Fixed-Precision Ranking and Selection as Markov Decision Process

In this study, we conceptualize the fixed-precision ranking and selection (R&S) problem as a stochastic control problem and subsequently model it using Markov Decision Process (MDP). Our approach aims to study the fixed-precision paradigm of R&S within a stochastic dynamic programming framework. To address the fixed-precision R&S challenge, we employ AlphaRank, an innovative artificial intelligence technique for tackling fixed-budget R&S problems. This procedure intelligently handles learning and decision-making through deep reinforcement learning, thereby addressing the R&S problem where the mean differences between various alternatives tend to approach zero. We use a numerical example to illustrate the efficacy of AlphaRank in solving fixed-precision R&S problems. Notably, this method mitigates, to a certain extent, the issues faced by traditional fixed-precision programs, which often require excessive sampling to reach a specified accuracy level.