Transfer in Reinforcement Learning via Markov Logic Networks
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.