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@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 JAAMAS},
location = {Germany},
month = {Dec}
}