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arXiv · 2402.05234

QGFN: Controllable Greediness with Action Values

Elaine Lau·Stephen Zhewen Lu·Ling Pan·Doina Precup·Emmanuel Bengio
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Abstract

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.

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Elaine Lau, Stephen Zhewen Lu, Ling Pan, Doina Precup, Emmanuel Bengio. 2024-02-07. QGFN: Controllable Greediness with Action Values. https://arxiv.org/abs/2402.05234

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