Abstract
This paper studies a value function iteration algorithm based on nonexpansive function approximation and Monte Carlo integration that can be applied to almost all stationary dynamic programming problems. The method can be represented using a randomized fitted Bellman operator and a corresponding algorithm that is shown to be globally convergent with probability one. When additional restrictions are imposed, an O P(n -1/2) rate of convergence for Monte Carlo error is obtained.
| Original language | English |
|---|---|
| Pages (from-to) | 251-264 |
| Number of pages | 14 |
| Journal | Journal of Economic Dynamics and Control |
| Volume | 37 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - Jan 2013 |
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