[Optional] create a virtual environment:

conda create -n eph python=3.11
conda activate eph

Install the repo locally (with requirements listed in pyproject.toml):

pip install -e '.[all]'

Note: remove [all] if you don't want to install the optional dependencies.

To train and test we need to load the configuration file. under configs/ you can find the default configuration file eph.py. To change the configuration or create a new one, you can use export the "CONFIG" environment variable as the desired configuration name without the .py extension:

export CONFIG=eph

To train the model, you can use the following command:

python train.py

To test the model, you can use the following command:

python test.py

We made the configuration loading dynamic, so multiple configurations are allowed for different experiments under configs/.

Before running any script, you can change which configuration to load by changing the CONFIG_NAME variable in the config.py file:

CONFIG_NAME = 'eph'

For example, the above will load the default configuration file configs/eph.py.

To change the model, we made sure that the model path is loaded from the configuration file.

You can change the target by:

model_target = "model.Network"

This will load the Network class from the model.py module.

Go to src/data/ and follow the instructions in the README.md for generating the MovingAI's test set.

Our codebase is heavily based on DHC (https://github.com/ZiyuanMa/DHC) and DCC (https://github.com/ZiyuanMa/DCC). We used some inspiration from SCRIMP for our communication block (https://github.com/marmotlab/SCRIMP) and reimplemented structured maps experiments of MovingAI datasets from SACHA (https://github.com/Qiushi-Lin/SACHA).

We are also looking into implementing MAPF in some modern platform (i.e. TorchRL enviroments and integration with RL4CO) once we have some bandwidth to do so!

If you find our code or work (or hopefully both!) helpful, please consider citing us:

@article{tang2024eph,
  title={Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding},
  author={Tang, Huijie and Berto, Federico and Park, Jinkyoo},
  journal={arXiv preprint arXiv:2403.07559},
  year={2024}
}