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Robust Modified Policy Iteration.
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- Author(s): Kaufman, David L.; Schaefer, Andrew J.
- Source:
INFORMS Journal on Computing. Summer2013, Vol. 25 Issue 3, p396-410. 15p. 6 Charts, 3 Graphs. - Source:
- Additional Information
- Subject Terms:
- Abstract: Robust dynamic programming (robust DP) mitigates the effects of ambiguity in transition probabilities on the solutions of Markov decision problems. We consider the computation of robust DP solutions for discrete-stage, infinite-horizon, discounted problems with finite state and action spaces. We present robust modified policy iteration (RMPI) and demonstrate its convergence. RMPI encompasses both of the previously known algorithms, robust value iteration and robust policy iteration. In addition to proposing exact RMPI, in which the "inner problem" is solved precisely, we propose inexact RMPI, in which the inner problem is solved to within a specified tolerance. We also introduce new stopping criteria based on the span seminorm. Finally, we demonstrate through some numerical studies that RMPI can significantly reduce computation time. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of INFORMS Journal on Computing is the property of INFORMS: Institute for Operations Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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