We study the problem of inverse reinforcement learning (IRL) with the added
twist that the learner is assisted by a helpful teacher. More formally, we
tackle the following algorithmic question: How could a teacher provide an
informative sequence of demonstrations to an IRL learner to speed up the
learning process? We present an interactive teaching framework where a teacher
adaptively chooses the next demonstration based on learner's current policy. In
particular, we design teaching algorithms for two concrete settings: an
omniscient setting where a teacher has full knowledge about the learner's
dynamics and a blackbox setting where the teacher has minimal knowledge. Then,
we study a sequential variant of the popular MCE-IRL learner and prove
convergence guarantees of our teaching algorithm in the omniscient setting.
Extensive experiments with a car driving simulator environment show that the
learning progress can be speeded up drastically as compared to an uninformative
teacher.

本研究探究了带有教师辅助的反向强化学习问题，提出了一种交互式教学框架，设计了两种具体教学算法：全知教学和黑盒教学，最后在汽车驾驶仿真环境中进行了实验并证明了教学算法的效果。