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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Bridging the Gap between Reinforcement Learning and Knowledge Representation: A Logical Off- and On-Policy Framework
verfasst von : Emad Saad
Erschienen in: Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Verlag: Springer Berlin Heidelberg
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Knowledge Representation is an important issue in reinforcement learning. In this paper, we bridge the gap between reinforcement learning and knowledge representation, by providing a rich knowledge representation framework, based on normal logic programs with answer set semantics, that is capable of solving model-free reinforcement learning problems for more complex domains and exploits the domain-specific knowledge. We prove the correctness of our approach. We show that the complexity of finding an offline and online policy for a model-free reinforcement learning problem in our approach is NP-complete. Moreover, we show that any model-free reinforcement learning problem in an MDP environment can be encoded as a SAT problem. The importance of that is model-free reinforcement learning problems can be now solved as SAT problems.