A Generative Flow Network (GFlowNet) implementation using TensorFlow 2.
GFlowNets were introduced by Bengio et. al. here:
Bengio E, Jain M, Korablyov M, Precup D, and Bengio Y. (2021) Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation. NeurIPS.
The Trajectory Balance loss function was introduced here:
Malkin N, Jain M, Bengio E, Sun C, and Bengio Y. (2022) Trajectory Balance: Improved Credit Assignment in GFlowNets. arXiv:2201.13259v1
Offline training is discussed here:
Moksh J, Bengio E, Garcia A-H, et al. (2022) Biological Sequence Design with GFlowNets. arXiv:2203.04115v1
To get started you'll want to install pipenv then run:
pipenv install
which will build an environment with tensorflow and all the dependencies you'll be needing to complete this tutorial.
To run the demonstration jupyter notebook:
pipenv shell
and then, once in the pipenv environment:
jupyter lab
Open the notebook gflownet_demo.ipynb and run all cells!
The notebook imports gfn.py which is the tensorflow2 implementation of a
generative flow network.
gfn.py in turn, imports env.py, an implemention of the simple cube reward
environment used in the original GFlowNet papers.