AlphaRank: An Artificial Intelligence Approach for Ranking and Selection Problems
This work addresses ranking and selection problems, which are important for optimization and decision-making in fields like operations research, but it appears incremental as it builds on existing R&S procedures with AI enhancements.
The authors tackled the fixed-budget ranking and selection problem by introducing AlphaRank, an AI approach that combines Monte Carlo simulation-based rollout policies with deep reinforcement learning for offline pre-training. Numerical experiments showed AlphaRank significantly outperformed base policies, with improved trade-offs among mean, variance, and induced correlation.
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.