| --- |
| license: mit |
| library_name: pytorch |
| tags: |
| - reinforcement-learning |
| - multi-agent-reinforcement-learning |
| - offline-rl |
| - flow-matching |
| - generative-models |
| - pytorch |
| - arxiv:2605.01457 |
| --- |
| |
| # CoFlow Checkpoints |
|
|
| Official checkpoints for **CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making**. |
|
|
| <p align="center"> |
| <a href="https://arxiv.org/abs/2605.01457"><img src="https://img.shields.io/badge/Paper-arXiv%3A2605.01457-b31b1b?style=for-the-badge&logo=arxiv" alt="Paper"></a> |
| <a href="https://guowei-zou.github.io/coflow/"><img src="https://img.shields.io/badge/Project-Page-4169e1?style=for-the-badge&logo=githubpages" alt="Project Page"></a> |
| <a href="https://github.com/Guowei-Zou/coflow-release"><img src="https://img.shields.io/badge/Code-GitHub-111111?style=for-the-badge&logo=github" alt="Code"></a> |
| <a href="#citation"><img src="https://img.shields.io/badge/Citation-BibTeX-ff8c00?style=for-the-badge" alt="Citation"></a> |
| </p> |
|
|
| CoFlow is a coordinated few-step generative model for offline multi-agent reinforcement learning. It combines Coordinated Velocity Attention with adaptive coordination gating so multi-agent actions can be generated in one to a few model calls while preserving inter-agent coordination. |
|
|
| ## Repository Contents |
|
|
| This repository contains the 120 checkpoints used in the paper: |
|
|
| - 30 task-quality configurations |
| - 4 model variants per configuration |
|
|
| The task-quality configurations cover: |
|
|
| - MPE: Spread, Tag, and World with `expert`, `medium-replay`, `medium`, and `random` data qualities |
| - SMAC: `3m`, `8m`, `2s3z`, and `5m_vs_6m` with `Good`, `Medium`, and `Poor` data qualities |
| - MA-MuJoCo: `2xAnt` and `4xAnt` with `Good`, `Medium`, and `Poor` data qualities |
|
|
| Model variants: |
|
|
| - `coflow-c`: CoFlow with centralized execution |
| - `coflow-d`: CoFlow with decentralized execution |
| - `coflow-base-c`: CoFlow-base with centralized execution |
| - `coflow-base-d`: CoFlow-base with decentralized execution |
|
|
| Each leaf directory contains one paper-used `state_*.pt` checkpoint. See `MANIFEST.tsv` for the mapping from paper configuration to source run, seed, checkpoint step, and file size. |
|
|
| ## Download |
|
|
| Download the full checkpoint release: |
|
|
| ```bash |
| hf download coflow-project/CoFlow-checkpoints --local-dir CoFlow-checkpoints |
| ``` |
|
|
| Download one configuration, for example MPE Spread Expert with CoFlow-C: |
|
|
| ```bash |
| hf download coflow-project/CoFlow-checkpoints \ |
| --include "mpe/simple_spread/expert/coflow-c/*" \ |
| --local-dir CoFlow-checkpoints |
| ``` |
|
|
| ## Usage |
|
|
| The checkpoints are intended to be used with the official code release: |
|
|
| ```bash |
| git clone https://github.com/Guowei-Zou/coflow-release.git |
| ``` |
|
|
| Please follow the setup, evaluation, and configuration instructions in the GitHub repository. The directory structure in this checkpoint repository is aligned with the paper task names and model variants. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{zou2026coflowcoordinatedfewstepflow, |
| title={CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making}, |
| author={Guowei Zou and Haitao Wang and Beiwen Zhang and Boning Zhang and Hejun Wu}, |
| year={2026}, |
| eprint={2605.01457}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.AI}, |
| url={https://arxiv.org/abs/2605.01457}, |
| } |
| ``` |
|
|
|
|
