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A New Decomposition Technique for Solving Markov Decision Processes
Laroche P. et al
Symposium on Applied Computing - SAC'2001, Las Vegas, USA : (2001) - http://hal.inria.fr/inria-00101093
Pierre Laroche ()1, Yann Boniface ()2, René Schott3, 4
1:  LITA (EA3097) - Laboratoire d'Informatique Théorique et Appliquée
Université Paul Verlaine - Metz
France
2:  INRIA Lorraine - LORIA - CORTEX
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
France
3:  LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
http://www.loria.fr
INRIA – CNRS : UMR7503 – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
Campus Scientifique BP 239 54506 Vandoeuvre-lès-Nancy Cedex
France
4:  IECN - Institut Elie Cartan Nancy
http://www.iecn.u-nancy.fr/
CNRS : UMR7502 – INRIA – Université Henri Poincaré - Nancy I – Université Nancy II – Institut National Polytechnique de Lorraine (INPL)
France
Computer Science/Other
A New Decomposition Technique for Solving Markov Decision Processes
In this paper, we present a new tool for automatically solving Markov Decision Processes. Using a predefined partition o fthe MDP, a directed graph is built to decompose the global MDP into small local MDPs which are independently solved. An approximate solution for the global MDP is obtained using local solutions. Our approach has been tested on a mobile robotics application. It allows near-optimal solutions to be obtained in significantly reduced time. We also present preliminary results concerning a parallel implementation.
English

2001
international
Symposium on Applied Computing - SAC'2001
Las Vegas, USA
2001
ACM
none
5 p

planning under uncertainty – markov decision process – decomposition – parallelism || plannification sous incertitude – mdp – parallélisme
Colloque avec actes et comité de lecture. internationale.
A01-R-107 || laroche01a