Transfer in Reinforcement Learning via Markov Logic Networks

Lisa Torrey , Jude Shavlik , Sriraam Natarajan , Pavan Kuppili and Trevor Walker

Abstract:

We propose the use of statistical relational learning, and in particular the formalism of Markov Logic Networks, for transfer in reinforcement learning. Our goal is to extract relational knowledge from a source task and use it to speed up learning in a related target task. We do so by learning a Markov Logic Network that describes the source-task Q-function, and then using it for decision making in the early learning stages of the target task. Through experiments in the RoboCup simulated-soccer domain, we show that this approach can provide a substantial performance benefit in the target task.

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