论文标题
使用图形神经网络使用多代理深入学习在道路网络上优化大规模的车队管理
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural Network
论文作者
论文摘要
我们提出了一种新颖的方法，通过将多代理增强学习与图神经网络相结合，以优化车队管理。为了提供乘车服务，需要优化对空间域的动态资源和需求。尽管空间结构以前是用常规网格近似近似的，但我们的方法代表了带有图的路网，这更好地反映了基础的几何结构。动态资源分配是作为多代理增强学习的，其动作值函数（Q函数）与图神经网络近似。我们使用Deep Q-Networks（DQN）对图表使用随机策略更新规则，并在贪婪的策略更新中实现了优越的结果。我们设计了一个逼真的模拟器，该模拟器模仿经验出租车的数据，并在各种条件下确认拟议模型的有效性。
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.
