Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning
Kaixiang Lin (Michigan State University); Renyu Zhao (AI Labs, Didi Chuxing); Zhe Xu (AI Labs, Didi Chuxing); Jiayu Zhou (Michigan State University)
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.
How can we assist you?
We'll be updating the website as information becomes available. If you have a question that requires immediate attention, please feel free to contact us. Thank you!
If you are experiencing any issue related to registrations (confirmation, payment problem etc.) or have any questions regarding registrations, please do not submit this form. Please send an email to Kelly Hughes (firstname.lastname@example.org) or call 1.888.526.1242 or 303.530.4683.
Please enter the word you see in the image below: