Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets

• URL: http://arxiv.org/abs/2406.01150v1
• Date: Mon, 3 Jun 2024 09:44:10 GMT
• Title: Looking Backward: Retrospective Backward Synthesis for Goal-Conditioned GFlowNets
• Authors: Haoran He, Can Chang, Huazhe Xu, Ling Pan,
• Abstract summary: Generative Flow Networks (GFlowNets) are amortized sampling methods for learning a policy to sequentially generate objects with probabilities to their rewards. GFlowNets exhibit a remarkable ability to generate diverse sets of high-reward proportional objects, in contrast to standard reinforcement learning approaches. Recent works have arisen for learning goal-conditioned GFlowNets to acquire various useful properties, aiming to train a single GFlowNet capable of achieving different goals as the task specifies. We propose a novel method named Retrospective Backward Synthesis (RBS) to address these challenges. Specifically, RBS synthesizes a new backward trajectory
• Score: 27.33222647437964
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative Flow Networks (GFlowNets) are amortized sampling methods for learning a stochastic policy to sequentially generate compositional objects with probabilities proportional to their rewards. GFlowNets exhibit a remarkable ability to generate diverse sets of high-reward objects, in contrast to standard return maximization reinforcement learning approaches, which often converge to a single optimal solution. Recent works have arisen for learning goal-conditioned GFlowNets to acquire various useful properties, aiming to train a single GFlowNet capable of achieving different goals as the task specifies. However, training a goal-conditioned GFlowNet poses critical challenges due to extremely sparse rewards, which is further exacerbated in large state spaces. In this work, we propose a novel method named Retrospective Backward Synthesis (RBS) to address these challenges. Specifically, RBS synthesizes a new backward trajectory based on the backward policy in GFlowNets to enrich training trajectories with enhanced quality and diversity, thereby efficiently solving the sparse reward problem. Extensive empirical results show that our method improves sample efficiency by a large margin and outperforms strong baselines on various standard evaluation benchmarks.

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