Approximation spaces in off-policy Monte Carlo learning-Reference-Cited by-同舟云学术

Approximation spaces in off-policy Monte Carlo learning

Published:2007-08 Issue:5 Volume:20 Page:667-675
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Peters James F.,Henry Christopher

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference33 articles.

1. Gaskett, C., 2002. Q-Learning for Robot Control. supervisor: A. Zelinsky, Department of Systems Engineering, The Australian National University.

2. Grzymała-Busse, J., et al. (Eds.), 2001. Rough sets and their applications. International Journal of Applied Mathematics and Computer Science 11(3) (special issue).

3. Monte Carlo Methods;Hammersley,1964

4. Extremal Combinatorics;Jukna,2001

5. Mondada, F., Pettinaro, G.C., Guignard, A., 2004. A New Distributed Robotic Concept, Autonomous System Lab (LSA), EPFL-STI-I2S, Lausanne, Switzerland. Kluwer Academic Publishers, The Netherlands.

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