Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
Authors: Jiaoyang Li, Andrew Tinka, Scott Kiesel, Joseph W. Durham, T. K. Satish Kumar, Sven Koenig11272-11281
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically evaluate RHCR with a variety of MAPF solvers and show that it can produce high-quality solutions for up to 1,000 agents (= 38.9% of the empty cells on the map) for simulated warehouse instances, significantly outperforming existing work. |
| Researcher Affiliation | Collaboration | Jiaoyang Li,1 Andrew Tinka,2 Scott Kiesel,2 Joseph W. Durham,2 T. K. Satish Kumar,1 Sven Koenig1 1 University of Southern California 2 Amazon Robotics |
| Pseudocode | Yes | Algorithm 1: The low-level search for Windowed MAPF solvers generalizing Multi-Label A* (Grenouilleau, van Hoeve, and Hooker 2019). |
| Open Source Code | Yes | The code is available at https://github.com/Jiaoyang-Li/RHCR. |
| Open Datasets | Yes | We use the map in Figure 3a from (Liu et al. 2019). ... We use the map in Figure 3b. |
| Dataset Splits | No | The paper simulates environments and assigns tasks dynamically, but it does not specify explicit training, validation, or test dataset splits in the traditional sense. |
| Hardware Specification | Yes | We conduct all experiments on Amazon EC2 instances of type m4.xlarge with 16 GB memory. |
| Software Dependencies | No | The paper mentions software components like C++, SIPP, and SCIPP, and specific MAPF solvers (CBS, ECBS, CA*, PBS), but it does not provide version numbers for any of these dependencies. |
| Experiment Setup | Yes | For RHCR, we use time horizon w = 20 and replanning period h = 5. ... We simulate 5,000 timesteps for each experiment with potential function threshold p = 1. ... ECBS with suboptimality factor 1.1, CA* with random restarts. |