Beijing Didi Infinity Technology and Development Co., Ltd.

Chenxi Wang

Zhiwei Qin

Xiaocheng Tang

Yan Jiao

Multi-agent reinforcement learning may be used for rider order-dispatching via matching the distribution of orders and vehicles. Information may be obtained. The information may include a plurality of vehicle locations of a plurality of vehicles, a plurality of ride orders, and a current time. The obtained information may be input into a trained model. The trained model may be based on Kullback-Leibler divergence optimization and independent agents under a guidance of a joint policy. A plurality of order-dispatching tasks may be generated for the plurality of vehicles to fulfill.

Multi-agent reinforcement learning for order-dispatching via order-vehicle distribution matching

Systems and methods are provided for ride order dispatching and vehicle repositioning. A method for ride order dispatching and vehicle repositioning, comprises: obtaining information comprising a location of a vehicle, current orders, and a current time; inputting the obtained information to a trained model; and determining action information for the vehicle based on an output of the trained model, the action information comprising: re-positioning the vehicle or accepting a ride order. The model is configured with: receiving information of drivers and information of orders as inputs; obtaining a global state based on the information of drivers, the information of orders, and a global time; and querying a plurality of driver-order pairs and driver-reposition pairs based at least on the obtained global state to determine the action information as the output.

Growing community of inventors

Seattle, WA, United States of America

Chenxi Wang