Class Teaching for Inverse Reinforcement Learners
Abstract
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.
Cite
@article{arxiv.1911.13009,
title = {Class Teaching for Inverse Reinforcement Learners},
author = {Manuel Lopes and Francisco Melo},
journal= {arXiv preprint arXiv:1911.13009},
year = {2019}
}