  In this paper we propose the first machine teaching algorithm for multiple
inverse reinforcement learners. Specifically, our contributions are: (i) we
formally introduce the problem of teaching a sequential task to a heterogeneous
group of learners; (ii) we identify conditions under which it is possible to
conduct such teaching using the same demonstration for all learners; and (iii)
we propose and evaluate a simple algorithm that computes a demonstration(s)
ensuring that all agents in a heterogeneous class learn a task description that
is compatible with the target task. Our analysis shows that, contrary to other
teaching problems, teaching a heterogeneous class with a single demonstration
may not be possible as the differences between agents increase. We also
showcase the advantages of our proposed machine teaching approach against
several possible alternatives.
