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PKDD
2009
Springer

Considering Unseen States as Impossible in Factored Reinforcement Learning

9 years 2 months ago
Considering Unseen States as Impossible in Factored Reinforcement Learning
Abstract. The Factored Markov Decision Process (FMDP) framework is a standard representation for sequential decision problems under uncertainty where the state is represented as a collection of random variables. Factored Reinforcement Learning (FRL) is an Model-based Reinforcement Learning approach to FMDPs where the transition and reward functions of the problem are learned. In this paper, we show how to model in a theoretically well-founded way the problems where some combinations of state variable values may not occur, giving rise to impossible states. Furthermore, we propose a new heuristics that considers as impossible the states that have not been seen so far. We derive an algorithm whose improvement in performance with respect to the standard approach is illustrated through benchmark experiments.
Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillem
Added 27 May 2010
Updated 27 May 2010
Type Conference
Year 2009
Where PKDD
Authors Olga Kozlova, Olivier Sigaud, Pierre-Henri Wuillemin, Christophe Meyer
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