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

TL;DR

This paper introduces a graph neural network-based reinforcement learning framework for controlling autonomous mobility-on-demand systems, demonstrating improved transferability, generalization, and scalability in complex urban transportation networks.

Contribution

It presents a novel deep reinforcement learning approach using graph neural networks for AMoD control, enhancing policy transferability and scalability.

Findings

Policies show strong zero-shot transfer capabilities

Effective in inter-city generalization and service area expansion

Scalable to complex urban topologies

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.