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Learning Domain-Independent Planning Heuristics over Hypergraphs (ICAPS'20)


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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 open a GitHub issue, or contact the authors of the paper. You can find our emails in the paper.

Updates (2023-09):


We recommend you use a virtual environment (e.g. virtualenv or conda). STRIPS-HGN requires Python 3.6+.

  1. Create Python environment (we're using conda and Python 3.8).
    conda create -n stripshgn python=3.8
    conda activate stripshgn
  2. Clone the repo and install the dependencies.
    git clone
    pip install -r requirements.txt
  3. Clone our custom version of Fast Downward and build it. The build may take a minute or two.
    git clone src/fast_downward
    python src/fast_downward/
  4. Try running the train script to make sure everything is working. If you see the help message then you're good to go.
    python src/ --help


The entry point for training is src/ and for evaluation is src/ Use the help flag (-h, --help) to get usage information.

We provide example scripts for running experiments in the experiments directory. These outline the command line options that were used for training.

Making Training more Stable

Training STRIPS-HGN can be quite unstable, which is partially why we used the k folds method during training. Here are some tips to make training more stable:

  • Remove the ReLU activation function in the output transform in EncodeProcessDecode (remove the nn.ReLU(inplace=True) from edge_model, node_model, and global_model).
    • When the STRIPS-HGN is initialized with weights such that the output is negative, the ReLU will zero the gradients and the network will not learn. Removing the ReLU and just keeping the linear layer seems to solve this problem.
    • Note that you will need to torch.clamp(outputs, min=0) or min(0, outputs), so the outputs are non-negative and are hence valid heuristics.
  • Using a smaller learning rate such as 1e-4 (the paper used 1e-3), or automatically reduce the learning rate when the loss plateaus (you can try torch.optim.lr_scheduler.ReduceLROnPlateau).

Thanks to Carlos Núñez Molina (University of Granada) and Dillon Chen (Australian National University) for these tips. These changes mean you can try reducing the number of folds or potentially not use them at all.

Codebase 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/ 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

Past Updates

17th September 2023: Added tips for addressing unstable training and fixed bug that didn't merge HypergraphsTuple correctly by accumulating the node indices. This meant that using batch size > 1 would not work properly. Thanks to Carlos Núñez Molina for raising these issues.

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.

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.

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.

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

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

  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},


Learning Domain-Independent Planning Heuristics over Hypergraphs (ICAPS'20)







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