Before revision:  We empirically show that the algorithm improves the student's learning upon convergence rate and utility. Specifically, compared with an agent who learns the same task on its own, we observe that the student 's policy converges faster, and the converging policy accumulates higher rewards more robustly .
After revision:  We empirically show that the algorithm improves the student's learning , (ii) the student converges to a performance level that was not possible without the demonstrations, and (iii) the student maintains its enhanced performance even after the teacher stops sharing useful demonstrations due to its privacy budget constraints .