# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 6 Discussion  
The proposed approach in the form of the WFQ continual-DRL algorithm and its implementation in the system model to achieve dynamic bandwidth allocation leads to the following observations:  
1. By dynamically adapting the weights assigned to each queue, the system can react to irregular traffic patterns, optimize bandwidth utilization, and maintain service quality. 2. Training the agent encourages the development of adaptive policies for weight assignment in WFQ systems, enabling real-time optimization based on monitored network states through interaction with the network environment. 3. Traffic patterns can change over time in dynamic network environments, meaning that the WFQ continual-DRL algorithm must continually adapt to these changes. Without an approach such as EWC, the agent may forget previously learned optimal policies, a phenomenon known as catastrophic forgetting. The EWC trains a regularization term added to the DRL loss function by penalizing significant deviations from parameters essential to previously learned tasks. This ensures that new learning does not overwrite valuable existing knowledge and maintains consistent performance across varying networks. In scenarios involving eight and twelve queues, a higher value of \(\lambda_{EWC}\) strengthens regularization, meaning that important knowledge persists as complexity increases. This improves performance by balancing adaptability and memory retention, ensuring the effectiveness of the WFQ continual-DRL learning algorithm. Figure 21: Correlation of real traffic data. 4. Expanding the number of queues enables a more fine-grained allocation of bandwidth, representing a test of the adaptability of the WFQ continual-DRL approach in optimizing resource distribution under dynamic traffic scenarios. The results indicate that the model successfully generalizes to larger queue sets while maintaining stable performance, as reflected in the stabilization of the reward. 5. When the number of queues is expanded, the parameters of the neural network structure in SAC-EWC need to be tuned by increasing the number of neurons, batch size, and buffer size, which enables the model to capture more complex patterns in the data. As a result, it enables the model to make more informed decisions, thereby improving performance and reward. 6. The implementation of EWC as one mechanism of continual learning requires additional computational resources due to the regularization term in the loss function. It is, therefore, essential to balance the benefits of EWC with the available computational capacity. In this investigation, a random function was used to select an appropriate strength for the EWC regularization term. Appropriate tuning ensures that the model maintains stability without restricting its ability to learn new patterns in the network traffic. 7. The steady decrease in MSE and MAE in both the eight- and twelve-queue systems suggests effective learning and enhanced prediction accuracy, ensuring reliable decision-making and consistent performance. 8. The RMSE results clearly indicate that the SAC enhanced with EWC yields a lower prediction error compared to the baseline SAC model. After approximately 200 episodes, while the standard SAC stabilizes in the range of 0.07 to 0.09, the enhanced model further reduces the RMSE and stabilizes between 0.05 and 0.07. This improvement demonstrates the effectiveness of EWC in preserving important learned parameters, resulting in more accurate and stable policy predictions over time. 9. In addition to improving prediction accuracy, as shown by the RMSE results, SAC with EWC also achieves a higher Jain's Fairness Index, indicating better resource allocation across various network flows. The fairness values are closer to 1, not only showing the enhanced model performance but also maintaining fairness in scheduling decisions. The importance of employing EWC in environments with a WFQ system lies in the fact that fairness is as critical as efficiency. 10.