arXiv Machine Learning

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

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.

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

arXiv:2607. 18286v1 Announce Type: cross Abstract: Transit signal priority (TSP) requires balancing competing objectives: reducing bus delay while limiting adverse impacts on non-bus traffic and avoiding extreme waits for a subset of vehicles.

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

reinforcement-learning fine-tuning benchmarks

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

reinforcement-learning benchmarks safety

arXiv Machine Learning

Aug 4

Hard Constraints, Smooth Gradients: Learning Feasible Inventory Policies via Differentiable Projection

arXiv:2608. 02343v1 Announce Type: cross Abstract: Many operational problems are constrained sequential decision processes with large, combinatorial action spaces and interdependent feasibility constraints.

By Patrick Helm, Jan-Niklas Doerr, Joren Gijsbrechts, Stefan Minner

rag reinforcement-learning benchmarks

arXiv Machine Learning

Jul 8

Deep Reinforcement Learning for Dynamic Origin-Destination Matrix Estimation in Microscopic Traffic Simulations Considering Credit Assignment

arXiv:2511. 06229v3 Announce Type: replace Abstract: This paper focuses on dynamic origin-destination matrix estimation (DODE), a crucial calibration process necessary for the effective application of microscopic traffic simulations.

By Donggyu Min, Seongjin Choi, Dong-Kyu Kim

agents reinforcement-learning

arXiv AI

1d ago

Drive, Pack, Fly: The Travelling Thief Problem with Drone

arXiv:2608. 16435v1 Announce Type: new Abstract: In collection operations, accumulating payload progressively slows the vehicle, imposing a cumulative penalty on routing efficiency.

By Kabir Murjani, Abhay Sobhanan

reinforcement-learning robotics benchmarks

arXiv Machine Learning

Aug 6

HCRide: Harmonizing Passenger Fairness and Driver Preference for Human-Centered Ride-Hailing

arXiv:2508. 04811v2 Announce Type: replace Abstract: Order dispatch systems play a vital role in ride-hailing services, which directly influence operator revenue, driver profit, and passenger experience.

By Lin Jiang, Yu Yang, Guang Wang

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 14

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.

By Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma

agents reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 12

Partially Observable Learning for Multi-Platform Dispatch Optimization

arXiv:2608. 10897v1 Announce Type: new Abstract: Instant delivery platforms have become a critical component of urban logistics, increasingly relying on crowdsourced couriers to fulfill highly dynamic orders.

By Fengming Yao, Man Luo

agents reinforcement-learning safety

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