# Dynamic Decision Making in Engineering System Design: A Deep Q-Learning Approach

arXiv: https://arxiv.org/abs/2312.17284 · HTML: https://mlsanity.com/paper/2312.17284/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 67 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 6 of 6)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Continuous Deep Q-Learning with Model-based Acceleration](https://mlsanity.com/paper/1603.00748/) (2016)
- [Simulation optimization: A review of algorithms and applications](https://mlsanity.com/paper/1706.08591/) (2017)
- [Knowledge-Based Sequential Decision-Making Under Uncertainty](https://mlsanity.com/paper/1905.07030/) (2019)
- [Benders, Nested Benders and Stochastic Programming: An Intuitive Introduction](https://mlsanity.com/paper/1312.3158/) (2013)

## Abstract (arXiv)

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.