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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