44 citations · 59 across the 27 of their papers we have counts for
5 papers · 1 filter
Multi-Timescale Model Predictive Control for Slow-Fast Systems
Lukas Schroth, Daniel Morton, Amon Lahr +3
Model Predictive Control (MPC) has established itself as the primary methodology for constrained control, enabling autonomy across diverse applications. While model fidelity is cru…
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning
Aaryan Singhal, Daniele Gammelli, Justin Luke +3
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…
Learning to Control Autonomous Fleets from Observation via Offline Reinforcement Learning
Carolin Schmidt, Daniele Gammelli, Francisco Camara Pereira +1
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 reques…
Graph Meta-Reinforcement Learning for Transferable Autonomous Mobility-on-Demand
Daniele Gammelli, Kaidi Yang, James Harrison +3
Autonomous Mobility-on-Demand (AMoD) systems represent an attractive alternative to existing transportation paradigms, currently challenged by urbanization and increasing travel ne…
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand Systems
Daniele Gammelli, Kaidi Yang, James Harrison +3
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…