Reinforcement Learning with Markov Logic Networks

Weiwei Wang and Yang Gao 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.


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