  With the increasing demand for multiple applications on internet of vehicles.
It requires vehicles to carry out multiple computing tasks in real time.
However, due to the insufficient computing capability of vehicles themselves,
offloading tasks to vehicular edge computing (VEC) servers and allocating
computing resources to tasks becomes a challenge. In this paper, a multi task
digital twin (DT) VEC network is established. By using DT to develop offloading
strategies and resource allocation strategies for multiple tasks of each
vehicle in a single slot, an optimization problem is constructed. To solve it,
we propose a multi-agent reinforcement learning method on the task offloading
and resource allocation. Numerous experiments demonstrate that our method is
effective compared to other benchmark algorithms.
