## 4 Experimental Evaluation
### Performance Evaluation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Change Magnitude** & **Algorithm** & \
\begin{tabular}{c} **Re-Convergence** \\ **Cycle (Training** \\ **Cycles)** \\ \end{tabular} &
\begin{tabular}{c} **Delay Cycles** \\ **Compared to** \\ **ERA-MADDPG** \\ \end{tabular} \\ \hline \multirow{3}{*}{5\% node change} & ERA-MADDPG & 10 \(\pm\) 0.8 & - \\  & MADQN & 22 \(\pm\) 1.5 & +12 \\  & SR-DRL & 38 \(\pm\) 2.3 & +28 \\  & ERA-MADDPG & 22 \(\pm\) 1.2 & - \\
10\% node change & MADQN & 33 \(\pm\) 2.1 & +11 \\  & SR-DRL & 59 \(\pm\) 3.7 & +37 \\  & ERA-MADDPG & 21 \(\pm\) 1.3 & - \\
15\% node change & MADQN & 35 \(\pm\) 2.4 & +14 \\  & SR-DRL & 60 \(\pm\) 4.2 & +39 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Re-convergence speed comparison after topological node changes. The ERA-MADDPG algorithm has better convergence speed than the MADQN algorithm and the SR-DRL algorithm when facing dynamic changes of 5%, 10%, and 15% in network nodes. The main reason is that the MADDPG algorithm can better cope with the cooperation situation in the multi-agent system by collaboratively training multiple agents. Different agents collaborate to optimize the performance of the actor-critic network. The actor-critic structure is more stable during the training process, which helps to solve the instability problem in the multi-agent system. When faced with dynamic changes, the advantages of multi-agents are significantly greater than those of single-agents. This is mainly because multi-agent algorithms can achieve distributed decision-making, collaboration, and competition with a certain degree of fault tolerance when faced with dynamic changes in network nodes, thereby better adapting to changes in complex network environments. Comparison of recovery training for link changes of 5%, 10%, and 15%  
Figure 7: Training comparison after dynamic changes of topological nodes. In the dynamic changes of the network, the link changes may be caused by network topology adjustments, equipment failures, or excessive link loads. When faced with link changes, it was once again verified that the ERA-MADDPG algorithm can adapt to new network environments and maintain efficient routing decision-making capabilities in the face of network changes. This paper sets the dynamic changes of links between network topology nodes to 5%, 10%, and 15%, respectively, and retrains the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm when the links between network topology nodes are dynamically changing by 5%, 10%, and 15%. Then, the results show that all three algorithms--ERA-MADDPG, MADQN, and SR-DRL--tend to stabilize again after the link changes. The training process is shown in Figure 8a-c. The comparison of convergence speed after link changes is shown in Table 4.  
Figure 8: Comparison of training on dynamic changes of network links. Figure 8 demonstrates that the ERA-MADDPG algorithm adapts faster to link changes than node changes: re-convergence cycles increase from 12 (5% link changes) to 22 (15% link changes), an 83% increase, while MADQN shows a 113% increase (15 \(\rightarrow\) 32 cycles). This difference may stem from the algorithm's ability to leverage link-state adjacency information in the CNN-based state mapping, which is more directly applicable to link change scenarios. Figure 8 highlights the algorithm's resilience to link dynamics, such as bandwidth fluctuations or partial outages. For 15% link changes, ERA-MADDPG adjusts link weights within 22 cycles by leveraging CNN-based state mapping to detect real-time link congestion. This contrasts with SR-DRL, a single-agent approach that struggles to balance global traffic distribution, resulting in longer re-convergence (54 cycles). The multi-agent framework's ability to jointly optimize inter-domain and intra-domain routes is critical here. The results in Figure 8a-c show that when the network links change dynamically by 5%, 10%, and 15%, the ERA-MADDPG algorithm reconverges around the 12 th, 13 th, and 22 nd training cycles, respectively.