  Transferring as fast as possible the functioning of our brain to artificial
intelligence is an ambitious goal that would help advance the state of the art
in AI and robotics. It is in this perspective that we propose to start from
hypotheses derived from an empirical study in a human-robot interaction and to
verify if they are validated in the same way for children as for a basic
reinforcement learning algorithm. Thus, we check whether receiving help from an
expert when solving a simple close-ended task (the Towers of Hano\"i) allows to
accelerate or not the learning of this task, depending on whether the
intervention is canonical or requested by the player. Our experiences have
allowed us to conclude that, whether requested or not, a Q-learning algorithm
benefits in the same way from expert help as children do.
