Transfer learning via advice taking

Lisa Torrey, Jude Shavlik, Trevor Walker, Richard MacLin

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations

Abstract

The goal of transfer learning is to speed up learning in a new task by transferring knowledge from one or more related source tasks. We describe a transfer method in which a reinforcement learner analyzes its experience in the source task and learns rules to use as advice in the target task. The rules, which are learned via inductive logic programming, describe the conditions under which an action is successful in the source task. The advice-taking algorithm used in the target task allows a reinforcement learner to benefit from rules even if they are imperfect. A human-provided mapping describes the alignment between the source and target tasks, and may also include advice about the differences between them. Using three tasks in the RoboCup simulated soccer domain, we demonstrate that this transfer method can speed up reinforcement learning substantially.

Original languageEnglish (US)
Title of host publicationAdvances in Machine Learning I
Subtitle of host publicationDedicated to the Memory of Professor Ryszard S.Michalski
EditorsJacek Koronacki, Slawomir Wierzchon, Zbigniew Ras, Janusz Kacprzyk
Pages147-170
Number of pages24
DOIs
StatePublished - Jan 19 2010
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume262
ISSN (Print)1860-949X

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