Installation#
scope-rl is available at PyPI, and can be installed from pip or source as follows.
pip install scope-rl
git clone https://github.com/hakuhodo-technologies/scope-rl
cd scope-rl
python setup.py install
Citation#
If you use our pipeline in your work, please cite our paper below.
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito.
SCOPE-RL: A Python Library for Offline Reinforcement Learning, Off-Policy Evaluation, and Policy Selection
(a preprint is coming soon..)
@article{kiyohara2023scope,
title={SCOPE-RL: A Python Library for Offline Reinforcement Learning, Off-Policy Evaluation, and Policy Selection},
author={Kiyohara, Haruka and Kishimoto, Ren and Kawakami, Kosuke and Kobayashi, Ken and Nakata, Kazuhide and Saito, Yuta},
journal={arXiv preprint arXiv:23xx.xxxxx},
year={2023}
}
If you use the proposed metric (SharpeRatio@k) or refer to our findings in your work, please cite our paper below.
Haruka Kiyohara, Ren Kishimoto, Kosuke Kawakami, Ken Kobayashi, Kazuhide Nakata, Yuta Saito.
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation in Reinforcement Learning
(a preprint is coming soon..)
@article{kiyohara2023towards,
title={Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy Evaluation in Reinforcement Learning},
author={Kiyohara, Haruka and Kishimoto, Ren and Kawakami, Kosuke and Kobayashi, Ken and Nakata, Kazuhide and Saito, Yuta},
journal={arXiv preprint arXiv:23xx.xxxxx},
year={2023}
}