# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 1 Introduction  
In recent years, deep learning has demonstrated remarkable efficacy across various application domains. By emulating human cognitive processes and intelligent behaviors, deep learning methods have proven highly effective in addressing complex tasks. Multi-task learning [1], as a learning paradigm that can handle multiple tasks simultaneously, offers significant advantages over single-task approaches through shared representation learning and cross-task knowledge transfer. This capability mirrors human intelligence where skills acquired from related tasks synergistically enhance overall performance. The improved generalization stems from learning robust feature representations that capture common patterns across tasks while preserving task-specific distinctions. Despite its promise, multi-task learning faces a critical challenge: effectively balancing competing optimization objectives across tasks. Traditional approaches typically employ static weighting schemes (e.g., equal weights or fixed priority rankings) that assume that task relationships remain constant during training [2]. Such methods contrast sharply with emerging dynamical systems perspectives that reveal deep neural networks as evolving trajectories in continuous-time frameworks [3], where task interactions exhibit complex temporal dependencies. Practical challenges arise from diverging task characteristics including:  
* Heterogeneous data distributions and loss landscapes;
* Varying gradient magnitudes and optimization difficulties;* Time-varying task correlations in dynamic environments. Recent advances in dynamical system interpretations of deep learning have provided new theoretical foundations for addressing these challenges. Neural ODEs demonstrate how continuous-time formulations enable smoother optimization trajectories [3], while transformer architectures reveal emergent particle-like interactions in representation spaces [4]. These insights suggest that effective multi-task optimization requires modeling task relationships as dynamic forces rather than static priors [5]. Current dynamic weighting methods, however, remain limited by three key constraints:  
Heuristic dependency: Most approaches (e.g., uncertainty weighting [6], gradient norm alignment [7]) rely on manually designed update rules that may not generalize across architectures and tasks. Myopic optimization: Existing methods typically make greedy weight adjustments based on immediate gradient information, lacking long-term planning for optimal training trajectories. Passive adaptation: Conventional strategies reactively respond to current task conflicts rather than proactively shaping beneficial task interactions. To overcome these limitations, we propose a fundamentally different paradigm: Deep Reinforcement Learning-Driven Dynamic Weighting. Our key innovations and distinctions from prior work include:  
* **A strategic decision-making framework**: Formulating weight adaptation as a Markov Decision Process enables long-term optimization planning through value function estimation, addressing the myopia of existing methods. * **Automated relationship discovery**: Unlike methods requiring pre-defined task similarity metrics, our DRL agent autonomously learns inter-task relationships through environment interactions, eliminating manual bias in weight initialization. * **Adaptive credit assignment**: The framework introduces a novel reward mechanism that jointly optimizes immediate task balancing and long-term representation quality, overcoming the local optimum trap in greedy gradient-based methods. The proposed approach establishes three technical advances over conventional adaptive weighting methods: (1) An end-to-end trainable weight policy network that generalizes across task configurations, and (2) a temporal attention mechanism for modeling time-varying task correlations. To evaluate the efficacy of the proposed method, we conduct experiments on three datasets.