This repository contains the Pytorch implementation of 'SLOPE: Search with Learned Optimal Pruning-based Expansion'.

conda create -n slope python=3.8
conda activate slope
pip install -r requirements.txt -f https://download.pytorch.org/whl/cu113/torch_stable.html

Both the training data and pretrained weights can be downloaded here. For training or evaluation, the directory structure should be as such:

SLOPE/
│
├── src/
├── data/
└── weights/

Generating optimal cost-to-go data, stored in data/map_type/ctg_data, is done with python generate_data.py; manually set dataset type and sample number in the script. Similarly, optimal regions are found with python generate_regions.py; pre-generated cost-to-go data is required.

To run training, set dataset type, number of epochs and batch size in train.py. Options for data balancing are located in loading functions from load_data.py.

Evaluation of trained models is done on maps indexed 400 to 500 by running python eval.py. Model with a singular search algorithm can be run with the run_eval function, while benchmarking both SLOPE algorithms with and without neural cost-to-go is done with compare_heurs function. Note pruning thresholds should be manually set.

If you found SLOPE relevant for your research, please cite it as:

@inproceedings{bokan2024slope,
  title={SLOPE: Search with Learned Optimal Pruning-based Expansion},
  author={Bokan, Davor and Ajanovic, Zlatan and Lacevic, Bakir}
}