Semi-Markov Afterstate Actor-Critic (SMAAC)

This repository is the official implementation of Winning the L2RPN Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic.

Environment setting

• python >= 3.6
• grid2op == 0.9.4 (Important!)
• lightsim2grid == 0.2.3 (Manual install required. (https://github.com/BDonnot/lightsim2grid))

Create conda environment

conda env create -f environment.yml
conda activate smaac

lightsim2grid installation

git clone https://github.com/BDonnot/lightsim2grid.git
cd lightsim2grid
git checkout v0.2.3
git submodule init
git submodule update
make
pip install -U pybind11
pip install -U .

Data download

Since chronic data is required to train or evaluate, please Download.
Then, replace data/ with it.

cd SMAAC
rm -rf data
tar -zxvf data.tar.gz

Scripts

Train

The detail of arguments is provided in test.py.

python test.py -n=[experiment_name] -s=[seed] -c=[environment_name (5, sand, wcci)]

# Example
python test.py -n=wcci_run -s=0 -c=wcci

Evaluate

The detail of arguments is provided in evaluate.py.

python evaluate.py -n=[experiment_dirname] -c=[environment_name]

# Example
python evaluate.py -n=wcci_run_0 -c=wcci

# If you want to evaluate an example trained model on WCCI, execute as below
python evaluate.py -n=example

References

@inproceedings{yoon2021winning,
    title={Winning the L2{\{}RPN{\}} Challenge: Power Grid Management via Semi-Markov Afterstate Actor-Critic},
    author={Deunsol Yoon and Sunghoon Hong and Byung-Jun Lee and Kee-Eung Kim},
    booktitle={International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=LmUJqB1Cz8}
}

Credit

Our code is based on rte-france's Grid2Op (https://github.com/rte-france/Grid2Op)

License Information

Copyright (c) 2020 KAIST-AILab

This source code is subject to the terms of the Mozilla Public License (MPL) v2 also available here