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How to optimally dispatch orders to vehicles and how to tradeoff betweenimmediate and future returns are fundamental questions for a typicalride-hailing platform. We model ride-hailing as a large-scale parallel rankingproblem and study the joint decision-making task of order dispatching and fleetmanagement in online ride-hailing platforms. This task brings unique challengesin the following four aspects. First, to facilitate a huge number of vehiclesto act and learn efficiently and robustly, we treat each region cell as anagent and build a multi-agent reinforcement learning framework. Second, tocoordinate the agents from different regions to achieve long-term benefits, weleverage the geographical hierarchy of the region grids to perform hierarchicalreinforcement learning. Third, to deal with the heterogeneous and variantaction space for joint order dispatching and fleet management, we design theaction as the ranking weight vector to rank and select the specific order orthe fleet management destination in a unified formulation. Fourth, to achievethe multi-scale ride-hailing platform, we conduct the decision-making processin a hierarchical way where a multi-head attention mechanism is utilized toincorporate the impacts of neighbor agents and capture the key agent in eachscale. The whole novel framework is named as CoRide. Extensive experimentsbased on multiple cities real-world data as well as analytic synthetic datademonstrate that CoRide provides superior performance in terms of platformrevenue and user experience in the task of city-wide hybrid order dispatchingand fleet management over strong baselines.

CoRide: Joint Order Dispatching and Fleet Management for Multi-Scale Ride-Hailing Platforms