  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.
