June 5, 2024, 4:43 a.m. | Haoran He, Emmanuel Bengio, Qingpeng Cai, Ling Pan

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02213v1 Announce Type: new
Abstract: The Generative Flow Network (GFlowNet) is a probabilistic framework in which an agent learns a stochastic policy and flow functions to sample objects with probability proportional to an unnormalized reward function. GFlowNets share a strong resemblance to reinforcement learning (RL), that typically aims to maximize reward, due to their sequential decision-making processes. Recent works have studied connections between GFlowNets and maximum entropy (MaxEnt) RL, which modifies the standard objective of RL agents by learning an …

abstract agent arxiv cs.lg flow framework function functions generative network objects policy probability reinforcement reinforcement learning sample stochastic type

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