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Autonomous Agents and Multi-Agent Systems

Erschienen in:

01.04.2020

Agents teaching agents: a survey on inter-agent transfer learning

verfasst von: Felipe Leno Da Silva, Garrett Warnell, Anna Helena Reali Costa, Peter Stone

Erschienen in: Autonomous Agents and Multi-Agent Systems | Ausgabe 1/2020

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Abstract

While recent work in reinforcement learning (RL) has led to agents capable of solving increasingly complex tasks, the issue of high sample complexity is still a major concern. This issue has motivated the development of additional techniques that augment RL methods in an attempt to increase task learning speed. In particular, inter-agent teaching—endowing agents with the ability to respond to instructions from others—has been responsible for many of these developments. RL agents that can leverage instruction from a more competent teacher have been shown to be able to learn tasks significantly faster than agents that cannot take advantage of such instruction. That said, the inter-agent teaching paradigm presents many new challenges due to, among other factors, differences between the agents involved in the teaching interaction. As a result, many inter-agent teaching methods work only in restricted settings and have proven difficult to generalize to new domains or scenarios. In this article, we propose two frameworks that provide a comprehensive view of the challenges associated with inter-agent teaching. We highlight state-of-the-art solutions, open problems, prospective applications, and argue that new research in this area should be developed in the context of the proposed frameworks.

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Titel: Agents teaching agents: a survey on inter-agent transfer learning
verfasst von: Felipe Leno Da Silva
Garrett Warnell
Anna Helena Reali Costa
Peter Stone
Publikationsdatum: 01.04.2020
Verlag: Springer US
Erschienen in: Autonomous Agents and Multi-Agent Systems / Ausgabe 1/2020
Print ISSN: 1387-2532
Elektronische ISSN: 1573-7454
DOI: https://doi.org/10.1007/s10458-019-09430-0

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