  Motivated by the promising advances of deep-reinforcement learning (DRL)
applied to cooperative multi-agent systems we propose a model and learning
procedure to solve the Capacitated Multi-Vehicle Routing Problem (CMVRP) with
fixed fleet size. Our learning procedure follows a centralized-training and
decentralized-execution paradigm. We empirically test our model and showed its
capability for producing near-optimal solutions through cooperative actions. In
large instances, our model generates better solutions than other commonly used
heuristics. Additionally, our model can solve arbitrary instances of the CMVRP
without requiring re-training.
