# Optimizing Weighted Fair Queuing with Deep Reinforcement Learning for Dynamic Bandwidth Allocation
## 5 Results and Analysis
### Training Real Traffic
A noteworthy development is observed in the value of the MSE, which drops to 0.5302 at episode 400 and remains low, at approximately 0.5263, until episode 450. In the steady state, from episodes 400 to 700, the MSE values exhibit strong consistency over time, indicating that the model makes predictions that are consistent and accurate. Small fluctuations are expected during training due to the dynamic nature of the environment. The lowest MSE value of 0.4909 is observed at episode 1000, confirming the optimal performance of the model. The results for the last 200 episodes suggest that the model has achieved high \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Queues**} & \multirow{2}{*}{**Categories**} & \multicolumn{2}{c}{**Average Arrival Rates**} \\  & & **(Packets/s)** \\ \hline
1 & streaming & 0.051215 \\ \hline
2 & RPC & 0.134050 \\ \hline
3 & VPN & 0.017920 \\ \hline
4 & shopping & 0.019674 \\ \hline
5 & social media & 0.078182 \\ \hline
6 & data transfer & 0.205775 \\ \hline
7 & Mining & 0.016681 \\ \hline
8 & Game & 0.030445 \\ \hline
9 & remote access & 0.040550 \\ \hline
10 & database & 0.016725 \\ \hline
11 & Video & 0.026934 \\ \hline
12 & unspecified & 0.036511 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The average arrival rate of the second twelve categories. accuracy and stability, as the variation between episodes 750 and 950 remains controlled. From the column of MEA values at the start of training, showing that the MEA is 0.6437 at episode 50 and increases slightly to 0.6765 at episode 100. A slight increase is expected when the model adjusts at episode 300 during training, and the MEA value stabilizes, indicating that the model is starting to learn effectively. The MEA value decreases from episode 400 (0.5754) to episode 750 (0.5640), indicating that the model is learning to make more accurate predictions. The fluctuation between episodes 600 and 700 (between around 0.5994 and 0.6057) suggests that the model exhibits normal and healthy training behavior. At episode 1000, the lowest MAE value of 0.5528 is recorded, indicating optimal performance. From episode 850 (MAE values 0.5543 to 0.5528), the model maintains a constant learning rate with minimal fluctuations. \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
50 & 0.868022 & 0.643713 & 0.533128 & 0.573652 & 0.788154  
According to the evaluation of the twelve-queue real traffic for the first twelve categories using MSE and MAE metrics, there is an improvement in model stability over time. According to the MSE, a consistent decline across the training episodes indicates advanced improvement in the model's predictive accuracy. Beginning at 0.533 in episode 50, the MSE declines to 0.317 in episode 350, reflecting effective start learning. Between episodes 400 and 700, the MSE values fluctuate between 0.322 and 0.356, suggesting a stabilization phase where the model fine-tunes its parameters. In the final episode, from 750 to 1000, the MSE continues its downward trend, reaching 0.331, which highlights the model's capacity to minimize errors over time. The MAE shows below throughout the training process, suggesting improvements in the model's average prediction accuracy, which starts at 0.574 in episode 50, then decreases to 0.441 in episode 350, indicating successful initial learning. Between episodes 400 and 700, the MAE values exhibit small fluctuations, ranging from 0.445 to 0.468, which reflects the model's efforts to refine its predictions and handle more nuanced patterns in the data. From episode 750 to 1000, the MAE continues to decline, reaching 0.453, which underscores the model's steady improvement in minimizing average absolute errors.