Differentially Private Policy Evaluation

Borja Balle; Maziar Gomrokchi; Doina Precup

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arXiv · 1603.02010

Differentially Private Policy Evaluation

Borja Balle·Maziar Gomrokchi·Doina Precup

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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.

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Borja Balle, Maziar Gomrokchi, Doina Precup. 2016-03-07. Differentially Private Policy Evaluation. https://arxiv.org/abs/1603.02010

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