Policy Iteration for Factored MDPs (2000)by D. Koller and R. Parr
Abstract:
Many large MDPs can be represented compactly using a dynamic Bayesian network. Although the structure of the value function does not retain the structure of the process, recent work has shown that value functions in factored MDPs can often be approximated well using a decomposed value function: a linear combination of restricted basis functions, each of which refers only to a small subset of variables. An approximate value function for a particular policy can be computed using approximate dynamic programming, but this approach (and others) can only produce an approximation relative to a distance metric which is weighted by the stationary distribution of the current policy. This type of weighted projection is illsuited to policy improvement. We present a new approach to value determination, that uses a simple closedform computation to directly compute a leastsquares decomposed approximation to the value function for any weights. We then use this value determination algorithm as a subroutine in a policy iteration process. We show that, under reasonable restrictions, the policies induced by a factored value function are compactly represented, and can be manipulated efficiently in a policy iteration process. We also present a method for computing error bounds for decomposed value functions using a variableelimination algorithm for function optimization. The complexity of all of our algorithms depends on the factorization of system dynamics and of the approximate value function.
Download Information
D. Koller and R. Parr (2000). "Policy Iteration for Factored MDPs." Proceedings of the 16th Annual Conference on Uncertainty in AI (UAI) (pp. 326334).


Bibtex citation
@inproceedings{Koller+Parr:UAI00,
author = "D. Koller and R. Parr",
booktitle = "Proceedings of the 16th Annual Conference on Uncertainty in AI (UAI)",
title = "Policy Iteration for Factored {MDP}s",
pages = "326334",
year = "2000",
}
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