Research on Large-Scale Data Processing and Dynamic Content Optimization Algorithm Based On Reinforcement Learning

With the advent of the era of big data, Internet services are faced with the challenges of massive data processing and content distribution. Traditional network management methods have gradually exposed their limitations when dealing with the rapidly growing user needs and complex application scenarios. Therefore, this paper proposes a dynamic data scheduling and content optimization scheme based on deep reinforcement learning, which optimizes data stream processing and real-time content distribution through an agent decision model to improve system efficiency and user experience. In this paper, we construct a decision process considering multiple network states and behavior choices, design an adaptive reward mechanism, and improve the training efficiency and stability of the agent by combining the near-end strategy optimization method. At the same time, the multi-agent framework is extended to enable it to cope with complex network environments and tasks, ensuring efficient operation in the face of dynamic changes and partial observability. A large number of experimental results show that the proposed algorithm is better than the traditional method in many indexes such as delay, bandwidth management and data loss, and shows strong robustness and stability. This study provides theoretical support and practical guidance for intelligent optimization in large-scale network environment, and has a wide application prospect.