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