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Efficient approximate planning in continuous space Markovian Decision Problems

Article type: Research Article

Authors: Szepesvári, Csaba

Affiliations: Mindmaker Ltd., Konkoly‐Thege M. u. 29‐33, 1121, Budapest, Hungary E‐mail: szepes@mindmaker.hu

Abstract: Monte‐Carlo planning algorithms for planning in continuous state‐space, discounted Markovian Decision Problems (MDPs) having a smooth transition law and a finite action space are considered. We prove various polynomial complexity results for the considered algorithms, improving upon several known bounds.

Keywords: Markovian Decision Problems, planning, value iteration, Monte‐Carlo algorithms

Journal: AI Communications, vol. 14, no. 3, pp. 163-176, 2001

Published: 2001
Price: EUR 27.50
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