Exploration of Optimizing Advertising Design Using CAD and Deep Reinforcement Learning

Abstract

This article delves into the integrated utilization of CAD (Computer-Aided Design) technology and DRL (Deep Reinforcement Learning) in the realm of advertising design, proposing a novel model for innovative designs. Through an examination of the fundamentals of DL (Deep Learning) and reinforcement learning, along with their potential applications in advertising design, the study formulates a model that fuses CAD technology with DRL. The primary objective of this model is to achieve automation and intelligence in advertising design, thereby enhancing creativity, minimizing costs, and boosting efficiency. To ascertain the efficacy of the proposed model, simulation experiments were conducted, utilizing a comprehensive dataset of advertising. The experimental findings indicate that the advertising design model, which integrates CAD and DRL, exhibits significant advantages in augmenting design creativity, lowering design costs, and elevating design efficiency. Compared with the traditional advertising design method, this model can better understand the needs of users and market trends and generate more creative and attractive advertising design schemes. Its research provides a valuable reference for researchers in related fields and promotes the application research of DRL in creative fields such as advertising design.