In recent years, multi-task learning (MTL) has been shown to have considerable potential for enhancing model performance through the inter-task sharing of knowledge. Nevertheless, the effective balancing of optimization across diverse tasks remains a significant challenge. This paper presents a novel Deep Reinforcement Learning (DRL)-based framework that employs an Actor-Critic architecture to dynamically adjust task weights. In contrast to traditional methodologies that depend on heuristic rules or predefined assumptions about inter-task relationships, our approach autonomously learns optimal weighting strategies through interactions between an intelligent agent and the multi-task environment. The experimental results obtained across multiple datasets demonstrate that our method not only enhances overall performance and achieves a more balanced distribution of tasks but also maintains high training efficiency. Furthermore, the framework’s robust generalization and adaptability make it well-suited for addressing complex and dynamic learning scenarios.