Uncertainty in Artificial Intelligence
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Solving POMDPs by Searching in Policy Space
Eric Hansen
Abstract:
Most algorithms for solving POMDPs iteratively improve a value function that implicitly represents a policy and are said to search in value function space. This paper presents an approach to solving POMDPs that represents a policy explicitly as a finite-state controller and iteratively improves the controller by search in policy space. Two related algorithms illustrate this approach. The first is a policy iteration algorithm that can outperform value iteration in solving infinitehorizon POMDPs. It provides the foundation for a new heuristic search algorithm that promises further speedup by focusing computational effort on regions of the problem space that are reachable, or likely to be reached, from a start state.
Keywords:
Pages: 211-219
PS Link:
PDF Link: /papers/98/p211-hansen.pdf
BibTex:
@INPROCEEDINGS{Hansen98,
AUTHOR = "Eric Hansen ",
TITLE = "Solving POMDPs by Searching in Policy Space",
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 = "211--219"
}


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