mlsanityData to 7 Oct 2026

The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications

arXiv 2203.07092 ↗First posted 14 Mar 2022cs.LG cs.MA stat.MLcited by 3

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Abstract (arXiv)

We study two state-of-the-art solutions to the multi-agent pickup and delivery (MAPD) problem based on different principles -- multi-agent path-finding (MAPF) and multi-agent reinforcement learning (MARL). Specifically, a recent MAPF algorithm called conflict-based search (CBS) and a current MARL algorithm called shared experience actor-critic (SEAC) are studied. While the performance of these algorithms is measured using quite different metrics in their separate lines of work, we aim to benchmark these two methods comprehensively in a simulated warehouse automation environment.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 21 of its 38 references that are in the map. First posted on arXiv 14 Mar 2022.

Builds on

21 of 38 references.

References · 21

  1. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  4. 2017Counterfactual Multi-Agent Policy Gradientscited by 733
  5. 2018QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 697
  6. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  7. 2020Shared Experience Actor-Critic for Multi-Agent Reinforcement Learningcited by 114
  8. 2018Multi-Agent Generative Adversarial Imitation Learningcited by 99
  9. 2018PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learningcited by 89
  10. 2021Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharingcited by 85
  11. 2018Searching with Consistent Prioritization for Multi-Agent Path Findingcited by 80
  12. 2020Lifelong Multi-Agent Path Finding in Large-Scale Warehousescited by 79
9 more
  1. 2017Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Taskscited by 62
  2. 2020PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelongcited by 61
  3. 2019Multi-Agent Adversarial Inverse Reinforcement Learningcited by 60
  4. 2018Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Deliverycited by 26
  5. 2021Multi-Agent Imitation Learning with Copulascited by 13
  6. 2017Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarioscited by 10
  7. 2021Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidancecited by 8
  8. 2020Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Searchcited by 7
  9. 2019Decentralized Multi-Agents by Imitation of a Centralized Controllercited by 5

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2024DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problemscited by 18
  2. 2025Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimizationcited by 1
  3. 2025Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehousecited by 0