# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 5 Results and Analysis
### Training Real Traffic
Referring to the twelve-queue real traffic for the second twelve categories using MSE and MAE metrics, the MSE indicates a decreasing trend through the training process, showing advanced improvement in the model's predictive accuracy. Beginning at 0.788 in episode 50, the MSE declines to 0.511 by episode 350, which is effective in early-stage learning. Between episodes 400 and 700, the MSE values fluctuate within a range of around 0.400 to 0.525, suggesting a stabilization stage where the model fine-tunes its parameters. In the last phase, from 750 to 1000, the MSE continues its downward trend, reaching 0.412, which indicates the model's capacity to minimize errors over time. The MAE for real traffic of the recent twelve categories also displays a decreasing trend throughout the training process, indicating improvements in the model's average prediction accuracy. Initiating at 0.696 at episode 50, the MAE decreases to 0.560 at episode 350, indicating successful initial learning. Between episodes 400 and 700, the MAE values exhibit minor fluctuations, ranging from 0.499 to 0.571. From episode 750 to 1000, the MAE decreases, reaching 0.505, which highlights the model's steady improvement in minimizing average absolute errors. In conclusion of comparison Table, the simulation has higher MSE and MAE than real traffic 1 and traffic 2 for twelve queues of the dataset; it generalized well to real-world conditions. A G/G/1/K WPQ system parallel queuing model with a gamma distribution was formulated to simulate complicated network traffic. That indicates that the simulation accurately captured realistic traffic variability and system conditions. Thus, the WPQ scheduler, as implemented in continual and DRL framework, demonstrated robust performance, validating its effectiveness. According to Figure 14, the MSE curve initially rises to 0.8, indicating prediction error during early adaptation. In the last episodes of training, it then decreases and enters a steady fluctuating range nearly between 0.5 and 0.7. This bounded variability indicates that the model has stabilized and maintains reliable performance under dynamic multi-queue conditions, although in extended queues. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Episodes**} & \multicolumn{2}{c}{**Twelve Queues of Simulation**} & \multicolumn{2}{c}{**Twelve Queues of Real Traffic 1**} & \multicolumn{2}{c}{**Twelve Queues of Real Traffic 2**} \\ \cline{2-7}  & **MSE** & **MAE** & **MSE** & **MAE** & **MSE** & **MAE** \\ \hline
800 & 0.535042 & 0.577101 & 0.310399 & 0.437901 & 0.390746 & 0.489732 \\ \hline
850 & 0.496227 & 0.55432 & 0.349823 & 0.463259 & 0.381719 & 0.485158 \\ \hline
900 & 0.544107 &  
As shown in Figure 15, during the early training, the MAE curve rises to 0.7 in the initial step, indicating a high error. In the last episodes, it then drops and stabilizes within a fluctuating range between nearly 0.55 and 0.65. This behavior suggests the model's ability to generalize while maintaining consistent performance in a dynamic environment. According to Figure 16, the MSE curve for the twelve-queue system trained on real traffic exhibits a high initial error due to the complexity of the traffic patterns. Still, it gradually decreases and stabilizes between 3.0 and 3.5, indicating that the model has effectively learned and adapted to real-world traffic. For Figure 17, the MAE curve begins with a high error, reflecting meaningful early prediction deviations. As training advances, the error steadily declines. It stabilizes between nearly 4.0 and 4.5, demonstrating bounded fluctuations. That indicates the model has adapted well to real traffic dynamics and supports robust performance. Regarding Figure 18, the MSE curve for the second real traffic dataset starts high, reflecting the initial instability of the prediction. It gradually decreases and stabilizes within the range of 4.5 to 5.5, showing enhanced model learning. This trend suggests that the model is evolving to accommodate more diverse traffic patterns over time.