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Abstract: Markov Decision Processes (MDPs) provide a coherent mathematical framework for planning under uncertainty. However, exact MDP solution algorithms require the manipulation of a value function, which specifies a value for each state in the system. Most real-world MDPs are too large for such a representation to be feasible, preventing the use of exact MDP algorithms. Various approximate solution algorithms have been proposed, many of which use a linear combination of basis functions to... (Update)
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BibTeX entry: (Update)
Carlos Guestrin, Daphne Koller, and Ronald Parr. Max-norm projections for factored MDPs. In Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI-01), Seattle, Washington, August 2001. Morgan Kaufmann. http://citeseer.ist.psu.edu/article/guestrin01maxnorm.html More
@inproceedings{ guestrin01maxnorm,
author = "Carlos Guestrin and Daphne Koller and Ronald Parr",
title = "Max-norm Projections for Factored {MDPs}",
booktitle = "{IJCAI}",
pages = "673-682",
year = "2001",
url = "citeseer.ist.psu.edu/article/guestrin01maxnorm.html" }
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59 Feature-based methods for large scale dynamic programming - Tsitsiklis, Van Roy - 1996
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7 Chelsea Publishing Co (context) - Cheney - 1982
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