Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning
Year: 2026
Type: dataset
Abstract: Datasets, trained model checkpoints and planner outputs for the NeurIPS 2026 paper "Efficient Lookahead Encoding and Abstracted Width for Learning General Policies in Classical Planning" (Aichmüller, Ståhlberg, Funkquist, Geffner). The record contains everything behind the paper's coverage tables, plus a snapshot of the code to train and run the policies. Contents:- README.md: layout, how to run a checkpoint, the mapping from folders to paper columns, and how the reported checkpoints were chosen.- manifest.tsv: one row per (paper column, domain, checkpoint), with validation and test results and the paths of the checkpoint and its evaluation.- datasets.zip: the 21 planning domains of the paper (10 from the IPC 2023 learning track, 11 further domains) with their train/validation/test splits in PDDL.- checkpoints.zip: 1,169 trained R-GNN policies. For every paper column and domain, this is the checkpoint whose test coverage is reported, plus the next best checkpoints of its planning round.- evaluations-<column>.zip (one per paper column): the planner output of every checkpoint on the test split, i.e. plans, per-problem summaries and search traces.- code.zip: snapshot of https://github.com/maichmueller/aggregated-delta (training and planning code, prebuilt wheels for CPython 3.12 on Linux x86-64, Dockerfile and Apptainer definition), plus source snapshots of the exact dependency versions. Usage: unzip all archives into one folder, build the container from code/ and plan from that folder (see README.md). Running checkpoints only needs code.zip, datasets.zip and checkpoints.zip. Counting "Solved: yes" in the per-problem summaries of the reported checkpoints reproduces every coverage number of the paper. Licenses: data, checkpoints and planner outputs are released under CC BY 4.0. The code in code.zip is released under GPL-3.0 (see code/LICENSE).
Institutions: RWTH Aachen University, Linköping University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225408500