TeXDYNA: hierarchical reinforcement learning in factored MDPs

Authors:
Olga Kozlova;Olivier Sigaud;Christophe Meyer
Affiliations:
Institut des Systèmes Intelligents et de Robotique, Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Paris Cedex 5;Institut des Systèmes Intelligents et de Robotique, Université Pierre et Marie Curie-Paris 6, CNRS, UMR, Paris Cedex 5;Thales Security Solutions & Services, ThereSIS Research and Innovation Office, Palaiseau Cedex
Venue:
SAB'10 Proceedings of the 11th international conference on Simulation of adaptive behavior: from animals to animats
Year:
2010

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Abstract

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.