Differentially Private Policy Evaluation

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Borja Balle, Maziar Gomrokchi, Doina Precup ;
Proceedings of The 33rd International Conference on Machine Learning, PMLR 48:2130-2138, 2016.

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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