Multi-agent reinforcement learning for fleet management: a survey

Haoyang Chen; Zhuoming Li; Yuxin Yao

doi:10.1117/12.2641877

10 November 2022 Multi-agent reinforcement learning for fleet management: a survey

Haoyang Chen, Zhuoming Li, Yuxin Yao

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123482A (2022) https://doi.org/10.1117/12.2641877
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Fleet management has achieved great success benefiting from the application of deep reinforcement learning (DRL) in recent years and has yielded many successful commercial applications like ride-hailing services, whose basic goal is to efficiently manage the fleet of vehicles to meet the demand separated temporally and spatially. However, research that provides insight about how existing methods succeeded in dealing with massive agent interactions from a multi-agent perspective is still missing. In this paper, we review the RL methods of order dispatching and vehicle re-positioning in recent years, and classify them from the perspective of multi-agent reinforcement learning (MARL). We provide a comparison of vehicle-based methods, grid-based methods, and order-based methods, along with the popular datasets and open simulators. Afterward, we discuss several challenges and opportunities for the application of DRL in this domain.

Citation Download Citation

Haoyang Chen, Zhuoming Li, and Yuxin Yao "Multi-agent reinforcement learning for fleet management: a survey", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123482A (10 November 2022); https://doi.org/10.1117/12.2641877

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