Enhanced Task Prioritization System Using Deep-Q-Network Model

This study presents the development and evaluation of a Deep Q Network model for intelligent task prioritization, addressing the limitations of static, manual methods in traditional to do systems. The research utilizes a synthetically generated dataset of task attributes including deadlines, complexity, and priority scores created with Python's Pandas and NumPy libraries to simulate real world scenarios. This dataset enabled the training and validation of a reinforcement learning agent that autonomously learns optimal prioritization strategies based on user behavior and contextual factors. The proposed Deep Q Network model was evaluated against baseline methods including Earliest Deadline First and a Static Eisenhower Matrix, demonstrating superior performance with 92.3% prioritization accuracy and a 93.5% deadline adherence rate. The results highlight the significant potential of deep reinforcement learning for dynamic task management, providing a foundation for future integration into productivity tools through a proposed system architecture incorporating Django and React Native.