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.

翻译：我们提出了一种优化车队管理的新办法,将多试剂强化学习与图形神经网络相结合。为了提供乘载服务,需要优化空间域域内的动态资源和需求。虽然空间结构以前与常规网格相近,但我们的方法是用图表代表公路网络,更好地反映基本的几何结构。动态资源分配是多试剂强化学习,其行动价值功能(Q功能)与图形神经网络相近。我们用深重Q网络(DQN)对图表采用随机政策更新规则,并取得优于贪婪政策更新的结果。我们设计了一个现实的模拟器,以效仿经验性出租车呼叫数据,并在各种条件下确认拟议模式的有效性。