Coordinated control method for ridesharing service area using deep reinforcement learning

We propose a coordinated control method of agents, which are self-driving ridesharing vehicles, by using multi-agent deep reinforcement learning (MADRL) so that they individually determine where they should wait for passengers to provide better services as well as to increase their profits in rideshare services. With the increasing demand for ridesharing services, many drivers and passengers have started to participate. However, many drivers spend most of their operating time with empty vehicles, which is not only inefficient but also causes problems such as wasted energy, increased traffic congestion in urban areas, and shortages of ridesharing vehicles in less demand areas. To address this issue, distributed service area adaptation method for ride sharing (dSAAMS), in which agents learn where they should wait using MADRL, was already proposed, but we found that it does not work well under certain environments. Therefore, we propose dSAAMS* with modified input and improved reward scheme for agents to generate coordinated behaviors to adapt to various environments. Then, we evaluated the performance and charac-teristics of the proposed method by using a simulation environment with varying passenger generation patterns and real data in Manhattan. Our results indicate that the dSAAMS* provides better quality service than the conventional methods and performs better in dynamically changing environments.