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

arXiv: https://arxiv.org/abs/2203.07092 · HTML: https://mlsanity.com/paper/2203.07092/

## Key facts

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

## Builds on (most cited first, 21 of 21)

- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1803.11485/) (2018)
- [Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks](https://mlsanity.com/paper/2006.07869/) (2020)
- [Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2006.07169/) (2020)
- [Multi-Agent Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1807.09936/) (2018)
- [PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning](https://mlsanity.com/paper/1809.03531/) (2018)
- [Scaling Multi-Agent Reinforcement Learning with Selective Parameter Sharing](https://mlsanity.com/paper/2102.07475/) (2021)
- [Searching with Consistent Prioritization for Multi-Agent Path Finding](https://mlsanity.com/paper/1812.06356/) (2018)
- [Lifelong Multi-Agent Path Finding in Large-Scale Warehouses](https://mlsanity.com/paper/2005.07371/) (2020)
- [Lifelong Multi-Agent Path Finding for Online Pickup and Delivery Tasks](https://mlsanity.com/paper/1705.10868/) (2017)
- [PRIMAL2: Pathfinding via Reinforcement and Imitation Multi-Agent Learning -- Lifelong](https://mlsanity.com/paper/2010.08184/) (2020)
- [Multi-Agent Adversarial Inverse Reinforcement Learning](https://mlsanity.com/paper/1907.13220/) (2019)
- [Lifelong Path Planning with Kinematic Constraints for Multi-Agent Pickup and Delivery](https://mlsanity.com/paper/1812.06355/) (2018)
- [Multi-Agent Imitation Learning with Copulas](https://mlsanity.com/paper/2107.04750/) (2021)
- [Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios](https://mlsanity.com/paper/1702.05515/) (2017)
- [Cooperative Multi-Agent Path Finding: Beyond Path Planning and Collision Avoidance](https://mlsanity.com/paper/2105.10993/) (2021)
- [Learning to Resolve Conflicts for Multi-Agent Path Finding with Conflict-Based Search](https://mlsanity.com/paper/2012.06005/) (2020)
- [Decentralized Multi-Agents by Imitation of a Centralized Controller](https://mlsanity.com/paper/1902.02311/) (2019)

## Built on by (most cited first, 3 of 3)

- [DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems](https://mlsanity.com/paper/2405.17272/) (2024)
- [Multi-Agent Reinforcement Learning with Long-Term Performance Objectives for Service Workforce Optimization](https://mlsanity.com/paper/2503.01069/) (2025)
- [Sequence Pathfinder for Multi-Agent Pickup and Delivery in the Warehouse](https://mlsanity.com/paper/2509.23778/) (2025)

## 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.