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.

This research presented the design and evaluation of an intelligent task prioritization system using a Deep Q-Network (DQN), contributing a validated reinforcement learning model to the growing field of AI-driven productivity tools (Bader & Matthes, 2021). Through controlled simulations, we demonstrated that our DQN agent successfully learns to capture meaningful patterns in task attributes, enabling a dynamic prioritization strategy that significantly outperforms conventional rule-based methods like Earliest Deadline First and the Eisenhower Matrix in terms of deadline adherence, accuracy, and overall efficiency. The modular architecture of the proposed system underscores its potential for integration into real-world organizational contexts and existing productivity platforms. While this study is based on simulated data, it provides a crucial proof-of-concept and a robust performance baseline. Future work will focus on validating these findings with real-user studies, expanding the model’s context awareness to include factors like cognitive load, and incorporating explainable AI (XAI) techniques to enhance user trust and transparency. This work thus establishes a foundational framework for the next generation of adaptive, personalized task management systems.

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