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Evolutionary Policy Iteration Under a Sampling Regime for Stochastic Combinatorial Optimization

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2 Author(s)
Hannah, L.A. ; Dept. of Oper. Res. & Financial Eng., Princeton Univ., Princeton, NJ, USA ; Powell, W.B.

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

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