## 4 Experimental Evaluation
### Performance Analysis  
In the performance evaluation, the ERA-MADDPG algorithm demonstrates competitive results in terms of delay, throughput, and packet loss rate compared with MADQN and SR-DRL. Specifically, under 5-15% topological node or link changes (representing extreme scenarios such as simultaneous multi-node failures or large-scale link reconfiguration), the average delay of ERA-MADDPG ranges from 136 ms to 139 ms, while the packet loss rate remains below 6%. These change amplitudes simulate critical network disruptions that require rapid routing adaptation. To assess whether these values are tolerable in practical applications, we compare them with existing SDN-based systems. For example, in the study of Intrusion Detection Systems (IDS) based on SDN, the authors reported that the Ryu controller introduces a processing delay of approximately 100-150 ms in traffic monitoring scenarios [25]. Our proposed algorithm's delay falls within this range, indicating that the additional latency introduced by multi-agent collaboration and CNN-based processing is comparable to typical SDN controller overheads and is thus acceptable for most real-time networking applications, such as non-critical enterprise networks or delay-tolerant IoT systems. Notably, the packet loss rate of ERA-MADDPG is significantly lower than that of the single-agent SR-DRL algorithm (7% under node changes), which benefits from multi-agent collaborative learning that optimizes global routing decisions. Although higher packet loss rates (6% under link changes) may occur in extreme scenarios, they can be mitigated by adjusting reward function weights (e.g., increasing the penalty for packet loss in Equation (19)) to prioritize reliability in sensitive networks. Furthermore, the trade-off between convergence speed and delay is worth emphasizing. To further explore the impact of packet loss penalty, we increased the reward weight \(n_{3}\) for packet loss in Equation (19) from its default value (0.3) to 0.6, while proportionally adjusting \(n_{1}\) and \(n_{2}\) to maintain the normalization. The result showed a notable reduction in average packet loss rate--from 4.0% to 2.3%--at the cost of a slight increase in average delay (from 136 ms to 140 ms). This validates that reward-weight tuning can serve as an effective mechanism to prioritize reliability in scenarios where low packet loss is critical. ERA-MADDPG achieves faster re-convergence (over 25% improvement) than comparative algorithms when facing topology changes, which is critical for maintaining network stability. In dynamic environments where rapid adaptation to failures or traffic shifts is prioritized, the algorithm's delay and loss rates represent a reasonable compromise for enhanced resilience. In summary, the delay and packet loss introduced by ERA-MADDPG are within tolerable limits for SDN-based routing systems, especially considering its superior convergence efficiency and multi-agent collaboration advantages. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Average Throughput** \\ **(Mbps)**\(\pm\)**SD** \\ \end{tabular} & \
\begin{tabular}{c} **Average Delay** \\ **(ms)**\(\pm\)**SD** \\ \end{tabular} &
\begin{tabular}{c} **Average Packet Loss** \\ **Rate (\%)**\(\pm\)**SD** \\ \end{tabular} \\ \hline ERA-MADDPG & 76 \(\pm\) 2.1 & 136 \(\pm\) 4.3 & 4.0 \(\pm\) 0.8 \\ MADQN & 73 \(\pm\) 3.5 & 141 \(\pm\) 5.2 & 5.0 \(\pm\) 1.1 \\ SR-DRL & 67 \(\pm\) 4.8 & 163 \(\pm\) 6.7 & 7.0 \(\pm\) 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance comparison of the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm. It is worth noting that a 15% topology change represents an extreme stress test scenario, equivalent to sudden failures of multiple core nodes or intentional network reconfiguration under high-traffic loads. While such scenarios are rare in routine operations, they are critical for verifying the algorithm's resilience in emergency situations (e.g., natural disasters or cyber-attacks).