3 citations · 6 across the 5 of their papers we have counts for
5 papers
Reproducibility in the Control of Autonomous Mobility-on-Demand Systems
Xinling Li, Meshal Alharbi, Daniele Gammelli +7
Autonomous Mobility-on-Demand (AMoD) systems, powered by advances in robotics, control, and Machine Learning (ML), offer a promising paradigm for future urban transportation. AMoD…
Multi-Agent Soft Actor-Critic with Coordinated Loss for Autonomous Mobility-on-Demand Fleet Control
Zeno Woywood, Jasper I. Wiltfang, Julius Luy +2
We study a sequential decision-making problem for a profit-maximizing operator of an autonomous mobility-on-demand system. Optimizing a central operator's vehicle-to-request dispat…
Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shifts
Tobias Enders, James Harrison, Maximilian Schiffer
We study the robustness of deep reinforcement learning algorithms against distribution shifts within contextual multi-stage stochastic combinatorial optimization problems from the…
Global Rewards in Multi-Agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
Heiko Hoppe, Tobias Enders, Quentin Cappart +1
We study vehicle dispatching in autonomous mobility on demand (AMoD) systems, where a central operator assigns vehicles to customer requests or rejects these with the aim of maximi…
Strategic Workforce Planning in Crowdsourced Delivery with Hybrid Driver Fleets
Julius Luy, Gerhard Hiermann, Maximilian Schiffer
Nowadays, logistics service providers (LSPs) increasingly consider using a crowdsourced workforce on the last mile to fulfill customers' expectations regarding same-day or on-deman…