Graph neural network reinforcement learning for autonomous mobility-on-demand systems
Autonomous mobility-on-demand (AMoD) systems represent a rapidly develo** mode of
transportation wherein travel requests are dynamically handled by a coordinated fleet of …
transportation wherein travel requests are dynamically handled by a coordinated fleet of …
Graph meta-reinforcement learning for transferable autonomous mobility-on-demand
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to
existing transportation paradigms, currently challenged by urbanization and increasing …
existing transportation paradigms, currently challenged by urbanization and increasing …
The+ 1 method: model-free adaptive repositioning policies for robotic multi-agent systems
Robotic multi-agent systems can efficiently handle spatially distributed tasks in dynamic
environments. Problem instances of particular interest, and generality are the dynamic …
environments. Problem instances of particular interest, and generality are the dynamic …
Data-driven h-infinity control with a real-time and efficient reinforcement learning algorithm: An application to autonomous mobility-on-demand systems
Reinforcement learning (RL) is a class of artificial intelligence algorithms being used to
design adaptive optimal controllers through online learning. This paper presents a model …
design adaptive optimal controllers through online learning. This paper presents a model …
Modeling, Analysis, and Control of Autonomous Mobility-on-Demand Systems: A Discrete-Time Linear Dynamical System and a Model Predictive Control Approach
Autonomous vehicles are rapidly evolving and will soon enable large-scale mobility-on-
demand (MoD) systems applications. Managing the fleets of available vehicles, commonly …
demand (MoD) systems applications. Managing the fleets of available vehicles, commonly …
A modular and transferable reinforcement learning framework for the fleet rebalancing problem
Mobility on demand (MoD) systems show great promise in realizing flexible and efficient
urban transportation. However, significant technical challenges arise from operational …
urban transportation. However, significant technical challenges arise from operational …
Learning to control autonomous fleets from observation via offline reinforcement learning
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 …
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …
A survey of machine learning-based ride-hailing planning
Ride-hailing is a sustainable transportation paradigm where riders access door-to-door
traveling services through a mobile phone application, which has attracted a colossal …
traveling services through a mobile phone application, which has attracted a colossal …
Maximum throughput dispatch for shared autonomous vehicles including vehicle rebalancing
J Robbennolt, MW Levin - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Shared autonomous vehicles (SAVs) provide on demand point-to-point transportation for
passengers. This service has been extensively studied using dispatch heuristics and agent …
passengers. This service has been extensively studied using dispatch heuristics and agent …
Implementing reinforcement learning for on-demand vehicle rebalancing in MATSim
In this paper, we present a software architecture that extends the MATSim mobility
simulation framework by providing an external rebalancing server, which offers a set of …
simulation framework by providing an external rebalancing server, which offers a set of …