A Value-based Dynamic Learning Approach for Vehicle Dispatch in Ride-Sharing

To ensure real-time response to passengers, existing solutions to the vehicle dispatch problem typically optimize dispatch policies using small batch windows and ignore the spatial-temporal dynamics over the long-term horizon. In this paper, we focus on improving the long-term performance of ride-sharing services and propose a deep reinforcement learning based approach for the ride-sharing dispatch problem. In particular, this work includes: (1) an offline policy evaluation (OPE) based method to learn a value function that indicates the expected reward of a vehicle reaching a particular state; (2) an online learning procedure to update the offline trained value function to capture the real-time dynamics during the operation; (3) an efficient online dispatch method that optimizes the matching policy by considering both past and future influences. Extensive simulations are conducted based on New York City taxi data, and show that the proposed solution further increases the service rate compared to the state-of-the-art far-sighted ride-sharing dispatch approach.