https://github.com/seba-1511/qrl

Code for "Policy Learning and Evaluation with Randomized Quasi-Monte Carlo"
https://github.com/seba-1511/qrl

Last synced: 11 days ago
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Code for "Policy Learning and Evaluation with Randomized Quasi-Monte Carlo"

• Host: GitHub
• URL: https://github.com/seba-1511/qrl
• Owner: seba-1511
• License: apache-2.0
• Created: 2022-02-15T02:57:11.000Z (almost 3 years ago)
• Default Branch: main
• Last Pushed: 2022-03-12T22:34:07.000Z (almost 3 years ago)
• Last Synced: 2024-12-02T18:58:29.900Z (24 days ago)
• Language: Python
• Size: 14.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Policy Learning and Evaluation with Randomized Quasi-Monte Carlo

[![AISTATS](https://img.shields.io/badge/AISTATS-2022-informational.svg)](http://seba1511.net/projects/qrl/)

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](https://github.com/openai/spinningup).

## Resources

* Website: [seba1511.net/projects/qrl](http://seba1511.net/projects/qrl)

* Preprint: [arxiv.org/abs/2202.07808](https://arxiv.org/abs/2202.07808)

* Code: [github.com/seba-1511/qrl](https://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:

~~~bibtex

@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:

~~~shell

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

~~~