The Optimization of Media Information Retrieval and Adaptive Information Management Based on Deep Reinforcement Learning

The rapid growth of large-scale information and the dynamic nature of user behaviors pose significant challenges for modern information retrieval systems, which often struggle to adapt to non-stationary environments and fail to fully utilize multimodal data, leading to suboptimal performance. To address these issues, this study proposes the adaptive deep reinforcement learning (RL) framework for information retrieval and management, which combines RL, multimodal data fusion, and an adaptive update mechanism to dynamically adjust to evolving user preferences and document collections. The adaptive deep RL framework for information retrieval and management employs a RL-based policy network to optimize retrieval strategies, a multimodal encoder to integrate diverse data sources, and an adaptive mechanism to maintain robustness in dynamic scenarios.