Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network

• URL: http://arxiv.org/abs/2011.06175v2
• Date: Fri, 6 Aug 2021 02:34:33 GMT
• Title: Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
• Authors: Juhyeon Kim, Kihyun Kim
• Abstract summary: We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network. We design a realistic simulator that emulates the empirical taxi call data, and confirm the effectiveness of the proposed model under various conditions.
• Score: 0.8702432681310401
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a novel approach to optimize fleet management by combining multi-agent reinforcement learning with graph neural network. To provide ride-hailing service, one needs to optimize dynamic resources and demands over spatial domain. While the spatial structure was previously approximated with a regular grid, our approach represents the road network with a graph, which better reflects the underlying geometric structure. Dynamic resource allocation is formulated as multi-agent reinforcement learning, whose action-value function (Q function) is approximated with graph neural networks. We use stochastic policy update rule over the graph with deep Q-networks (DQN), and achieve superior results over the greedy policy update. We design a realistic simulator that emulates the empirical taxi call data, and confirm the effectiveness of the proposed model under various conditions.

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