InBEDE: Integrating Contextual Bandit with TD Learning for Joint Pricing and Dispatch of RideHailing Platforms For both the traditional streethailing taxi industry and the recently emerged online ridehailing, it has been a major challenge to improve the ridehailing marketplace efficiency due to spatiotemporal imbalance between the supply and demand, among other factors. Despite the numerous approaches to improve marketplace efficiency using pricing and dispatch strategies, they usually optimize pricing or dispatch separately. In this paper, we show that these two processes are in fact intrinsically interrelated. Motivated by this observation, we make an attempt to simultaneously optimize pricing and dispatch strategies. However, such a joint optimization is extremely challenging due to the inherent huge scale and lack of a uniform model of the problem. To handle the high complexity brought by the new problem, we propose InBEDE (Integrating contextual Bandit with tEmporal DiffErence learning), a learning framework where pricing strategies are learned via a contextual bandit algorithm, and the dispatch strategies are optimized with the help of temporal difference learning. The two learning components proceed in a mutual bootstrapping manner, in the sense that the policy evaluations of the two components are interdependent. Evaluated with realworld datasets of two Chinese cities from Didi Chuxing, an online ridehailing platform, we show that the market efficiency of the ridehailing platform can be significantly improved using InBEDE.

Deep Reinforcement Learning for Multidriver Vehicle Dispatching and Repositioning Problem Order dispatching and driver repositioning (also known as fleet management) in the face of spatially and temporally varying supply and demand are central to a ridesharing platform marketplace. Handcrafting heuristic solutions that account for the dynamics in these resource allocation problems is difficult, and may be better handled by an endtoend machine learning method. Previous works have explored machine learning methods to the problem from a highlevel perspective, where the learning method is responsible for either repositioning the drivers or dispatching orders, and as a further simplification, the drivers are considered independent agents maximizing their own reward functions. In this paper we present a deep reinforcement learning approach for tackling the full fleet management and dispatching problems. In addition to treating the drivers as individual agents, we consider the problem from a systemcentric perspective, where a central fleet management agent is responsible for decisionmaking for all drivers.