A multi-agent reinforcement learning-based method for multiple electric vehicles charging scheduling

The multiple electric vehicles (EVs) charging scheduling problem is a challenging issue in the context of sustainable urban mobility. To address this complex problem, a mixed reward multi-agent deep deterministic policy gradient (MRMADDPG)- based method is proposed in this paper. The proposed MR-MADDPG provides a framework for multiple EVs to collaboratively and adaptively make charging decisions in a shared charging infrastructure. By learning and adapting from interactions with the charging environment, the MR-MADDPG empowers every EV within the fleet to make real-time charging decisions based on its local observation and eventually gets relatively low charging costs. This research contributes to the advancement of sustainable urban transportation by harnessing the capabilities of MRMADDPG, promoting efficient energy use and reducing charging cost of EV.