Related papers: Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems

Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems

URL: http://arxiv.org/abs/2104.11434v1
Date: Fri, 23 Apr 2021 06:42:38 GMT
Title: Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Authors: Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone
Abstract summary: We argue that the AMoD control problem is naturally cast as a node-wise decision-making problem. We propose a deep reinforcement learning framework to control the rebalancing of AMoD systems through graph neural networks. We show how the learned policies exhibit promising zero-shot transfer capabilities when faced with critical portability tasks.
Score: 42.08603087208381
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: 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.