  Action advising is a peer-to-peer knowledge exchange technique built on the
teacher-student paradigm to alleviate the sample inefficiency problem in deep
reinforcement learning. Recently proposed student-initiated approaches have
obtained promising results. However, due to being in the early stages of
development, these also have some substantial shortcomings. One of the
abilities that are absent in the current methods is further utilising advice by
reusing, which is especially crucial in the practical settings considering the
budget and cost constraints in peer-to-peer. In this study, we present an
approach to enable the student agent to imitate previously acquired advice to
reuse them directly in its exploration policy, without any interventions in the
learning mechanism itself. In particular, we employ a behavioural cloning
module to imitate the teacher policy and use dropout regularisation to have a
notion of epistemic uncertainty to keep track of which state-advice pairs are
actually collected. As the results of experiments we conducted in three Atari
games show, advice reusing via generalisation is indeed a feasible option in
deep RL and our approach can successfully achieve this while significantly
improving the learning performance, even when paired with a simple early
advising heuristic.
