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STRIPS-HGN

STRIPS-HGN is a framework for learning domain-independent planning heuristics completely from scratch using the hypergraph induced by the delete-relaxation of a STRIPS problem.

For any issues please contact the authors of the paper. You can find our emails in the paper.

Usage

Directory Structure

  • benchmarks: contains the PDDL domain and problems
  • experiments: the experiments used in the paper as Python scripts
  • scripts: helpful scripts used to run experiments on AWS. The scripts/ec2_setup.sh script could give hints to installing and running STRIPS-HGN
  • src
    • hypergraph_nets: Hypergraph Networks implementation based off Graph Networks. Note this implementation is sub-optimal and can be improved.
    • strips_hgn: logic for setting up, running and collecting results for the experiments. Includes hypergraph generation, feature mapping, generating training data, etc.
  • tests: some unit tests

Dependencies

  • You will need Python 3.6+
  • Use pip to install the requirements in requirements.txt (pip install -r requirements.txt)
  • Clone my custom version of Fast Downward (https://github.com/williamshen-nz/fast_downward.git) which you will need to build (use build.py)

I would recommend using a virtual environment. The entry point for training is src/train.py and for evaluation is src/eval.py. Use the help flag (-h, --help) to get usage information.

Known Issues and TODOs

  1. Move evaluation platform to Fast Downward (this is already completed and working in a separate repository)
  2. Move remaining experiment scripts from old codebase
    • Hanoi
    • Matching Blocksworld
    • Sokoban
    • Domain-Independent Experiments
  3. Optimise Hypergraph Networks

Past Updates

Update - 19th April 2021: apologies for the delay. Unfortunately, I have not had time to work on research so I am releasing the code-base as is.

Update - 21st Sept 2020: the implementation is ready but just needs some final cleaning up and testing. Due to work and other circumstances I have been unable to spend much time on research. I hope to release update this repository by ICAPS (October 19-30). For those who want a copy of the code in the meantime please email me.

Update - 21st June 2020: our re-implementation of STRIPS-HGN is almost ready to go and is currently undergoing testing and experiments. We intend to make it available before the end of June.

Please consider citing our paper if you found our work useful:

@inproceedings{shen20:stripshgn,
  author = {Shen, William and Trevizan, Felipe and Thi{\'e}baux, Sylvie},
  title = {Learning Domain-Independent Planning Heuristics with Hypergraph Networks},
  booktitle = {Proc. of 30th Int. Conf. on Automated Planning and Scheduling (ICAPS)},
  year = {2020},
}