• Sorted by Date • Classified by Publication Type • Sorted by First Author Last Name • Classified by Research Category •
Matthew E. Taylor, Peter Stone, and Yaxin Liu. Transfer Learning via Inter-Task Mappings for Temporal Difference Learning. Journal of Machine Learning Research, 8(1):2125–2167, 2007.
[PDF]499.9kB [postscript]831.3kB
Temporal difference (TD) learning has become a popular reinforcement learning technique in recent years. TD methods, relying on function approximators to generalize learning to novel situations, have had some experimental successes and have been shown to exhibit some desirable properties in theory, but the most basic algorithms have often been found slow in practice. This empirical result has motivated the development of many methods that speed up reinforcement learning by modifying a task for the learner or helping the learner better generalize to novel situations. This article focuses on generalizing across tasks, thereby speeding up learning, via a novel form of transfer using handcoded task relationships. We compare learning on a complex task with three function approximators, a cerebellar model arithmetic computer (CMAC), an artificial neural network (ANN), and a radial basis function (RBF), and empirically demonstrate that directly transferring the action-value function can lead to a dramatic speedup in learning with all three. Using transfer via inter-task mapping (tvitm), agents are able to learn one task and then markedly reduce the time it takes to learn a more complex task. Our algorithms are fully implemented and tested in the RoboCup soccer Keepaway domain.
@Article{JMLR07-taylor,
Author="Matthew E.\ Taylor and Peter Stone and Yaxin Liu",
title="Transfer Learning via Inter-Task Mappings for Temporal Difference Learning",
journal="Journal of Machine Learning Research",
year="2007",
volume="8",number="1",
pages="2125--2167",
abstract="Temporal difference (TD) learning has become a
popular reinforcement learning technique in recent years. TD
methods, relying on function approximators to generalize
learning to novel situations, have had some experimental
successes and have been shown to exhibit some desirable
properties in theory, but the most basic algorithms have
often been found slow in practice. This empirical result has
motivated the development of many methods that speed up
reinforcement learning by modifying a task for the learner
or helping the learner better generalize to novel
situations. This article focuses on generalizing across
tasks, thereby speeding up learning, via a novel form of
transfer using handcoded task relationships. We compare
learning on a complex task with three function
approximators, a cerebellar model arithmetic computer
(CMAC), an artificial neural network (ANN), and a radial
basis function (RBF), and empirically demonstrate that
directly transferring the action-value function can lead to
a dramatic speedup in learning with all three. Using
transfer via inter-task mapping (tvitm), agents are able to
learn one task and then markedly reduce the time it takes to
learn a more complex task. Our algorithms are fully
implemented and tested in the RoboCup soccer Keepaway
domain.",
}
Generated by bib2html.pl (written by Patrick Riley ) on Mon Nov 10, 2008 14:24:53