AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems

Understand

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.