Cover Image

Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhao, Zeyuan, Li, Chaoran, Zhang, Shao, Wen, Ying
Format:	Preprint
Published:	2025
Subjects:	Robotics Artificial Intelligence Machine Learning Multiagent Systems
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Attention-Guided Search for Dense Multi-Agent Pathfinding
by: Jain, Rishabh, et al.
Published: (2025)

Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers
by: Krnjaic, Aleksandar, et al.
Published: (2022)

HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
by: Tang, Huijie, et al.
Published: (2024)

Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
by: Tang, Huijie, et al.
Published: (2024)

Learning Team-Based Navigation: A Review of Deep Reinforcement Learning Techniques for Multi-Agent Pathfinding
by: Chung, Jaehoon, et al.
Published: (2023)

SkyRover: A Modular Simulator for Cross-Domain Pathfinding
by: Ma, Wenhui, et al.
Published: (2025)

Pro-Routing: Proactive Routing of Autonomous Multi-Capacity Robots for Pickup-and-Delivery Tasks
by: Garces, Daniel, et al.
Published: (2025)

Optimal Integrated Task and Path Planning and Its Application to Multi-Robot Pickup and Delivery
by: Aryan, Aman, et al.
Published: (2024)

MARFT: Multi-Agent Reinforcement Fine-Tuning
by: Liao, Junwei, et al.
Published: (2025)

SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding
by: Liao, Shuhao, et al.
Published: (2025)

An Analysis of Constraint-Based Multi-Agent Pathfinding Algorithms
by: Lee, Hannah, et al.
Published: (2025)

Local Guidance for Configuration-Based Multi-Agent Pathfinding
by: Arita, Tomoki, et al.
Published: (2025)

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning
by: Zhang, Beining, et al.
Published: (2025)

Learning Multi-Agent Loco-Manipulation for Long-Horizon Quadrupedal Pushing
by: Feng, Yuming, et al.
Published: (2024)

Many-to-Many Multi-Agent Pickup and Delivery
by: Schneider, Ethan, et al.
Published: (2026)

Cross-Modal Navigation with Multi-Agent Reinforcement Learning
by: Liu, Shuo, et al.
Published: (2026)

Redistributing Rewards Across Time and Agents for Multi-Agent Reinforcement Learning
by: Kapoor, Aditya, et al.
Published: (2025)

Deep Reinforcement Learning for Multi-Agent Coordination
by: Aina, Kehinde O., et al.
Published: (2025)

Multi-Agent Path Finding with Real Robot Dynamics and Interdependent Tasks for Automated Warehouses
by: Lehoux-Lebacque, Vassilissa, et al.
Published: (2024)

Online Multi-Agent Pickup and Delivery with Task Deadlines
by: Makino, Hiroya, et al.
Published: (2024)

Multi-Agent Inverse Q-Learning from Demonstrations
by: Haynam, Nathaniel, et al.
Published: (2025)

Controlling Behavioral Diversity in Multi-Agent Reinforcement Learning
by: Bettini, Matteo, et al.
Published: (2024)

Pairwise is Not Enough: Hypergraph Neural Networks for Multi-Agent Pathfinding
by: Jain, Rishabh, et al.
Published: (2026)

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
by: Vyaltsev, Valeriy, et al.
Published: (2026)

MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
by: Andreychuk, Anton, et al.
Published: (2024)

POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding
by: Skrynnik, Alexey, et al.
Published: (2024)

Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Finding
by: Jiang, He, et al.
Published: (2024)

Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
by: Gerstgrasser, Matthias, et al.
Published: (2023)

ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning
by: Yu, Xin, et al.
Published: (2023)

Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm
by: Kim, Hyeonjun, et al.
Published: (2025)

Evaluating and Improving Graph-based Explanation Methods for Multi-Agent Coordination
by: Kailas, Siva, et al.
Published: (2025)

Superhuman Safe and Agile Racing through Multi-Agent Reinforcement Learning
by: Geles, Ismail, et al.
Published: (2026)

System Neural Diversity: Measuring Behavioral Heterogeneity in Multi-Agent Learning
by: Bettini, Matteo, et al.
Published: (2023)

Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning
by: Selmonaj, Ardian, et al.
Published: (2025)

Assigning Credit with Partial Reward Decoupling in Multi-Agent Proximal Policy Optimization
by: Kapoor, Aditya, et al.
Published: (2024)

Multi-Agent Inverse Reinforcement Learning in Real World Unstructured Pedestrian Crowds
by: Chandra, Rohan, et al.
Published: (2024)

Advancing Learnable Multi-Agent Pathfinding Solvers with Active Fine-Tuning
by: Andreychuk, Anton, et al.
Published: (2025)

Reliable and Efficient Multi-Agent Coordination via Graph Neural Network Variational Autoencoders
by: Meng, Yue, et al.
Published: (2025)

Innate-Values-driven Reinforcement Learning based Cooperative Multi-Agent Cognitive Modeling
by: Yang, Qin
Published: (2024)

Schema-Guided Scene-Graph Reasoning based on Multi-Agent Large Language Model System
by: Chen, Yiye, et al.
Published: (2025)