  Integrating unmanned aerial vehicles (UAVs) into vehicular networks have
shown high potentials in affording intensive computing tasks. In this paper, we
study the digital twin driven vehicular edge computing networks for adaptively
computing resource management where an unmanned aerial vehicle (UAV) named
FlexEdge acts as a flying server. In particular, we first formulate an energy
consumption minimization problem by jointly optimizing UAV trajectory and
computation resource under the practical constraints. To address such a
challenging problem, we then build the computation offloading process as a
Markov decision process and propose a deep reinforcement learning-based
proximal policy optimization algorithm to dynamically learn the computation
offloading strategy and trajectory design policy. Numerical results indicate
that our proposed algorithm can achieve quick convergence rate and
significantly reduce the system energy consumption.
