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Generative Flow Networks (GFlowNets) are a new family of probabilisticsamplers where an agent learns a stochastic policy for generating complexcombinatorial structure through a series of decision-making steps. Despitebeing inspired from reinforcement learning, the current GFlowNet framework isrelatively limited in its applicability and cannot handle stochasticity in thereward function. In this work, we adopt a distributional paradigm forGFlowNets, turning each flow function into a distribution, thus providing moreinformative learning signals during training. By parameterizing each edge flowthrough their quantile functions, our proposed \textit{quantile matching}GFlowNet learning algorithm is able to learn a risk-sensitive policy, anessential component for handling scenarios with risk uncertainty. Moreover, wefind that the distributional approach can achieve substantial improvement onexisting benchmarks compared to prior methods due to our enhanced trainingalgorithm, even in settings with deterministic rewards.

Distributional GFlowNets with Quantile Flows