WLPlan: Relational Features for Symbolic Planning

TL;DR

WLPlan is a C++ and Python tool that automatically generates relational features from planning tasks, aiding scalable learning and analysis in symbolic planning.

Contribution

Introduces WLPlan, a novel C++ package with Python bindings for generating relational features from planning tasks using graph transformations and kernels.

Findings

Enables scalable feature extraction for planning tasks.

Supports downstream learning and analysis routines.

Provides open-source implementation and usage instructions.

Abstract

Scalable learning for planning research generally involves juggling between different programming languages for handling learning and planning modules effectively. Interpreted languages such as Python are commonly used for learning routines due to their ease of use and the abundance of highly maintained learning libraries they exhibit, while compiled languages such as C++ are used for planning routines due to their optimised resource usage. Motivated by the need for tools for developing scalable learning planners, we introduce WLPlan, a C++ package with Python bindings which implements recent promising work for automatically generating relational features of planning tasks. Such features can be used for any downstream routine, such as learning domain control knowledge or probing and understanding planning tasks. More specifically, WLPlan provides functionality for (1) transforming planning tasks into graphs, and (2) embedding planning graphs into feature vectors via graph kernels. The source code and instructions for the installation and usage of WLPlan are available at tinyurl.com/42kymswc