Uncertainty in Artificial Intelligence
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A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes
Nevin Zhang, Stephen Lee, Weihong Zhang
Abstract:
We present a technique for speeding up the convergence of value iteration for partially observable Markov decisions processes (POMDPs). The underlying idea is similar to that behind modified policy iteration for fully observable Markov decision processes (MDPs). The technique can be easily incorporated into any existing POMDP value iteration algorithms. Experiments have been conducted on several test problems with one POMDP value iteration algorithm called incremental pruning. We find that the technique can make incremental pruning run several orders of magnitude faster.
Keywords: POMDPs, value iteration, efficiency
Pages: 696-703
PS Link: ftp://ftp.cs.ust.hk/pub/lzhang/uai99.ps.gz
PDF Link: /papers/99/p696-zhang.pdf
BibTex:
@INPROCEEDINGS{Zhang99,
AUTHOR = "Nevin Zhang and Stephen Lee and Weihong Zhang",
TITLE = "A Method for Speeding Up Value Iteration in Partially Observable Markov Decision Processes",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "696--703"
}


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