L. Torrey, J. Shavlik, T. Walker & R. Maclin (2007).
Relational Macros for Transfer in Reinforcement Learning. Proceedings of the Seventeenth Conference on Inductive Logic Programming, Corvallis, Oregon.
Poster (PDF). Slides (PPT).

This publication is available in PDF.

The poster version of this publication is available in PDF.

Abstract:

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.

Return to the publications of the Univ. of Wisconsin Machine Learning Research Group.