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ILP
2007
Springer

Relational Macros for Transfer in Reinforcement Learning

12 years 3 months ago
Relational Macros for Transfer in Reinforcement Learning
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.
Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richa
Added 08 Jun 2010
Updated 08 Jun 2010
Type Conference
Year 2007
Where ILP
Authors Lisa Torrey, Jude W. Shavlik, Trevor Walker, Richard Maclin
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