通过融合熵与子网络结构，提升强化学习解的多样性与效率。
Enhancing Solution Efficiency in Reinforcement Learning: Leveraging Sub-GFlowNet and Entropy Integration
- 引入新损失函数优化子GFlowNet训练目标
- 在分子合成任务中实现更高多样性与更快收敛
- 适合药物设计与黑箱优化等需要高效探索的场景
传统强化学习在药物设计和黑箱函数优化等场景中常难以生成多样且高回报的解。马尔可夫链蒙特卡洛（MCMC）方法虽为替代方案，但计算成本高且探索能力有限。为此，本文提出改进GFlowNet的方法，通过引入新损失函数并优化子GFlowNet的训练目标，融合熵信息并利用网络结构特性，显著提升候选解的多样性与计算效率。在超网格实验与分子合成任务中的实证结果表明，改进后的模型优于传统方法，验证了熵整合与结构特征利用在分子合成及多样化实验设计中的有效性。
原文摘要 · Abstract (English)
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.
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