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

Abstract

The main goal of this study is to combine computer-aided design (CAD) and reinforcement learning (RL) technologies to develop an adaptive water environment optimization strategy to cope with the increasingly severe water resource challenges. In this article, an intelligent decision-making system is constructed according to the actual water environment. The system can automatically adjust management strategies to maximize water quality improvement and ecosystem protection while minimizing management costs. To assess the strategy's efficacy, a range of simulation experiments have been devised to mimic its performance across varied water environmental conditions. The results show that the adaptive water environment optimization strategy is superior to the traditional management method in many key indicators, which significantly improves the efficiency and effect of water environment management. In addition, the strategy shows good adaptability and robustness and can maintain stable performance under complex and changeable water environment conditions. This study not only provides new theoretical and methodological support for the field of water environment optimization, but also points out the direction for future related research. It is expected that this strategy will play a greater role in practical application and contribute to the sustainable utilization and management of water resources.