Uncertainty in Artificial Intelligence
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Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems
Ron Parr
Abstract:
This paper presents two new approaches to decomposing and solving large Markov decision problems (MDPs), a partial decoupling method and a complete decoupling method. In these approaches, a large, stochastic decision problem is divided into smaller pieces. The first approach builds a cache of policies for each part of the problem independently, and then combines the pieces in a separate, light-weight step. A second approach also divides the problem into smaller pieces, but information is communicated between the different problem pieces, allowing intelligent decisions to be made about which piece requires the most attention. Both approaches can be used to find optimal policies or approximately optimal policies with provable bounds. These algorithms also provide a framework for the efficient transfer of knowledge across problems that share similar structure.
Keywords: Markov deicision problem, MDP, planning, decomposition.
Pages: 422-430
PS Link:
PDF Link: /papers/98/p422-parr.pdf
BibTex:
@INPROCEEDINGS{Parr98,
AUTHOR = "Ron Parr ",
TITLE = "Flexible Decomposition Algorithms for Weakly Coupled Markov Decision Problems",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "422--430"
}


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