グラフニューラルネットワーク強化学習による自動運転オンデマンド交通の制御
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
都市交通網をグラフとして捉え、グラフニューラルネットワークを用いた強化学習で自動運転オンデマンド車両の再配置を制御し、他都市へのゼロショット転移やサービスエリア拡張への高い汎化性能を示した論文。
著者: Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
分類: eess.SY, cs.LG, cs.RO, cs.SY
原文アブストラクト
Autonomous mobility-on-demand (AMoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of robotic, self-driving vehicles. Given a graph representation of the transportation network - one where, for example, nodes represent areas of the city, and edges the connectivity between them - we argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. In this paper, we propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. Crucially, we demonstrate that graph neural networks enable reinforcement learning agents to recover behavior policies that are significantly more transferable, generalizable, and scalable than policies learned through other approaches. Empirically, we show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks such as inter-city generalization, service area expansion, and adaptation to potentially complex urban topologies.
