DRLPG: Reinforced Opponent-Aware Order Pricing for Hub Mobility Services

Zuohan Wu, Chen Jason Zhang, Han Yin, Rui Meng, Libin Zheng, Huaijie Zhu, Wei Liu

Abstract: A modern service model known as the “hub-oriented” model has emerged with the development of mobility services. This model allows users to request vehicles from multiple companies (agents) simultaneously through a unified entry (a ‘hub’). In contrast to conventional services, the “hub-oriented” model emphasizes pricing competition. To address this scenario, an agent should consider its competitors when developing its pricing strategy. In this paper, we introduce DRLPG, a mixed opponent-aware pricing method, which consists of two main components: the two-stage guarantor and the end-to-end deep reinforcement learning (DRL) module, as well as interaction mechanisms. In the guarantor, we design a prediction-decision framework. Specifically, we propose a new objective function for the spatiotemporal neural network in the prediction stage and utilize a traditional reinforcement learning method in the decision stage, respectively. In the end-to-end DRL framework, we explore the adoption of conventional DRL in the “hub-oriented” scenario. Finally, a meta-decider and an experience-sharing mechanism are proposed to combine both methods and leverage their advantages. We conduct extensive experiments on real data, and DRLPG achieves an average improvement of 99.9% and 61.1% in the peak and low peak periods, respectively. Our results demonstrate the effectiveness of our approach compared to the baseline.