Numerically Solving Diffusion Control and Queueing Control Problems Based on Neural Networks

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• Nian Si (University of Chicago)
• Tuesday 06 August 2024, 10:00-11:00
• External.

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SSDW02 - Stochastic reflection

Motivated by applications in queueing theory, we consider a class of singular stochastic control problems whose state space is the d-dimensional positive orthant. The original problem is approximated by a drift control problem, to which we develop and illustrate a simulation-based computational method that relies heavily on deep neural network technology. Furthermore, we develop and implement disrete-review polices that effectively solve the pre-limit queueing control problems. To show that nearly optimal solutions are obtainable using those methods, we present computational results for a variety of queueing network examples that have appeared previously in the literature. This talk is based on joint works with Michael Harrison from Stanford university, and Baris Ata from the University of Chicago.

This talk is part of the Isaac Newton Institute Seminar Series series.

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