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The emergence of on-demand ride pooling services allows each vehicle to servemultiple passengers at a time, thus increasing drivers' income and enablingpassengers to travel at lower prices than taxi/car on-demand services (only onepassenger can be assigned to a car at a time like UberX and Lyft). Althoughon-demand ride pooling services can bring so many benefits, ride poolingservices need a well-defined matching strategy to maximize the benefits for allparties (passengers, drivers, aggregation companies and environment), in whichthe regional dispatching of vehicles has a significant impact on the matchingand revenue. Existing algorithms often only consider revenue maximization,which makes it difficult for requests with unusual distribution to get a ride.How to increase revenue while ensuring a reasonable assignment of requestsbrings a challenge to ride pooling service companies (aggregation companies).In this paper, we propose a framework for vehicle dispatching for ride poolingtasks, which splits the city into discrete dispatching regions and uses thereinforcement learning (RL) algorithm to dispatch vehicles in these regions. Wealso consider the mutual information (MI) between vehicle and orderdistribution as the intrinsic reward of the RL algorithm to improve thecorrelation between their distributions, thus ensuring the possibility ofgetting a ride for unusually distributed requests. In experimental results on areal-world taxi dataset, we demonstrate that our framework can significantlyincrease revenue up to an average of 3\% over the existing best on-demand ridepooling method.

Mutual Information as Intrinsic Reward of Reinforcement Learning Agents for On-demand Ride Pooling