Relational macros for transfer in reinforcement learning

Lisa Torrey, Jude Shavlik, Trevor Walker, Richard MacLin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Scopus citations


We describe an application of inductive logic programming to transfer learning. Transfer learning is the use of knowledge learned in a source task to improve learning in a related target task. The tasks we work with are in reinforcement-learning domains. Our approach transfers relational macros, which are finite-state machines in which the transition conditions and the node actions are represented by first-order logical clauses. We use inductive logic programming to learn a macro that characterizes successful behavior in the source task, and then use the macro for decision-making in the early learning stages of the target task. Through experiments in the RoboCup simulated soccer domain, we show that Relational Macro Transfer via Demonstration (RMT-D) from a source task can provide a substantial head start in the target task.

Original languageEnglish (US)
Title of host publicationInductive Logic Programming - 17th International Conference, ILP 2007, Revised Selected Papers
Number of pages15
StatePublished - 2008
Event17th International Conference on Inductive Logic Programming, ILP 2007 - Corvallis, OR, United States
Duration: Jun 19 2007Jun 21 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4894 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other17th International Conference on Inductive Logic Programming, ILP 2007
Country/TerritoryUnited States
CityCorvallis, OR


Dive into the research topics of 'Relational macros for transfer in reinforcement learning'. Together they form a unique fingerprint.

Cite this