Privacy-Preserving Off-Policy Evaluation

Addressing the problem that reinforcement learning training data may leak sensitive information, the paper proposes the first differential privacy method for off-policy evaluation and provides a theoretical analysis of its privacy protection properties and utility (convergence rate). Experiments show that the method outperforms prior comparable methods limited to the on-policy setting.