Optimizing document management with reinforcement learning: A framework for continuous improvement

Document management is an effective challenge for many organizations trying to get the best out of their workflows, retrieval efficiency and adaptability to dynamic operation needs. Using a reinforcement learning (RL) based approach, this work proposes a framework for constantly learning to improve document organization, classification and retrieval. The framework utilizes the flexibility of RL to learn optimal document management policies dynamically as it learns from user behaviour, document metadata and system performance metrics. Results from the experimental runs show that the proposed RL framework greatly outperforms the rule-based and machine-learning approaches, resulting in substantial improvements in retrieval speed, classification accuracy, and system efficiency. Additionally, the benefits of reinforcement learning are illustrated in comparison with baseline models for solving complex evolving document management problems. The research proposed here serves as a foundation for integrating RL into document management systems and the ongoing learning and optimization required by the demands of modern organizations.