  Many reinforcement learning applications involve the use of data that is
sensitive, such as medical records of patients or financial information.
However, most current reinforcement learning methods can leak information
contained within the (possibly sensitive) data on which they are trained. To
address this problem, we present the first differentially private approach for
off-policy evaluation. We provide a theoretical analysis of the
privacy-preserving properties of our algorithm and analyze its utility (speed
of convergence). After describing some results of this theoretical analysis, we
show empirically that our method outperforms previous methods (which are
restricted to the on-policy setting).
