Abstract
In this paper we introduce negation into Logical Markov Decision Processes, which is a model of Relational Reinforcement Learning. In the new model nLMDP the abstract state space can be constructed in a simple way, so that a good property of complementarity holds. Prototype action is also introduced into the model. A distinct feature of the model is that applicable abstract actions can be obtained automatically with valid substitutions. Given a complementary abstract state space and a set of prototype actions, a model-free Θ-learing method is implemented for evaluating the state-action-substitution value funcion.
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Zhiwei, S., Xiaoping, C. (2006). Unique State and Automatical Action Abstracting Based on Logical MDPs with Negation. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_118
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DOI: https://doi.org/10.1007/11881223_118
Publisher Name: Springer, Berlin, Heidelberg
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