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Improving interactive reinforcement learning: what makes a good teacher?

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journal contribution
posted on 01.01.2018, 00:00 authored by F Cruz Naranjo, S Magg, Y Nagai, S Wermter
Interactive reinforcement learning (IRL) has become an important apprenticeship approach to speed up convergence in classic reinforcement learning (RL) problems. In this regard, a variant of IRL is policy shaping which uses a parent-like trainer to propose the next action to be performed and by doing so reduces the search space by advice. On some occasions, the trainer may be another artificial agent which in turn was trained using RL methods to afterward becoming an advisor for other learner-agents. In this work, we analyse internal representations and characteristics of artificial agents to determine which agent may outperform others to become a better trainer-agent. Using a polymath agent, as compared to a specialist agent, an advisor leads to a larger reward and faster convergence of the reward signal and also to a more stable behaviour in terms of the state visit frequency of the learner-agents. Moreover, we analyse system interaction parameters in order to determine how influential they are in the apprenticeship process, where the consistency of feedback is much more relevant when dealing with different learner obedience parameters.

History

Journal

Connection science

Volume

30

Issue

3

Pagination

306 - 325

Publisher

Taylor & Francis

Location

Abingdon, Eng.

ISSN

0954-0091

eISSN

1360-0494

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2018, The Author(s)