  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.
