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

Authors:
raw_author_name: Jiawen Kang
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Minrui Xu
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Zehui Xiong
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Yutao Jiao
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Dusit Niyato
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Yongju Tong
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006
raw_author_name: Shengli Xie
raw_affiliation_string: School of Automation, Guangdong University of Technology,Guangzhou,China,510006

Abstract: 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. Fortunately, 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 processing latency, while the high mobility of vehicles brings challenges 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 algorithm 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 representation 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 demonstrate 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.