GFlowNet Training by Policy Gradients

• URL: http://arxiv.org/abs/2408.05885v1
• Date: Mon, 12 Aug 2024 01:24:49 GMT
• Title: GFlowNet Training by Policy Gradients
• Authors: Puhua Niu, Shili Wu, Mingzhou Fan, Xiaoning Qian,
• Abstract summary: We propose a new GFlowNet training framework, with policy-dependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL) This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL.
• Score: 11.02335801879944
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative Flow Networks (GFlowNets) have been shown effective to generate combinatorial objects with desired properties. We here propose a new GFlowNet training framework, with policy-dependent rewards, that bridges keeping flow balance of GFlowNets to optimizing the expected accumulated reward in traditional Reinforcement-Learning (RL). This enables the derivation of new policy-based GFlowNet training methods, in contrast to existing ones resembling value-based RL. It is known that the design of backward policies in GFlowNet training affects efficiency. We further develop a coupled training strategy that jointly solves GFlowNet forward policy training and backward policy design. Performance analysis is provided with a theoretical guarantee of our policy-based GFlowNet training. Experiments on both simulated and real-world datasets verify that our policy-based strategies provide advanced RL perspectives for robust gradient estimation to improve GFlowNet performance.

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