Awesome
Generative Augmented Flow Networks
This repository is the implementation of Generative Augmented Flow Networks in ICLR 2023 (Spotlight). This codebase is based on the open-source gflownet implementation, and please refer to that repo for more documentation.
Citing
If you used this code in your research or found it helpful, please consider citing our paper:
@inproceedings{
pan2023generative,
title={Generative Augmented Flow Networks},
author={Ling Pan and Dinghuai Zhang and Aaron Courville and Longbo Huang and Yoshua Bengio},
booktitle={International Conference on Learning Representations},
year={2023},
url={https://openreview.net/forum?id=urF_CBK5XC0}
}
Requirements
Grid
- python: 3.6
- torch: 1.3.0
- scipy: 1.5.4
- numpy: 1.19.5
- tdqm
Molecule discovery
Please check the gflownet repo for more details about the environment
Usage
Please follow the instructions below to replicate the results in the paper.
- Grid
python toy_grid_dag.py --augmented 1 --seed <SEED> --horizon <HORIZON>
- Molecule discovery
python gflownet.py --w_ri 1