Efficient Large-Scale Fleet Management via Multi-Agent Deep Reinforcement Learning

By Kaixiang Lin, Renyu Zhao, Zhe Xu, Jiayu Zhou. Knowledge Discovery and Data Mining, 2018

386 citations recorded by the source corpus.

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.

Subfields: Automotive Engineering, Control and Systems Engineering, Transportation

View Paper: https://www.semanticscholar.org/paper/332327c6d00a776fdcf77637be481effa86e6de0

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Citations in the corpus

Multi-agent deep reinforcement learning: a survey (2021, background): “The work of Lin et al. (2018) applied the principle of inverse reinforcement learning to the multi-agent setting.”
Multi-Agent Reinforcement Learning: A Review of Challenges and Applications (2021, methodology): “A multi-agent reinforcement learning framework was used to address the fleet management problem for large-scale online ride-sharing platforms such as Uber and Lift [61].”
Deep Reinforcement Learning for Multiagent Systems: A Review of Challenges, Solutions, and Applications (2018, methodology): “The large-scale fleet management problem is addressed in [106] using MADRL through two algorithms, namely, contextual deep Q-learning and contextual multiagent AC.”
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement Learning (2020, neutral): “There are also other works studying large-scale MARL recently (Lin et al., 2018; Jiang & Lu, 2018; Yang et al., 2018; Suarez et al., 2019), which typically simplify the problem by weight sharing and taking only local observations.”
A review of cooperative multi-agent deep reinforcement learning (2019, background): “In a related area, [114] consider ride-sharing management problem and propose a customized Q-learning and an actor-critic algorithm for this problem.”
Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends (2020, background): “While learning how to act is commonly done within simulations, it can be common for studies to use real-world data as calibration tools [101], [67].”
Transfer Learning in Deep Reinforcement Learning: A Survey (2020, methodology): “For example, Transportation Systems domain has adopted RL for addressing traffic congestion issues with better traffic signal scheduling and transportation resource allocation [8], [9], [169], [170].”
Artificial intelligence and innovation management: A review, framework, and research agenda✰ (2021, background): “One example of a real-world application of reinforcement learning is its use by DiDi Chuxing, China's biggest ride-hailing company (Lin et al., 2018; Qin et al., 2019).”
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning (2018, background): “While the framework of multi-agent reinforcement learning (MARL) [40, 17, 35, 9, 30] has been equipped with successful methods in deep reinforcement learning (RL) [20, 15] and shown promise on high-dimensional problems with complex agent interactions [19, 21, 13, 6, 16, 34], learning multi-agent coo”