  Interactive reinforcement learning has become an important apprenticeship
approach to speed up convergence in classic reinforcement learning problems. In
this regard, a variant of interactive reinforcement learning is policy shaping
which uses a parent-like trainer to propose the next action to be performed and
by doing so reduces the search space by advice. On some occasions, the trainer
may be another artificial agent which in turn was trained using reinforcement
learning methods to afterward becoming an advisor for other learner-agents. In
this work, we analyze internal representations and characteristics of
artificial agents to determine which agent may outperform others to become a
better trainer-agent. Using a polymath agent, as compared to a specialist
agent, an advisor leads to a larger reward and faster convergence of the reward
signal and also to a more stable behavior in terms of the state visit frequency
of the learner-agents. Moreover, we analyze system interaction parameters in
order to determine how influential they are in the apprenticeship process,
where the consistency of feedback is much more relevant when dealing with
different learner obedience parameters.
