Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
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DOI：
10.1109/itsc48978.2021.9565029
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发表时间：
2020-11
期刊：
影响因子：
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通讯作者：
Juhyeon Kim
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文献类型：
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作者：
Juhyeon Kim
本文提出了一种将多智能体强化学习与图神经网络相结合的优化车队管理的新方法。提供网约车服务，需要对空间域的动态资源和需求进行优化。虽然空间结构以前是用规则网格近似的，但我们的方法用图形表示道路网络，这更好地反映了底层的几何结构。动态资源分配被表述为多智能体强化学习，其动作值函数（Q函数）用图神经网络逼近。我们利用深度q网络（deep Q-networks， DQN）在图上使用随机策略更新规则，取得了比贪婪策略更新更好的结果。我们设计了一个真实的模拟器来模拟经验出租车呼叫数据，并在各种条件下验证了所提出模型的有效性。
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.