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Abstract: We present a provably efficient and near-optimal algorithm for reinforcement learning in Markov decision processes (MDPs) whose transition model can be factored as a dynamic Bayesian network (DBN). (Update)
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BibTeX entry: (Update)
M. Kearns and D. Koller. Efficient reinforcement learning in factored MDPs. In Proc. IJCAI, 1999. http://citeseer.ist.psu.edu/article/kearns99efficient.html More
@inproceedings{ kearns99efficient,
author = "Michael J. Kearns and Daphne Koller",
title = "Efficient Reinforcement Learning in Factored {MDPs}",
booktitle = "{IJCAI}",
pages = "740-747",
year = "1999",
url = "citeseer.ist.psu.edu/article/kearns99efficient.html" }
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188 Decision theoretic planning: Structural assumptions and comp.. - Boutilier, Dean et al. - 1999
130 Influence diagrams (context) - Howard, Matheson - 1984
113 Tractable inference for complex stochastic processes - Boyen, Koller - 1998
61 Polynomialtime approximation algorithms for the Ising model - Jerrum, Sinclair - 1993
59 The BATmobile: Towards a Bayesian automated taxi - Forbes, Huang et al. - 1995
42 Computing factored value functions for policies in structure.. - Koller, Parr - 1999
37 A sparse sampling algorithm for near-optimal planning in lar.. - Kearns, Mansour et al. - 1999
34 Solving very large weakly coupled Markov decision processes - Meuleau, Hauskrecht et al. - 1998
8 Central limit theorem for nonstationary Markov chains (context) - Dobrushin - 1956
8 Near-optimal performance for reinforcement learning in polyn.. (context) - Kearns, Singh - 1998
7 An expert system for control of waste water treatment---a pi.. (context) - Jensen, Kjrulff et al. - 1989
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