manufacturing based on IoT Technology,Guangzhou 510006,China

School of Automation,Guangdong University of Technology,Guangzhou 510006,China,Key Laboratory of Intelligent Information Processing and System Integration of IoT,Ministry of Education,Guangzhou 510006,China

School of Computer Science and Engineering,Nanyang Technological University,Singapore,Singapore

Pillar of Information Systems Technology and Design,Singapore University of Technology and Design,Singapore,Singapore

College of Communication Engineering,Army Engineering University of PLA,Nanjing 210007,China

National Natural Science Foundation of China,Beijing 100085,China

School of Automation,Guangdong University of Technology,Guangzhou 510006,China,Key Laboratory of Intelligent Detection and IoT in Manufacturing,Ministry of Education,Guangzhou 510006,China

School of Automation,Guangdong University of Technology,Guangzhou 510006,China,Guangdong Key Laboratory of IoT Information Technology,Guangzhou 510006,China

展开 >

NSFCNSFCNSFCPearl River Talent Recruitment ProgramGuangzhou Basic Research ProgramNational Research Foundation,SingaporeInfocomm Media Development Authority under its Future Communications Research Development ProgrammeDSO National Laboratories under the AI Singapore Programme under AISG AwardEnergy Research Test-Bed and Industry Partnership Funding Initiative,Energy Grid(EG)2.0 programmeDesCartes and the Campus for Research Excellence and Technological Enterprise(CREATE)programmeMOE Tier 1Singapore University of Technology and Design(SUTD)SUTD-ZJU IDEAMinistry of Education,Singapore,through its SUTD Kickstarter Initiative

62102099U22A2054621015942021QN02S6432023A04J1699AISG2-RP-2020-019RG87/22SRG-ISTD-2021-165SUTD-ZJUVP202102SKI 20210204

2024

10.1109/JAS.2023.123993

自动化学报(英文版)
中国自动化学会,中国科学院自动化研究所,中国科技出版传媒股份有限公司

自动化学报(英文版)

CSTPCDEI
ISSN：2329-9266
年,卷(期)：2024.11(2)
  • 59