  Accelerating learning processes for complex tasks by leveraging previously
learned tasks has been one of the most challenging problems in reinforcement
learning, especially when the similarity between source and target tasks is
low. This work proposes REPresentation And INstance Transfer (REPAINT)
algorithm for knowledge transfer in deep reinforcement learning. REPAINT not
only transfers the representation of a pre-trained teacher policy in the
on-policy learning, but also uses an advantage-based experience selection
approach to transfer useful samples 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 in generic
cases of task similarity. In particular, when the source tasks are dissimilar
to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in
both training-time reduction and asymptotic performance of return scores.
