Referenced Items are not available for this document.
This article modifies the evolutionary policy selection algorithm of Chang et al., which was designed for use in infinite horizon Markov decision processes (MDPs) with a large action space to a discrete stochastic optimization problem, in an algorithm called Evolutionary Policy Iteration-Monte Carlo (EPI-MC). EPI-MC allows EPI to be used in a stochastic combinatorial optimization setting with a finite action space and a noisy cost (value) function by introducing a sampling schedule. Convergence of EPI-MC to the optimal action is proven and experimental results are given.
Published in:
Automatic Control, IEEE Transactions on
(Volume:55
,
Issue:
5
)
Date of Publication: May 2010
- Page(s):
- 1254 - 1257
- ISSN :
- 0018-9286
- INSPEC Accession Number:
- 11285348
- Digital Object Identifier :
- 10.1109/TAC.2010.2042766
- Product Type:
- Journals & Magazines
- Date of Publication :
- 08 February 2010
- Date of Current Version :
- 06 May 2010
- Issue Date :
- May 2010
- Sponsored by :
- IEEE Control Systems Society
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