Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integration

TL;DR

This paper improves GFlowNet for reinforcement learning by integrating entropy and network structure, leading to more diverse high-reward solutions with better computational efficiency, demonstrated through experiments in molecule synthesis and hypergrid tasks.

Contribution

The paper introduces a new loss function and training objective for GFlowNet, enhancing candidate diversity and efficiency by leveraging entropy and network structure insights.

Findings

Refined GFlowNet outperforms traditional methods in diversity and efficiency.

Empirical validation on molecule synthesis and hypergrid tasks.

Demonstrates the importance of entropy and network structure in solution generation.

Abstract

Traditional reinforcement learning often struggles to generate diverse, high-reward solutions, especially in domains like drug design and black-box function optimization. Markov Chain Monte Carlo (MCMC) methods provide an alternative method of RL in candidate selection but suffer from high computational costs and limited candidate diversity exploration capabilities. In response, GFlowNet, a novel neural network architecture, was introduced to model complex system dynamics and generate diverse high-reward trajectories. To further enhance this approach, this paper proposes improvements to GFlowNet by introducing a new loss function and refining the training objective associated with sub-GFlowNet. These enhancements aim to integrate entropy and leverage network structure characteristics, improving both candidate diversity and computational efficiency. We demonstrated the superiority of the refined GFlowNet over traditional methods by empirical results from hypergrid experiments and molecule synthesis tasks. The findings underscore the effectiveness of incorporating entropy and exploiting network structure properties in solution generation in molecule synthesis as well as diverse experimental designs.