  In this paper, we investigate the computational resource allocation problem
in a distributed Ad-Hoc vehicular network with no centralized infrastructure
support. To support the ever increasing computational needs in such a vehicular
network, the distributed virtual cloud network (VCN) is formed, based on which
a computational resource sharing scheme through offloading among nearby
vehicles is proposed. In view of the time-varying computational resource in
VCN, the statistical distribution characteristics for computational resource
are analyzed in detail. Thereby, a resource-aware combinatorial optimization
objective mechanism is proposed. To alleviate the non-stationary environment
caused by the typically multi-agent environment in VCN, we adopt a centralized
training and decentralized execution framework. In addition, for the objective
optimization problem, we model it as a Markov game and propose a DRL based
multi-agent deep deterministic reinforcement learning (MADDPG) algorithm to
solve it. Interestingly, to overcome the dilemma of lacking a real central
control unit in VCN, the allocation is actually completed on the vehicles in a
distributed manner. The simulation results are presented to demonstrate our
scheme's effectiveness.
