Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning

1 code implementation • 9 Nov 2023 • Aaryan Singhal, Daniele Gammelli, Justin Luke, Karthik Gopalakrishnan, Dominik Helmreich, Marco Pavone

Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make several real-time decisions such as matching available vehicles to ride requests, rebalancing idle vehicles to areas of high demand, and charging vehicles to ensure sufficient range.

Graph Reinforcement Learning for Network Control via Bi-Level Optimization

1 code implementation • 16 May 2023 • Daniele Gammelli, James Harrison, Kaidi Yang, Marco Pavone, Filipe Rodrigues, Francisco C. Pereira

Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems.

reinforcement-learning

Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning

2 code implementations • 28 Feb 2023 • Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira, Filipe Rodrigues

Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in which a centrally coordinated fleet of self-driving vehicles dynamically serves travel requests.

Offline RL reinforcement-learning +1

Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand

1 code implementation • 15 Feb 2022 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone

Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel needs.

Meta Reinforcement Learning reinforcement-learning +1

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

1 code implementation • 28 Jul 2021 • Daniele Gammelli, Yihua Wang, Dennis Prak, Filipe Rodrigues, Stefan Minner, Francisco Camara Pereira

Bike-sharing systems are a rapidly developing mode of transportation and provide an efficient alternative to passive, motorized personal mobility.

Decision Making Management

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

1 code implementation • 23 Apr 2021 • Daniele Gammelli, Kaidi Yang, James Harrison, Filipe Rodrigues, Francisco C. Pereira, Marco Pavone

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.

Decision Making reinforcement-learning +1

Generalized Multi-Output Gaussian Process Censored Regression

1 code implementation • 10 Sep 2020 • Daniele Gammelli, Kasper Pryds Rolsted, Dario Pacino, Filipe Rodrigues

When modelling censored observations, a typical approach in current regression methods is to use a censored-Gaussian (i. e. Tobit) model to describe the conditional output distribution.

regression Stochastic Optimization

Recurrent Flow Networks: A Recurrent Latent Variable Model for Density Modelling of Urban Mobility

1 code implementation • ICML Workshop INNF 2021 • Daniele Gammelli, Filipe Rodrigues

Mobility-on-demand (MoD) systems represent a rapidly developing mode of transportation wherein travel requests are dynamically handled by a coordinated fleet of vehicles.

Variational Inference

Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes

1 code implementation • 21 Jan 2020 • Daniele Gammelli, Inon Peled, Filipe Rodrigues, Dario Pacino, Haci A. Kurtaran, Francisco C. Pereira

Transport demand is highly dependent on supply, especially for shared transport services where availability is often limited.

Gaussian Processes