This repository houses code for the AAAI 2021 paper:
Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks
Tom Silver, Rohan Chitnis, Aidan Curtis, Joshua Tenenbaum, Tomas Lozano-Perez, Leslie Pack Kaelbling.
For any questions or issues with the code, please email ronuchit@gmail.com and tslvr@mit.edu.
Link to paper: https://arxiv.org/abs/2009.05613
Link to video: https://www.youtube.com/watch?v=FWsVJc2fvCE
Instructions for running (tested on Mac and Linux):
pip install -r requirements.txt
.validate
on your path that points to the build/Validate
binary, e.g. ln -s <path to VAL>/build/Validate /usr/local/bin/validate
. If done successfully, running validate
on your command line should give an output that starts with the line: "VAL: The PDDL+ plan validation tool". If you have trouble with the symlink, you can just directly change VALIDATE_CMD
in planning/validate.py
to point to the build/Validate
binary. Note: we have found more success directly downloading binaries from https://dev.azure.com/schlumberger/ai-planning-validation/_build?view=runs (click the latest green run, click one of the Jobs near the bottom, then click "artifacts produced" to get to the downloadable binaries), rather than building from the Github source.Now, ./run.sh
should work. Different domains and methods can be run by modifying the variables at the top of run.sh.