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

Siyi He

TL;DR

This work enhances GFlowNet-based reinforcement learning by integrating network-structure awareness through sub-GFlowNet and an entropy-weighted loss. By decomposing the loss across subgraphs and weighting by subflow entropy, the approach aims to improve candidate diversity and training efficiency. Empirical results in hypergrid environments show accelerated convergence and better alignment with the target distribution, while molecule synthesis tasks reveal a favorable trade-off between rapid reward attainment and diversity, depending on the objective. The study advances flow-based generative modeling by linking DAG structure to training dynamics and suggesting future directions for scalable, conditional sub-GFlowNets.

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.

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

TL;DR

This work enhances GFlowNet-based reinforcement learning by integrating network-structure awareness through sub-GFlowNet and an entropy-weighted loss. By decomposing the loss across subgraphs and weighting by subflow entropy, the approach aims to improve candidate diversity and training efficiency. Empirical results in hypergrid environments show accelerated convergence and better alignment with the target distribution, while molecule synthesis tasks reveal a favorable trade-off between rapid reward attainment and diversity, depending on the objective. The study advances flow-based generative modeling by linking DAG structure to training dynamics and suggesting future directions for scalable, conditional sub-GFlowNets.

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.
Paper Structure (11 sections, 28 equations, 4 figures)

This paper contains 11 sections, 28 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Data Structure
  4. Problem Set-up
  5. Model
  6. Sub-GFlowNet
  7. Sub-GFlowNet Loss
  8. Experiment
  9. Numerical Stimulation
  10. Real Data
  11. Discussion

Figures (4)

  • Figure 1: Comparison of L1 Distance Between Empirical and True Distributions as Horizon Varies for GFlowNet with Different Loss Functions in dim=2
  • Figure 2: Comparison of L1 Distance Between Empirical and True Distributions as Horizon Varies for GFlowNet with Different Loss Functions in dim=3
  • Figure 3: Comparison of L1 Distance Between Empirical and True Distributions as Horizon Varies for GFlowNet with Different Loss Functions in dim=4
  • Figure 4: Comparative Evolution of Performance Metrics in GFlowNet under Three Different Training Objectives over 200,000 Iterations