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.

GFlowNets 是一种新型的基于流的方法，用于学习通过一系列动作以及与给定正向奖励成比例的概率来生成对象的随机策略。我们在放宽应用范围的前提下对 GFlowNets 的假设进行了改进，特别是解除了关于非环性的限制。为此，我们扩展了可测空间上的 GFlowNets 理论，其中包括了没有循环限制的连续状态空间，并在这个广义上下文中提供了循环的一般化。我们展示了迄今为止使用的损失函数会使流陷入循环，因此我们定义了一族能解决这个问题的损失函数。在图形和连续任务上进行的实验证实了这些原则。