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},
}