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Code and Data for the ICAPS 2022 paper "Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits"

Authors: Simon Ståhlberg; Blai Bonet; Hector Geffner;

Code and Data for the ICAPS 2022 paper "Learning General Optimal Policies with Graph Neural Networks: Expressive Power, Transparency, and Limits"

Abstract

This archive contains three files: file 'code.zip' contains the code used to train the models; file 'data.zip' contains the datasets used to train and validate models, and formulas used to test if features are learned by trained models; file 'models.zip' contains the final trained models together with logs from both training and evaluation, and the linear transformation between learned and hand-crafted features.

Keywords

automated planning, machine learning, graph neural networks, deep learning, general optimal policies, neural networks, classical planning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average