Exploration of Adaptive Environment Design Strategy Based on Reinforcement Learning in CAD Environment

This article tackles the challenge of designing CAD (Computer-Aided Design) environments and introduces an adaptive design strategy rooted in Reinforcement Learning (RL). We delve into the intricacies and variations inherent in CAD environment design, emphasizing the need for adaptability and exploring RL's potential to enhance CAD intelligence. To validate our strategy's efficacy, we've constructed a simulation platform that mimics real-world CAD design and interaction processes. Our findings reveal that the RL-driven adaptive design approach seamlessly adjusts CAD environment parameters and configurations to align with evolving design tasks, offering optimal design support. In contrast to traditional CAD setups, this adaptive approach notably boosts design efficiency, minimizes errors, and elevates user satisfaction. This strategy heralds a new era of intelligent CAD environment development, paving the way for technological advancements in engineering design. Its insights offer valuable guidance to scholars and practitioners alike, fostering continuous innovation in CAD environmental design technology. © 2024 U-turn Press LLC.