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Agents teaching agents: a survey on inter-agent transfer learning.
Felipe Leno
Da Silva, Garrett Warnell, Anna
Helena Reali Costa, and Peter Stone.
Autonomous Agents and Multi-Agent
Systems, Dec 2019.
Official version from JAAMAS
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.
@article{JAAMAS20-Leno,
author = {Felipe Leno Da Silva and
Garrett Warnell and Anna Helena Reali Costa and Peter Stone},
title = {Agents teaching agents: a survey on inter-agent transfer learning},
journal = {Autonomous Agents and Multi-Agent Systems},
year = {2019},
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.},
wwwnote={Official version from <a href="https://link.springer.com/article/10.1007/s10458-019-09430-0">JAAMAS</a>},
location = {Germany},
month = {Dec}
}
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