seba-1511/qrl

Code for "Policy Learning and Evaluation with Randomized Quasi-Monte Carlo"

Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

Code release for "Policy Learning and Evaluation with Randomized Quasi-Monte Carlo", AISTATS 2022.

This code provides a re-implementation of SAC combined with RQMC. It is based on the PyTorch implementation of SAC in spinning-up.

Resources

• Website: seba1511.net/projects/qrl
• Preprint: arxiv.org/abs/2202.07808
• Code: github.com/seba-1511/qrl

Citation

Please cite this work as follows:

S. M. R. Arnold, P. L'Ecuyer, L. Chen, Y. Chen, F. Sha, Policy Learning and Evaluation with Randomized Quasi-Monte Carlo. AISTATS 2022.

or with the following BibTex entry:

@inproceedings{Arnold2022qrl,
    title={Policy Learning and Evaluation with Randomized Quasi-Monte Carlo},
    author={Arnold, S\'ebastien M. R. and L'Ecuyer, Pierre and Chen, Liyu and Chen, Yi-fan and Sha, Fei},
    year={2022},
    booktitle={Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
    volume={131},
    series={Proceedings of Machine Learning Research},
    publisher={PMLR},
}

Usage

Policy learning experiments can be run with the following command:

python qsac.py --env HalfCheetah-v2 --rqmc --multi_actions 4