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Knowledge Representation is important issue in reinforcement learning. Inthis paper, we bridge the gap between reinforcement learning and knowledgerepresentation, by providing a rich knowledge representation framework, basedon normal logic programs with answer set semantics, that is capable of solvingmodel-free reinforcement learning problems for more complex do-mains andexploits the domain-specific knowledge. We prove the correctness of ourapproach. We show that the complexity of finding an offline and online policyfor a model-free reinforcement learning problem in our approach is NP-complete.Moreover, we show that any model-free reinforcement learning problem in MDPenvironment can be encoded as a SAT problem. The importance of that ismodel-free reinforcement

Bridging the Gap between Reinforcement Learning and Knowledge Representation: A Logical Off- and On-Policy Framework