arXiv Machine Learning June 2, 2026 By Matej Jusup, Kenan Zhang, Zhiyuan Hu, Barna P\'asztor, Andreas Krause, Francesco Corman

Scalable Ride-Sourcing Vehicle Rebalancing with Service Accessibility Guarantee: A Constrained Mean-Field Reinforcement Learning Approach

Read the original on arXiv Machine Learning →

arXiv:2503. 24183v3 Announce Type: replace Abstract: The expansion of ride-sourcing services such as Uber and Lyft has reshaped urban transportation by offering flexible, on-demand mobility via mobile applications.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 2

Multi-Objective Reinforcement Learning for Tactical Decision Making for Trucks in Highway Traffic

arXiv:2601. 18783v2 Announce Type: replace-cross Abstract: Balancing safety, efficiency, and operational costs in highway driving poses a challenging decision-making problem for heavy-duty vehicles.

By Deepthi Pathare, Leo Laine, Morteza Haghir Chehreghani

agents reinforcement-learning safety

arXiv AI

Jul 22

Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority

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By Philip-Roman Adam, Stefanie Schmidtner

reinforcement-learning

arXiv AI

Jun 18

A Distributionally Robust Reinforcement Learning Framework for Constrained Urban EV Dispatch

arXiv:2604. 25848v2 Announce Type: replace Abstract: We study city-scale control of electric-vehicle (EV) ride-hailing fleets where dispatch, repositioning, and charging decisions must respect charger and feeder limits under uncertain, spatially correlated demand and travel times.

By An Nguyen, Hoang Nguyen, Phuong Le, Hung Pham, Cuong Do, Laurent El Ghaoui

agents reinforcement-learning

arXiv AI

Jul 7

Multi-Agent Reinforcement Learning for V2X Resource Allocation: Disentangling MARL Challenges Through Benchmarking

arXiv:2603. 06607v2 Announce Type: replace-cross Abstract: Radio resource allocation (RRA) is a critical function in cellular vehicle-to-everything (C-V2X) networks, where vehicles must share limited wireless resources to support safety-critical communications.

By Siyuan Wang, Lei Lei, Pranav Maheshwari, Sam Bellefeuille, Kan Zheng

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 21

A Deep Reinforcement Learning Algorithm for the Vehicle Routing Problem with Stochastic Demands and Outsourcing

arXiv:2607. 16875v1 Announce Type: cross Abstract: We introduce the vehicle routing problem with stochastic demands and outsourcing options (VRP-SDO), in which a logistics service provider partitions customer requests into customers outsourced to a common carrier and customers committed to its fixed fleet.

By Mohsen Dastpak, Fausto Errico, Ola Jabali

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arXiv AI

Jun 17

Learning Fair Pareto-Optimal Policies in Multi-Objective Reinforcement Learning

arXiv:2606. 18111v1 Announce Type: cross Abstract: Fairness is an important aspect of decision-making in multi-objective reinforcement learning (MORL), where policies must ensure both optimality and equity across multiple, potentially conflicting objectives.

By Umer Siddique, Peilang Li, Yongcan Cao

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