Class Teaching for Inverse Reinforcement Learners
This work addresses a novel challenge in machine teaching for heterogeneous groups, which is incremental as it extends existing teaching methods to multiple learners.
The paper tackles the problem of teaching sequential tasks to multiple inverse reinforcement learners with heterogeneous characteristics, proposing a machine teaching algorithm that identifies conditions for using a single demonstration and evaluates its effectiveness against alternatives.
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.