  We study optimal teaching of Linear Behavior Cloning (LBC) learners. In this
setup, the teacher can select which states to demonstrate to an LBC learner.
The learner maintains a version space of infinite linear hypotheses consistent
with the demonstration. The goal of the teacher is to teach a realizable target
policy to the learner using minimum number of state demonstrations. This number
is known as the Teaching Dimension(TD). We present a teaching algorithm called
``Teach using Iterative Elimination(TIE)" that achieves instance optimal TD.
However, we also show that finding optimal teaching set computationally is
NP-hard. We further provide an approximation algorithm that guarantees an
approximation ratio of $\log(|A|-1)$ on the teaching dimension. Finally, we
provide experimental results to validate the efficiency and effectiveness of
our algorithm.
