Generative Flow Networks (or GFlowNets for short) are a family of probabilistic agents that learn to sample complex combinatorial structures through the lens of "inference as control". They have shown great potential in generating high-quality and diverse candidates from a given energy landscape. However, existing GFlowNets can be applied only to deterministic environments, and fail in more general tasks with stochastic dynamics, which can limit their applicability. To overcome this challenge, this paper introduces Stochastic GFlowNets, a new algorithm that extends GFlowNets to stochastic environments. By decomposing state transitions into two steps, Stochastic GFlowNets isolate environmental stochasticity and learn a dynamics model to capture it. Extensive experimental results demonstrate that Stochastic GFlowNets offer significant advantages over standard GFlowNets as well as MCMC- and RL-based approaches, on a variety of standard benchmarks with stochastic dynamics.

翻译：生成流网络（简称GFlowNets）是一类通过“推理即控制”视角学习采样复杂组合结构的概率智能体。它们在从给定能量景观中生成高质量、多样化候选解方面展现出巨大潜力。然而，现有GFlowNets仅适用于确定性环境，无法处理包含随机动力学的更通用任务，这限制了其应用范围。为克服这一挑战，本文提出随机生成流网络（Stochastic GFlowNets），这是一种将GFlowNets扩展至随机环境的新算法。通过将状态转移分解为两步，随机生成流网络隔离了环境随机性，并学习一个动力学模型来捕捉这种随机性。大量实验结果表明，在多个含随机动力学的标准基准任务中，随机生成流网络相较于标准GFlowNets、基于MCMC的方法以及基于强化学习的方法均具有显著优势。