A Dynamic Matching Time Strategy Based on Multi-Agent Reinforcement Learning in Ride-Hailing

Abstract

For online ride-hailing platforms, choosing the right time to match idle vehicles with passengers is one of the most important factors affecting the platform's profit. On one hand, vehicles and passengers arrive dynamically, and an appropriate delayed matching may generate a highly efficient matching result with more values. On the other hand, different regions may have different states of supply (vehicles) and demand (passengers), and the matching time should be different. At this moment, we need an efficient matching time strategy that takes into account matching time and regional differences to maximize the platform's long-term profit. In this paper, we propose a dynamic matching time algorithm based on multi-agent reinforcement learning, which is called Multi-Region Differentiated Matching Decision. Firstly, we describe the order matching process and then model it as a decentralized partially observable Markov decision process (Dec-POMDP). Secondly, considering that there are regional differences in supply and demand, we divide the overall area based on historical data and propose an algorithm based on multi-agent reinforcement learning to realize multiregion differentiated dynamic matching. Finally, we conduct extensive experiments to evaluate our matching algorithm against benchmark algorithms in a real-world dataset. The experimental results show that our algorithm can outperform benchmark algorithms.