Research on collaborative control of digital application requirement design based on reinforcement learning

With the growing digital economy, designing digital application requirements is increasingly crucial. Traditional methods face challenges like inefficiency and slow adaptation to changes. This paper introduces a collaborative control method using reinforcement learning to address these issues. It highlights the benefits of reinforcement learning in tasks like requirement prioritization, progress tracking, and change control. A framework is proposed, including a requirement analysis module for feature extraction and prioritization, and a reinforcement learning module for algorithm implementation and optimization. Experiments confirm that this approach enhances the efficiency and quality of digital application requirement design.