## 5 Experimental Analysis
### Statistical Tests  
The results were obtained by collecting the average values of evaluation metrics over the final 500 episodes, representing the stable performance of each experiment. Each algorithm yielded 10 sample points for the three system-level evaluation metrics, respectively. To determine whether the performance differences among algorithms on these metrics are statistically significant, the Friedman test was applied for overall significance analysis. The test results show that the \(p\)-values for the three metrics are 0.0008, 0.00015, and 0.0034, respectively, all well below the significance level of 0.05, indicating significant differences among the algorithms on all metrics. Based on this, the Nemenyi post hoc test was further conducted to analyze the pairwise performance differences between algorithms, aiming to reveal which specific algorithm pairs exhibit statistically significant performance disparities. The significance heatmaps of algorithmic differences under the three metrics are illustrated in Figure 7. \begin{table}
\begin{tabular}{l l c c} \hline \hline
**Comparison** & **Metric** & \(\boldsymbol{\zeta=1.0}\) & \(\boldsymbol{\zeta=1.5}\) \\ \hline DDQN + PER & AWD (\(\downarrow\)) & 96.83\% & 99.54\% \\ vs. & ARR (\(\downarrow\)) & 97.99\% & 99.76\% \\ Baseline & ARL (\(\downarrow\)) & 95.28\% & 99.18\% \\ \hline DDQN + PER & AWD (\(\downarrow\)) & 95.76\% & 99.38\% \\ vs. & ARR (\(\downarrow\)) & 97.87\% & 99.68\% \\ ABC & ARL (\(\downarrow\)) & 91.60\% & 99.09\% \\ \hline DDQN + PER & AWD (\(\downarrow\)) & 41.93\% & 5.20\% \\ vs. & ARR (\(\downarrow\)) & 58.47\% & 11.73\% \\ DQN & ARL (\(\downarrow\)) & 45.08\% & 56.49\% \\ \hline DDQN + PER & AWD (\(\downarrow\)) & 36.23\% & 52.99\% \\ vs. & ARR (\(\downarrow\)) & 53.87\% & 65.22\% \\ DDQN & ARL (\(\downarrow\)) & 43.13\% & 43.94\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: DDQN + PER algorithm reduction compared to other algorithms for \(\zeta=1.0\) and \(\zeta=1.5\). This table presents the performance improvements of DDQN-PER compared to other baseline algorithms under different load conditions, with (\(\downarrow\)) used to indicate a reduction and optimization in the corresponding indicators. Figure 6: The system performance comparison when \(\zeta=1.5\). The trends of all three performance indicators under different algorithms within a limited number of training episodes are illustrated. The first row presents the indicator variations for the two traditional algorithms, while the second row shows the results for the three reinforcement learning algorithms. The colored shaded areas represent the fluctuation range across ten independent experiments. Polynomial fitting is applied to these areas to highlight the overall trend of the indicators. While the above tests focus on performance differences between algorithms, this study also evaluates within-group consistency by introducing the Fleiss' Kappa coefficient to measure the stability and behavioral consistency of similar algorithms under repeated experimental runs. The five algorithms were divided into two groups for consistency analysis: the traditional group (Baseline and ABC) and the reinforcement learning group (DQN, DDQN, and DDQN-PER). Based on the existing average values, normalization and discretization were performed to obtain \(AWD^{\text{disc}}\), \(ARR^{\text{disc}}\), and \(ARL^{\text{disc}}\). Referring to the interpretation standards of the Kappa coefficient proposed by [PERSON] and [PERSON] [31], we evaluated the consistency of each algorithm across the three metrics, and the final results are shown in Table 5. The consistency of traditional algorithms across all three metrics is relatively low, with kappa values all below 0.2, indicating that non-learning-based algorithms exhibit considerable fluctuations in dynamic environments and inconsistent behavioral patterns.