  Vehicular Metaverses represent emerging paradigms arising from the
convergence of vehicle road cooperation, Metaverse, and augmented intelligence
of things. Users engaging with Vehicular Metaverses (VMUs) gain entry by
consistently updating their Vehicular Twins (VTs), which are deployed on
RoadSide Units (RSUs) in proximity. The constrained RSU coverage and the
consistently moving vehicles necessitate the continuous migration of VTs
between RSUs through vehicle road cooperation, ensuring uninterrupted immersion
services for VMUs. Nevertheless, the VT migration process faces challenges in
obtaining adequate bandwidth resources from RSUs for timely migration, posing a
resource trading problem among RSUs. In this paper, we tackle this challenge by
formulating a game-theoretic incentive mechanism with multi-leader
multi-follower, incorporating insights from social-awareness and queueing
theory to optimize VT migration. To validate the existence and uniqueness of
the Stackelberg Equilibrium, we apply the backward induction method.
Theoretical solutions for this equilibrium are then obtained through the
Alternating Direction Method of Multipliers (ADMM) algorithm. Moreover, owing
to incomplete information caused by the requirements for privacy protection, we
proposed a multi-agent deep reinforcement learning algorithm named MALPPO.
MALPPO facilitates learning the Stackelberg Equilibrium without requiring
private information from others, relying solely on past experiences.
Comprehensive experimental results demonstrate that our MALPPO-based incentive
mechanism outperforms baseline approaches significantly, showcasing rapid
convergence and achieving the highest reward.
