Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning

•An vehicle routing and energy scheduling decision model is developed.•The proposed model enables a flexable energy sharing among a large number of EV’s.•The proposed algorithm outperforms heuristic algorithms in terms of solution quality.•The proposed model is more computational efficient than heuristic algorithms and CRL.•The proposed algorithm output less solution quality compared to CRL.