用百万样本测试威斯费勒-莱曼特征在规划中的超参数表现
Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study
- 引入新超参数，基于百万样本在单核CPU上系统评估
- 最优超参数组合能显著缩短规划执行时间
- 适合符号规划、高效学习启发式函数的研究者
威斯费勒-莱曼特征（WLFs）是一种新兴的经典机器学习工具，用于学习规划与搜索。研究表明，其在学习搜索中的价值函数方面，理论和实证均优于现有深度学习方法。本文引入新的WLF超参数，并系统研究其权衡与影响。利用WLF的高效性，我们在单核CPU上以1,000,000样本规模进行规划实验，分析超参数对训练与规划的影响。实验表明，在所测试的规划领域中存在一组稳健且最优的超参数。最优超参数更关注最小化执行时间，而非最大化模型表达能力。进一步的统计分析显示，训练指标与规划指标之间无显著相关性。
原文摘要 · Abstract (English)
Weisfeiler-Leman Features (WLFs) are a recently introduced classical machine learning tool for learning to plan and search. They have been shown to be both theoretically and empirically superior to existing deep learning approaches for learning value functions for search in symbolic planning. In this paper, we introduce new WLF hyperparameters and study their various tradeoffs and effects. We utilise the efficiency of WLFs and run planning experiments on single core CPUs with a sample size of 1,000,000 to understand the effect of hyperparameters on training and planning. Our experimental analysis show that there is a robust and best set of hyperparameters for WLFs across the tested planning domains. We find that the best WLF hyperparameters for learning heuristic functions minimise execution time rather than maximise model expressivity. We further statistically analyse and observe no significant correlation between training and planning metrics.
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