QGFN: Controllable Greediness with Action Values
<|reference_start|>QGFN: Controllable Greediness with Action Values: Generative Flow Networks (GFlowNets; GFNs) are a family of reward/energy-based generative methods for combinatorial objects, capable of generating diverse and high-utility samples. However, 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.<|reference_end|>
@article{lau2024qgfn:,
    title={QGFN: Controllable Greediness with Action Values},
    author={Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio},
    journal={arXiv preprint arXiv:2402.05234},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.05234},
    primaryClass={cs.LG}
}