Research on the Optimization of Virtual Reality Art Design Environment Construction Based on Deep Learning

The application demand of virtual reality technology in multiple industries continues to grow, but how to efficiently build and optimize virtual environments, especially in the generation and real-time rendering of large-scale complex scenes, is still a problem to be solved. This paper proposes a virtual reality environment construction optimization algorithm based on DRL. Through the combination of deep neural network and Q-learning, the algorithm can dynamically optimize the layout, rendering quality and interactive response of the scene while automatically generating the virtual environment, thereby improving the virtual reality experience. The experiment results indicate that the proposed SRE has a remarkable advantage in generating and optimizing the virtual environment. Compared with the traditional methods (such as Rule Optimization and Genetic Algorithm), the performance of the proposed algorithm is improved by 20%, the rendering efficiency is improved by 25%, and the interactive response time is reduced by 30%. This result shows the potential of deep reinforcement learning in virtual reality environment construction and provides a new solution for efficient and realtime virtual scene optimization. This study not only proposes an innovative deep reinforcement learning algorithm, but also demonstrates its feasibility and advantages in practical applications.