Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots. In this paper, we present TeXDYNA, an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement Learning and factorization techniques of Factored Reinforcement Learning. We validate our approach on the LIGHT BOX problem. © 2010 Springer-Verlag.
CITATION STYLE
Kozlova, O., Sigaud, O., & Meyer, C. (2010). TeXDYNA: Hierarchical reinforcement learning in factored MDPs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6226 LNAI, pp. 489–500). https://doi.org/10.1007/978-3-642-15193-4_46
Mendeley helps you to discover research relevant for your work.