AlphaRank: An Artificial Intelligence Approach for Ranking and Selection
  Problems
Ruihan Zhou L. Jeff Hong Yijie Peng
We introduce AlphaRank, an artificial intelligence approach to address the
fixed-budget ranking and selection (R&S) problems. We formulate the sequential
sampling decision as a Markov decision process and propose a Monte Carlo
simulation-based rollout policy that utilizes classic R&S procedures as base
policies for efficiently learning the value function of stochastic dynamic
programming. We accelerate online sample-allocation by using deep reinforcement
learning to pre-train a neural network model offline based on a given prior. We
also propose a parallelizable computing framework for large-scale problems,
effectively combining "divide and conquer" and "recursion" for enhanced
scalability and efficiency. Numerical experiments demonstrate that the
performance of AlphaRank is significantly improved over the base policies,
which could be attributed to AlphaRank's superior capability on the trade-off
among mean, variance, and induced correlation overlooked by many existing
policies.