# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 2 Relate Work
This research underscores the need to consider both optimization techniques and the choice of initial conditions to improve the  
Figure 1: Hard parameter sharing and soft parameter sharing network structure diagrams. effectiveness of multitask learning, particularly in complex tasks with varying degrees of difficulty. **Multitask Reinforcement Learning. ** Detailed background on multitask reinforcement learning can be found in [31], where the core challenge lies in effectively coordinating the learning processes of multiple tasks with potentially conflicting objectives. Combining reinforcement learning with multitask learning has been shown to be effective in [32], particularly in scenarios requiring adaptive policy sharing. A critical challenge in this paradigm is designing agent architectures that can simultaneously maintain task-specific policy specialization while enabling positive knowledge transfer, especially when dealing with non-stationary reward distributions across tasks. The Actor-Critic framework, as exemplified by the Asynchronous Advantage Actor-Critic (A3C) algorithm [33, 34], provides a natural architecture for addressing these challenges through its dual mechanism of policy improvement (Actor) and value estimation (Critic). However, applying this architecture to multi-task scenarios introduces unique complexities: (1) The state representation must encode cross-task performance metrics to facilitate inter-task coordination; (2) the action space needs to satisfy dynamic constraints for task weight allocation; and (3) the reward function design must balance immediate performance improvements with long-term policy stability. Our work directly addresses these challenges through a novel state encoding scheme that captures task performance variance, and a constrained action space formulation ensuring valid weight distributions. Recent advances in parameter sharing architectures, such as PathNet [35] and routing networks [36], demonstrate the potential of structural adaptation for mitigating negative transfer. However, these approaches primarily focus on static parameter partitioning and lack mechanisms for dynamic task prioritization during training. The PaCo method [37] addresses this through parameter combination strategies but relies on heuristic update rules. Our approach innovates by integrating the A3C's policy-gradient framework with dynamic weight adaptation, where the Critic evaluates not just state values but also inter-task performance relationships, enabling more informed weight adjustment decisions. The key innovation in our network scenario lies in the tight coupling between the Actor-Critic architecture and the multi-task learning dynamics. Specifically, the Actor network learns to generate weight adjustment policies that must satisfy both immediate performance requirements (through the Critic's value estimates) and long-term training stability constraints (enforced through our experience replay mechanism). This dual optimization objective addresses the fundamental tension in multi-task RL between exploiting current high-performing task policies and exploring better weight configurations for underperforming tasks. The experience replay buffer stores historical task imbalance states. This enables learning robust weight policies that generalize across training phases.