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

Understand

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.