# A Multi-Task Dynamic Weight Optimization Framework Based on Deep Reinforcement Learning
## 3 Methodology
### Deep Reinforcement Learning Agents Based on Experience Replay: An Actor-Critic Approach
**Actor Network Update**:  
Update the Actor network parameters by maximizing the Critic's evaluation of the value of the Actor's action output:  
\[L_{\text{actor}}=-\mathbb{E}[Q(s,\tilde{\alpha})] \tag{6}\]  
where \(\tilde{\alpha}\) is the action output by the Actor in state \(s\). Gradient ascent optimization is used to adjust the policy parameters, enabling the Actor to achieve a higher expected value for the action selection in a given state. At the end of each training cycle, the latest samples generated by the agent's interaction with the environment are stored in the experience playback buffer. During training, the Actor and Critic networks are updated using randomly sampled samples from the buffer. Experience playback not only reduces the over-reliance on new data (improving data efficiency) but also smoothes the data distribution (enhancing training stability), which helps prevent the policy from falling into a local optimum. With this DRL agent and its training mechanism, we can achieve adaptive weight assignment in multi-task learning environments, effectively improving the overall performance and inter-task fairness. **C. Multi-task learning framework based on deep reinforcement learning**  
Based on the above multi-task classification network and DRL agents, we propose an innovative framework that integrates DRL with multi-task learning to achieve adaptive task weight optimization. The framework dynamically adjusts the task weights during the training process, effectively balancing the multi-task learning process to improve the overall performance and generalization ability of the model. During the training cycle, the DRL agent continuously interacts with the multi-task model. At the beginning of each training cycle, a state is constructed based on the current accuracy rate of each task, the standard deviation of the accuracy rate, and the weight assignment of the previous round, and new task weights \(\{\alpha_{t}\}\) are generated by the DRL agent. These weights satisfy a minimum limit \(\alpha_{\text{min}}\), ensuring that each task has a basic weight assignment that is not completely ignored during training. Subsequently, the multi-task model is trained using the updated weights and performance is evaluated on a validation set, which is fed back to the DRL agent by calculating rewards to update its policy parameters. This process iterates in a continuous loop so that the DRL agent gradually learns the optimal weight adjustment strategy and dynamically balances the learning progress of each task. Algorithm 1 gives an overview of the training process. The parameter \(\lambda\) in the reward function fine-tunes the balance between different tasks. When \(\lambda\) is high, the system tends to improve the overall average performance; when \(\lambda\) is low, more emphasis is placed on the improvement of poorer performing tasks. By adjusting \(\lambda\), a compromise between performance improvement between tasks and overall accuracy optimization can be achieved. At the same time, the introduction of the experience playback mechanism allows the DRL agent to make full use of historical experience and improve training stability, effectively coping with the complexity and uncertainty inherent in multi-task learning. ```
1:Initialize a multi-task network (\(MTN\)), including shared layers and task-specific classifiers. 2:Initialize DRL agents with state and action dimensions. 3:Initialize the experience replay buffer Memory. 4:for epoch = 1 to Epochdo
5: Construct the current state \(s_{t}\) based on task accuracies and previous round task weights. 6: Compute action \(a_{t}=DRL.\text{actor}(s_{t})\). 7: Generate new task weights based on actions \(\alpha_{t}=\text{process}(a_{t})\). 8: Update task loss weights in \(MTN\) using \(\alpha_{t}\). 9:for each batch in training data do
10: Perform forward propagation through \(MTN\) to obtain outputs for each task. 11: Compute the loss for each task \(\mathcal{L}_{t}\). 12: Calculate the total loss \(\mathcal{L}=\sum_{t=1}^{T}\alpha_{t}\mathcal{L}_{t}\).