Reinforcement Learning with Markov Logic Networks
and Xingguo Chen and Shen Ge
In this paper, we propose a method to combine reinforcement
learning (RL) and Markov logic networks (MLN). RL usually does
not consider the inherent relations or logical connections of the features.
Markov logic networks combines first-order logic and graphical model
and it can represent a wide variety of knowledge compactly and abstractly.
We propose a new method, reinforcement learning algorithm
with Markov logic networks (RLMLN), to deal with many difficult problems
in RL which have much prior knowledge to employ and need some
relational representation of states. With RLMLN, prior knowledge can
be easily introduced to the learning systems and the learning process
will become more efficient. Experiments on blocks world illustrate that
RLMLN is a promising method.