Generative Flow Networks as Entropy-Regularized RL

• URL: http://arxiv.org/abs/2310.12934v3
• Date: Sun, 25 Feb 2024 19:39:24 GMT
• Title: Generative Flow Networks as Entropy-Regularized RL
• Authors: Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry Vetrov
• Abstract summary: generative flow networks (GFlowNets) are a method of training a policy to sample compositional objects with proportional probabilities to a given reward via a sequence of actions. We demonstrate how the task of learning a generative flow network can be efficiently as an entropy-regularized reinforcement learning problem. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods.
• Score: 4.857649518812728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating RL principles into the realm of generative flow networks.

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