WLPlan is a package for generating embeddings of PDDL planning problems for machine learning tasks. It supports both classical and numeric planning problems.
The Python interface can be installed simply with
pip install wlplan
The PyPI release only supports python>=3.10. Alternatively, you can also install the package from the source code with the install.sh script.
The C++ interface can be installed in your project by running
./cmake_build.py <path/to/installation>
and adding the following to the root CMakeLists.txt file of your project
list(APPEND CMAKE_PREFIX_PATH "<path/to/installation>")
find_package(wlplan)
...
target_link_libraries(<your_project> PRIVATE wlplan)
The graph representations of planning tasks implemented thus far are
| Name | WLPlan shorthand | Reference |
|---|---|---|
| Instance Learning Graph (ILG) | ilg |
D. Z. Chen, F. Trevizan, S. Thiébaux: Return to Tradition: Learning Reliable Heuristics with Classical Machine Learning. ICAPS 2024 |
| Numeric Instance Learning Graph (νILG) | nilg |
D. Z. Chen, S. Thiébaux: Graph Learning for Numeric Planning. NeurIPS 2024 |
The feature generators implemented thus far are
| Name | WLPlan shorthand | Reference |
|---|---|---|
| Relational Weisfeiler-Leman (WL) | wl |
P. Barceló, M. Galkin, C. Morris, M. A. Romero Orth: Weisfeiler and Leman Go Relational. LoG 2022 |
| 2-WL | kwl2 |
J. Cai, M. Fürer, N. Immerman: An optimal lower bound on the number of variables for graph identification. Combinatorica (1992) |
| Local 2-WL (2-LWL) | lwl2 |
C. Morris, K. Kersting, P. Mutzel: Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs. ICDM 2017 |
| Continuous-Categorical WL (ccWL) | ccwl |
D. Z. Chen, S. Thiébaux: Graph Learning for Numeric Planning. NeurIPS 2024 |
| individualised WL (iWL) | iwl |
coming soon |
| normalised iWL (niWL) | niwl |
coming soon |
Examples for how to use the package include this self-contained notebook and test, as well as the GOOSE framework.