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arXiv · 2312.15246

A Theory of Non-Acyclic Generative Flow Networks

Leo Maxime Brunswic·Yinchuan Li·Yushun Xu·Shangling Jui·Lizhuang Ma
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Abstract

GFlowNets is a novel flow-based method for learning a stochastic policy to generate objects via a sequence of actions and with probability proportional to a given positive reward. We contribute to relaxing hypotheses limiting the application range of GFlowNets, in particular: acyclicity (or lack thereof). To this end, we extend the theory of GFlowNets on measurable spaces which includes continuous state spaces without cycle restrictions, and provide a generalization of cycles in this generalized context. We show that losses used so far push flows to get stuck into cycles and we define a family of losses solving this issue. Experiments on graphs and continuous tasks validate those principles.

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BibTeXRIS

Leo Maxime Brunswic, Yinchuan Li, Yushun Xu, Shangling Jui, Lizhuang Ma. 2023-12-23. A Theory of Non-Acyclic Generative Flow Networks. https://doi.org/10.1609/aaai.v38i10.28989

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