It has been well demonstrated that inverse reinforcement learning (IRL) is an
effective technique for teaching machines to perform tasks at human skill
levels given human demonstrations (i.e., human to machine apprenticeship
learning). This paper seeks to show that a similar application can be
demonstrated with human learners. That is, given demonstrations from human
experts inverse reinforcement learning techniques can be used to teach other
humans to perform at higher skill levels (i.e., human to human apprenticeship
learning). To show this two experiments were conducted using a simple,
real-time web game where players were asked to touch targets in order to earn
as many points as possible. For the experiment player performance was defined
as the number of targets a player touched, irrespective of the points that a
player actually earned. This allowed for in-game points to be modified and the
effect of these alterations on performance measured. At no time were
participants told the true performance metric. To determine the point
modifications IRL was applied on demonstrations of human experts playing the
game. The results of the experiment show with significance that performance
improved over the control for select treatment groups. Finally, in addition to
the experiment, we also detail the algorithmic challenges we faced when
conducting the experiment and the techniques we used to overcome them.Comment: 31 pages, 23 figures, Submitted to Journal of Artificial Intelligence
Research, "for source code, see https://github.com/mrucker/kpirl-kla