Weisfeiler-Leman Features for Planning: A 1,000,000 Sample Size Hyperparameter Study
This work provides incremental improvements for researchers and practitioners in symbolic planning by identifying robust hyperparameter settings.
The paper tackled the problem of optimizing hyperparameters for Weisfeiler-Leman Features in planning tasks, finding through a large-scale study with 1,000,000 samples that the best hyperparameters minimize execution time rather than maximize model expressivity, with no significant correlation between training and planning metrics.
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.