  As a promising technology, vehicular edge computing (VEC) can provide
computing and caching services by deploying VEC servers near vehicles. However,
VEC networks still face challenges such as high vehicle mobility. Digital twin
(DT), an emerging technology, can predict, estimate, and analyze real-time
states by digitally modeling objects in the physical world. By integrating DT
with VEC, a virtual vehicle DT can be created in the VEC server to monitor the
real-time operating status of vehicles. However, maintaining the vehicle DT
model requires ongoing attention from the VEC server, which also needs to offer
computing services for the vehicles. Therefore, effective allocation and
scheduling of VEC server resources are crucial. This study focuses on a general
VEC network with a single VEC service and multiple vehicles, examining the two
types of delays caused by twin maintenance and computational processing within
the network. By transforming the problem using satisfaction functions, we
propose an optimization problem aimed at maximizing each vehicle's resource
utility to determine the optimal resource allocation strategy. Given the
non-convex nature of the issue, we employ multi-agent Markov decision processes
to reformulate the problem. Subsequently, we propose the twin maintenance and
computing task processing resource collaborative scheduling (MADRL-CSTC)
algorithm, which leverages multi-agent deep reinforcement learning. Through
experimental comparisons with alternative algorithms, it demonstrates that our
proposed approach is effective in terms of resource allocation.
