Differentially Private Policy Evaluation
Abstract
We present the first differentially private algorithms for reinforcement learning, which apply to the task of evaluating a fixed policy. We establish two approaches for achieving differential privacy, provide a theoretical analysis of the privacy and utility of the two algorithms, and show promising results on simple empirical examples.
Cite
Text
Balle et al. "Differentially Private Policy Evaluation." International Conference on Machine Learning, 2016.Markdown
[Balle et al. "Differentially Private Policy Evaluation." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/balle2016icml-differentially/)BibTeX
@inproceedings{balle2016icml-differentially,
title = {{Differentially Private Policy Evaluation}},
author = {Balle, Borja and Gomrokchi, Maziar and Precup, Doina},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {2130-2138},
volume = {48},
url = {https://mlanthology.org/icml/2016/balle2016icml-differentially/}
}