Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach
Abstract
Engineering system design, viewed as a decision-making process, faces challenges due to complexity and uncertainty. In this paper, we present a framework proposing the use of the Deep Q-learning algorithm to optimize the design of engineering systems. We outline a step-by-step framework for optimizing engineering system designs. The goal is to find policies that maximize the output of a simulation model given multiple sources of uncertainties. The proposed algorithm handles linear and non-linear multi-stage stochastic problems, where decision variables are discrete, and the objective function and constraints are assessed via a Monte Carlo simulation. We demonstrate the effectiveness of our proposed framework by solving two engineering system design problems in the presence of multiple uncertainties, such as price and demand.
Cite
@article{arxiv.2312.17284,
title = {Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach},
author = {Ramin Giahi and Cameron A. MacKenzie and Reyhaneh Bijari},
journal= {arXiv preprint arXiv:2312.17284},
year = {2024}
}