@inproceedings{
lau2024qgfn,
title={{QGFN}: Controllable Greediness with Action Values},
author={Elaine Lau and Stephen Zhewen Lu and Ling Pan and Doina Precup and Emmanuel Bengio},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=kQ9LgM2JQT}
}
Generative Flow Networks (GFlowNets; GFNs) are a family of energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. However, consistently biasing GFNs towards producing high-utility samples is non-trivial. In this work, we leverage connections between GFNs and reinforcement learning (RL) and propose to combine the GFN policy with an action-value estimate, $Q$, to create greedier sampling policies which can be controlled by a mixing parameter. We show that several variants of the proposed method, QGFN, are able to improve on the number of high-reward samples generated in a variety of tasks without sacrificing diversity.
QGFN: Controllable Greediness with Action Values
Elaine Lau Stephen Zhewen Lu Ling Pan Doina Precup Emmanuel Bengio