Awesome
Stochastic Generative Flow Networks
This repository is the implementation of Stochastic Generative Flow Networks in UAI 2023 (Spotlight). This codebase is based on the open-source gflownet implementation and BioSeq-GFN-AL implementation, and please refer to those repos for more documentation.
Citing
If you used this code in your research or found it helpful, please consider citing our paper:
@inproceedings{
pan2023stochastic,
title={Stochastic Generative Flow Networks},
author={Ling Pan and Dinghuai Zhang and Moksh Jain and Longbo Huang and Yoshua Bengio},
booktitle={Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence}ce on Learning Representations},
year={2023},
url={https://proceedings.mlr.press/v216/pan23a.html}
}
Requirements
Grid
- python: 3.6
- torch: 1.3.0
- scipy: 1.5.4
- numpy: 1.19.5
- tdqm
Sequence
Please check the BioSeq-GFN-AL repo for more details about the environment.
Usage
Please follow the instructions below to replicate the results in the paper.
- Grid (in the grid folder)
python main.py --stick <STICK> --horizon <HORIZON> --seed <SEED>
- Sequence (in the tfb folder)
python run_tfbind.py --stick <STICK> --seed <SEED>