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An Introduction to Inter-task Transfer for Reinforcement Learning.
Matthew
E. Taylor and Peter Stone.
AI Magazine, 32(1):15–34,
2011.
[PDF]237.0kB [postscript]773.0kB
Transfer learning has recently gained popularity due to the development of algorithms that can successfully generalize information across multiple tasks. This article focuses on transfer in the context of reinforcement learning domains, a general learning framework where an agent acts in an environment to maximize a reward signal. The goals of this article are to 1) familiarize readers with the transfer learning problem in reinforcement learning domains, 2) explain why the problem is both interesting and difficult, 3) present a selection of existing techniques that demonstrate different solutions, and 4) provide representative open problems in the hope of encouraging additional research in this exciting area.
@Article{AAAIMag11-Taylor,
Author="Matthew E.\ Taylor and Peter Stone",
title="An Introduction to Inter-task Transfer for Reinforcement Learning",
journal="{AI} Magazine",
year="2011",
volume="32",
number="1",
pages="15--34",
abstract="
Transfer learning has recently gained popularity due to
the development of algorithms that can successfully
generalize information across multiple tasks. This article
focuses on transfer in the context of reinforcement
learning domains, a general learning framework where an
agent acts in an environment to maximize a reward
signal. The goals of this article are to 1) familiarize
readers with the transfer learning problem in
reinforcement learning domains, 2) explain why the problem
is both interesting and difficult, 3) present a selection
of existing techniques that demonstrate different
solutions, and 4) provide representative open problems in
the hope of encouraging additional research in this
exciting area.",
}
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