POGEMA Benchmark

Welcome to the official repository for the POGEMA Benchmark. This is an umbrella repository that contains links and information about all the tools and algorithms related to the POGEMA Benchmark.

Repository Overview

This repository is organized into several key areas:

• Raw Data Directories: Contains evaluation results for baseline approaches. Details include:
  • raw_data_LMAPF folder: LMAPF results, including configurations and maps.
  • raw_data_MAPF folder: MAPF results, including configurations and maps.
  • Both directories feature YAML configuration files detailing the evaluation settings (number of agents, maps, seeds, episode length) and a maps.yaml file listing all the maps used in the evaluations.

Installation

POGEMA Environment

Install the POGEMA environment:

cd pogema
python setup.py install

POGEMA Toolbox

Install additional tools for POGEMA:

cd pogema-toolbox
python setup.py install

Algorithms

Explore integrated algorithms located in the algorithms directory:

• Navigate to the directory using:

  cd algorithms
  

• Install necessary dependencies:

  pip3 install -r docker/requirements.txt
  

• Optionally, build a Docker image to containerize the environment:

  cd docker && sh build.sh
  

  MAMBA baseline requires a separate Docker image:

  cd docker/mamba && sh build.sh
  

The following table contains links to the original repositories of all the integrated approaches:

Evaluation

Execute the evaluation script:

python eval.py

Contents at a Glance

.
├── algorithms
│   ├── Multiple algorithms for benchmarking
│   ├── Docker configuration for container setup
│   ├── eval.py for running evaluations
├── raw_data_LMAPF
│   ├── Data categorized by map types: Random, Mazes, Warehouse, etc.
├── raw_data_MAPF
│   ├── Similar categorization with specific map evaluations
└── README.md

Citation

If you use this repository in your research or wish to cite it, please make a reference to our paper:

@inproceedings{skrynnik2025pogema,
  title={POGEMA: A Benchmark Platform for Cooperative Multi-Agent Pathfinding},
  author={Skrynnik, Alexey and Andreychuk, Anton and Borzilov, Anatolii and Chernyavskiy, Alexander and Yakovlev, Konstantin and Panov, Aleksandr},
  booktitle={The Thirteenth International Conference on Learning Representations},
  year={2025}
}