Simulation Optimization in the New Era of AI
Published Online:13 Oct 2023https://doi.org/10.1287/educ.2023.0264
Abstract
We review simulation optimization methods and discuss how these methods underpin modern artificial intelligence (AI) techniques. In particular, we focus on three areas: stochastic gradient estimation, which plays a central role in training neural networks for deep learning and reinforcement learning; simulation sample allocation, which can be used as the node selection policy in Monte Carlo tree search; and variance reduction, which can accelerate training procedures in AI.
Funding: This work was supported in part by the U.S. Air Force Office of Scientific Research [Grant FA95502010211], the National Natural Science Foundation of China [Grants 72250065, 72022001, and 71901003], and U.S. National Science Foundation [Grant FAIN 2123683].
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