# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 5 Results and Analysis
### Experiment 1: Eight Queues
From the column of MAE values, we see that this parameter is 0.583208 at episode 50, fluctuates slightly, and then improves to 0.535 at episode 300. Between episodes 400 and 700, the error remains controlled and stable, with a decline, indicating strong generalization capabilities. This suggests that the model is learning effectively and reducing prediction errors over time. The lowest MAE values are achieved at episode 1000 (0.510144), indicating that the model performs optimally towards the end of training. The learning process of the WFQ continual-DRL algorithm was efficient, as the estimation of errors was reduced over time, resulting in a well-optimized algorithm for dynamic traffic management. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Episode Number** & **SAC Rewards** & **SAC with EWC Rewards** \\ \hline
650 & \(-7.381\) & \(-2.427\) \\ \hline
700 & \(-7.226\) & \(-2.212\) \\ \hline
750 & \(-7.311\) & \(-2.266\) \\ \hline
800 & \(-6.999\) & \(-2.136\) \\ \hline
850 & \(-8.165\) & \(-2.070\) \\ \hline
900 & \(-8.011\) & \(-2.203\) \\ \hline
950 & \(-7.897\) & \(-2.384\) \\ \hline
1000 & \(-7.2\) & \(-2.048\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c} \hline \hline
**Episode Number** & **MSE** & **MAE** \\ \hline
50 & 0.612392 & 0.583208 \\ \hline
100 & 0.639665 & 0.614071 \\ \hline
150 & 0.597392 & 0.601428 \\ \hline
200 & 0.547177 & 0.572127 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The MSE and MAE results for eight queues. In Figure 8, during training, the model with eight queues of WFQ initially has an MSE curve that rises to 0.7, indicating the model's adaptation to changing queue dynamics. Then, it decreases and fluctuates within the range of 0.35-0.45, indicating a change into a steady-state. These bounded fluctuations confirm robust error minimization and model stability under a dynamic environment. According to Figure 9, the MAE rises above 0.6, indicating an early training prediction error, and then gradually declines and stabilizes, with fluctuations in the range of nearly 0.5-0.55, reflecting steady fluctuations and demonstrating the model's consistent performance in its dynamic state. Figure 8: The Mean Square Error (MSE) of eight queues. \begin{table}
\begin{tabular}{c c c} \hline
**Episode Number** & **MSE** & **MAE** \\ \hline
250 & 0.505351 & 0.553376 \\ \hline
300 & 0.467858 & 0.53599 \\ \hline
350 & 0.466958 & 0.539003 \\ \hline
400 & 0.418296 & 0.510907 \\ \hline
450 & 0.455936 & 0.532404 \\ \hline
500 & 0.464072 & 0.53763 \\ \hline
550 & 0.491030 & 0.552584 \\ \hline
600 & 0.445696 & 0.526634 \\ \hline
650 & 0.426627 & 0.513851 \\ \hline
700 & 0.407303 & 0.502012 \\ \hline
750 & 0.405632 & 0.500403 \\ \hline
800 & 0.413735 & 0.506038 \\ \hline
850 & 0.393311 & 0.492884 \\ \hline
900 & 0.382233 & 0.486044 \\ \hline
950 & 0.39783 & 0.497124 \\ \hline
1000 & 0.419463 & 0.510144 \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._Table 4 represents a comparison between the RMSE and Jain's Index of the WPG-DRL algorithm and the enhanced WPG continual-DRL algorithm. SAC integration with EWC yields lower average and minimum RMSE and appears to have better prediction accuracy. The SAC with the EWC model achieved higher fairness in resource allocation compared to the baseline SAC, indicating that the enhanced model maintains a better allocation bandwidth among network flows. Jain's Index confirms that incorporating EWC leads to more stable and fair decision-making during training in dynamic environment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Episode Number** & **RMSE for SAC** & **RMSE of SAC with EWC** & **Jain’s Index of SAC** &
\begin{tabular}{c} **Jain’s Index of SAC with** \\ **EWC** \\ \end{tabular} \\ \hline  
\hline \end{tabular}
\end{table} 
Table 4: The comparison of RMSE and Jain’s Index for eight queues. Figure 9: The Mean Absolute Error (MAE) of eight queues. Figure 10 shows a comparison between the curve of RMSE computed for SAC and SAC with EWC.