AAAI Conference 2024 Conference Paper
A Theory of Non-acyclic Generative Flow Networks
- Leo Brunswic
- Yinchuan Li
- Yushun Xu
- Yijun Feng
- Shangling Jui
- Lizhuang Ma
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.