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PLOI

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@mit.edu 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):

  • Use Python 3.6 or higher.
  • Download Python dependencies: pip install -r requirements.txt.
  • Make sure the directory containing this code is in your PYTHONPATH.
  • Download and build the plan validation tool available at https://github.com/KCL-Planning/VAL, then make a symlink called 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".

Now, ./run.sh should work. Different domains and methods can be run by modifying the variables at the top of run.sh.

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Code for "Planning with Learned Object Importance in Large Problem Instances using Graph Neural Networks" (AAAI 2021)

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