Before revision:  We fix the teacher's policy as the context of the demonstrations , which allows for different internal models across the student and the teacher , and contrasts the existing methods. We make the following two contributions. (i) We develop a differentially private mechanism that protects the privacy of the teacher's training dataset.
After revision:  We fix the teacher's demonstrations with the student. The mechanism allows for the teacher to decide upon the accuracy of its demonstrations with respect to the privacy budget that it consumes, thereby granting the teacher full control over its data privacy. We then develop a kickstarted deep reinforcement learning algorithm for the student that is privacy-aware because we calibrate its objective with the parameters of the teacher's training dataset.