Before revision:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy , where only the samples with high advantage estimates are retained for policy update. We consider both learningan unseen target task by transferring from previously learned teacher tasks and learning a partially unseen task composed of multiple sub-tasks by transferring from a pre-learned teacher sub-task. In several benchmark experiments, REPAINT significantly reduces the total training time and improves the asymptotic performance compared totraining with no prior knowledge and other baselines .
After revision:  In instance transfer, we develop a sampling approach, i.e., advantage-based experience replay, on transitions collected following the teacher policy in the off-policy learning. Our experimental results on several benchmark tasks show that REPAINT significantly reduces the total training time and improves the asymptotic performance compared totraining with no prior knowledge and other baselines .