Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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.
['Lauren A. Hannah', 'Warren B. Powell']
I am writing a research paper on a idea Evolutionary Policy Iteration Under a Sampling Regime for Stochastic Combinatorial Optimization
[{"from": "human", "value": "I am writing a research paper on a idea Evolutionary Policy Iteration Under a Sampling Regime for Stochastic Combinatorial Optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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."}]