  Deep Reinforcement Learning (RL) techniques can benefit greatly from
leveraging prior experience, which can be either self-generated or acquired
from other entities. Action advising is a framework that provides a flexible
way to transfer such knowledge in the form of actions between teacher-student
peers. However, due to the realistic concerns, the number of these interactions
is limited with a budget; therefore, it is crucial to perform these in the most
appropriate moments. There have been several promising studies recently that
address this problem setting especially from the student's perspective. Despite
their success, they have some shortcomings when it comes to the practical
applicability and integrity as an overall solution to the learning from advice
challenge. In this paper, we extend the idea of advice reusing via teacher
imitation to construct a unified approach that addresses both advice collection
and advice utilisation problems. We also propose a method to automatically tune
the relevant hyperparameters of these components on-the-fly to make it able to
adapt to any task with minimal human intervention. The experiments we performed
in 5 different Atari games verify that our algorithm either surpasses or
performs on-par with its top competitors while being far simpler to be
employed. Furthermore, its individual components are also found to be providing
significant advantages alone.

Machine Learning;Artificial Intelligence