Year: 2018
Type: conference-paper
Abstract: Large-scale online ride-sharing platforms have substantially transformed our lives by reallocating transportation resources to alleviate traffic congestion and promote transportation efficiency. An efficient fleet management strategy not only can significantly improve the utilization of transportation resources but also increase the revenue and customer satisfaction. It is a challenging task to design an effective fleet management strategy that can adapt to an environment involving complex dynamics between demand and supply. Existing studies usually work on a simplified problem setting that can hardly capture the complicated stochastic demand-supply variations in high-dimensional space. In this paper we propose to tackle the large-scale fleet management problem using reinforcement learning, and propose a contextual multi-agent reinforcement learning framework including two concrete algorithms, namely contextual deep Q-learning and contextual multi-agent actor-critic, to achieve explicit coordination among a large number of agents adaptive to different contexts. We show significant improvements of the proposed framework over state-of-the-art approaches through extensive empirical studies.
Authors: Kaixiang Lin, Renyu Zhao, Zhe Xu, Jiayu Zhou
Institutions: Michigan State University, Didi Chuxing (China)
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
Awards: G3197379068, III: Small: Collaborative Research: Structured Methods for Multi-Task Learning, G4504108201, G4651552421, G5395595088, CRII: III: Integrating Domain Knowledge via Interactive Multi-Task Learning, G880222665, CAREER: Harness the Big Data via Large-Scale Lifelong Learning, G8876996369
OpenAlex ID: W2789200469