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

04/23/2021
by   Daniele Gammelli, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/25/2022

Robust Reinforcement Learning on Graphs for Logistics optimization

Logistics optimization nowadays is becoming one of the hottest areas in ...
01/30/2020

Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks

Graphs can be used to represent and reason about real world systems. A v...
12/14/2018

Simulation to scaled city: zero-shot policy transfer for traffic control via autonomous vehicles

Using deep reinforcement learning, we train control policies for autonom...
11/16/2021

Route Optimization via Environment-Aware Deep Network and Reinforcement Learning

Vehicle mobility optimization in urban areas is a long-standing problem ...
08/04/2020

Reinforced Epidemic Control: Saving Both Lives and Economy

Saving lives or economy is a dilemma for epidemic control in most cities...
07/28/2021

Predictive and Prescriptive Performance of Bike-Sharing Demand Forecasts for Inventory Management

Bike-sharing systems are a rapidly developing mode of transportation and...
04/11/2022

Learning Object-Centered Autotelic Behaviors with Graph Neural Networks

Although humans live in an open-ended world and endlessly face new chall...

Code Repositories

gnn-rl-for-amod

Official implementation of "Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand


view repo