Joint Optimization of Pricing and Service Type in Ride-Hailing using Multi-agent Reinforcement Learning

Abstract

Ride-hailing systems often offer both private hailing and pooling ride services, and platform performance depends on both how drivers are allocated across these services and how each service is priced. However, most existing studies treat these decisions separately or focus on a single service type, either private hailing or pooling. This paper proposes a multi-agent reinforcement learning framework to jointly optimize pricing and service type in ride-hailing systems under endogenous rider choice. Specifically, we formulate the problem as a multiagent Markov Decision Process and train a pricing agent together with a service type agent using Proximal Policy Optimization in a realistic simulation environment. To ensure scalability across different fleet sizes, we employ a parameter-sharing architecture among service agents. Experiments on a realistic Delft road network demonstrate that the proposed framework learns stable and effective policies, consistently outperforming several rule-based baselines. The results highlight the substantial benefits of jointly optimizing pricing and service type in ride-hailing systems.