mlsanityData to 7 Oct 2026

Multi-Agent Pathfinding: Definitions, Variants, and Benchmarks

arXiv 1906.08291 ↗First posted 19 Jun 2019cs.AI cs.MA cs.ROcited by 197

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Abstract (arXiv)

The MAPF problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.

Key facts. Cited by 197 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Jun 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 24; 2022: 22; 2023: 27; 2024: 44; 2025: 47; 2026: 33.

197 papers.

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  1. 2022LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfindingcited by 50
  2. 2024Multi-Robot Motion Planning with Diffusion Modelscited by 37
  3. 2025Simultaneous Multi-Robot Motion Planning with Projected Diffusion Modelscited by 37
  4. 2025Multi-agent Embodied AI: Advances and Future Directionscited by 36
  5. 2021Learning Selective Communication for Multi-Agent Path Findingcited by 32
  6. 2021Distributed Heuristic Multi-Agent Path Finding with Communicationcited by 31
  7. 2024No Regrets: Investigating and Improving Regret Approximations for Curriculum Discoverycited by 29
  8. 2023Learn to Follow: Decentralized Lifelong Multi-agent Pathfinding via Planning and Learningcited by 27
  9. 2024Why Solving Multi-agent Path Finding with Large Language Model has not Succeeded Yetcited by 22
  10. 2021Pairwise Symmetry Reasoning for Multi-Agent Path Finding Searchcited by 21
  11. 2024POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfindingcited by 20
  12. 2022Multi-Agent Path Finding with Prioritized Communication Learningcited by 17
38 more
  1. 2023Improving LaCAM for Scalable Eventually Optimal Multi-Agent Pathfindingcited by 17
  2. 2023Multi-Robot Coordination and Layout Design for Automated Warehousingcited by 17
  3. 2024MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scalecited by 17
  4. 2023Traffic Flow Optimisation for Lifelong Multi-Agent Path Findingcited by 14
  5. 2024Improving Learnt Local MAPF Policies with Heuristic Searchcited by 14
  6. 2025A Survey on Agent Workflow -- Status and Futurecited by 14
  7. 2021Beyond Robustness: A Taxonomy of Approaches towards Resilient Multi-Robot Systemscited by 12
  8. 2022Answer Set Planning: A Surveycited by 12
  9. 2022Multi-Goal Multi-Agent Pickup and Deliverycited by 12
  10. 2024Guidance Graph Optimization for Lifelong Multi-Agent Path Findingcited by 12
  11. 2023Decentralized Monte Carlo Tree Search for Partially Observable Multi-agent Pathfindingcited by 11
  12. 2021Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid Worldcited by 10
  13. 2024Work Smarter Not Harder: Simple Imitation Learning with CS-PIBT Outperforms Large Scale Imitation Learning for MAPFcited by 10
  14. 2025Discrete-Guided Diffusion for Scalable and Safe Multi-Robot Motion Planningcited by 10
  15. 2021Improving Continuous-time Conflict Based Searchcited by 9
  16. 2021Subdimensional Expansion for Multi-objective Multi-agent Path Findingcited by 9
  17. 2023Monte-Carlo Tree Search for Multi-Agent Pathfinding: Preliminary Resultscited by 9
  18. 2023Arbitrarily Scalable Environment Generators via Neural Cellular Automatacited by 9
  19. 2024Scaling Lifelong Multi-Agent Path Finding to More Realistic Settings: Research Challenges and Opportunitiescited by 9
  20. 2021MS*: A New Exact Algorithm for Multi-agent Simultaneous Multi-goal Sequencing and Path Findingcited by 8
  21. 2021Revisiting the Complexity Analysis of Conflict-Based Search: New Computational Techniques and Improved Boundscited by 8
  22. 2021Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidancecited by 8
  23. 2022Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigationcited by 8
  24. 2024Multi-Agent Motion Planning For Differential Drive Robots Through Stationary State Searchcited by 8
  25. 2025SRMT: Shared Memory for Multi-agent Lifelong Pathfindingcited by 8
  26. 2021Multi-Objective Path-Based D* Litecited by 7
  27. 2022Graph-Based Multi-Robot Path Finding and Planningcited by 7
  28. 2023Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood Searchcited by 7
  29. 2024Ensembling Prioritized Hybrid Policies for Multi-agent Pathfindingcited by 7
  30. 2021MAPFAST: A Deep Algorithm Selector for Multi Agent Path Finding using Shortest Path Embeddingscited by 6
  31. 2021Coordinated Multi-Agent Pathfinding for Drones and Trucks over Road Networkscited by 6
  32. 2022CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spacescited by 6
  33. 2022POGEMA: Partially Observable Grid Environment for Multiple Agentscited by 6
  34. 2022DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environmentscited by 6
  35. 2023SACHA: Soft Actor-Critic with Heuristic-Based Attention for Partially Observable Multi-Agent Path Findingcited by 6
  36. 2023Bidirectional Temporal Plan Graph: Enabling Switchable Passing Orders for More Efficient Multi-Agent Path Finding Plan Executioncited by 6
  37. 2024Deploying Ten Thousand Robots: Scalable Imitation Learning for Lifelong Multi-Agent Path Findingcited by 6
  38. 2021Symmetry Breaking for k-Robust Multi-Agent Path Findingcited by 5

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