Interactive teaching strategies for agent training

Agents learning how to act in new environments can benefit from input from more experienced agents or humans. This paper studies interactive teaching strategies for identifying when a student can benefit from teacher-advice in a reinforcement learning framework. In student-teacher learning, a teacher agent can advise the student on which action to take. Prior work has considered heuristics for the teacher to choose advising opportunities. While these approaches effectively accelerate agent training, they assume that the teacher constantly monitors the student. This assumption may not be satisfied with human teachers, as people incur cognitive costs of monitoring and might not always pay attention. We propose strategies for a teacher and a student to jointly identify advising opportunities so that the teacher is not required to constantly monitor the student. Experimental results show that these approaches reduce the amount of attention required from the teacher compared to teacher-initiated strategies, while maintaining similar learning gains. The empirical evaluation also investigates the effect of the information communicated to the teacher and the quality of the student's initial policy on teaching outcomes.