On the Convergence of Policy Iteration in Stationary Dynamic Programming
Martin L. Puterman, Shelby L. Brumelle
Source abstract
The policy iteration method of dynamic programming is studied in an abstract setting. It is shown to be equivalent to the Newton-Kantorovich iteration procedure applied to the functional equation of dynamic programming. This equivalence is used to obtain the rate of convergence and error bounds for the sequence of values generated by policy iteration. These results are discussed in the context of the finite state Markovian decision problem with compact action space. An example is analyzed in detail.
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