## 4 Experimental Evaluation
### Performance Evaluation
The ERA-MADDPG algorithm outperforms MADQN and SR-DRL for two reasons. First, it builds on DDPG to optimize continuous action spaces via policy gradients, which is well-suited for network traffic scheduling. Second, agents store experiences in a shared replay pool, which accelerates collaborative learning. Multi-agent collaborative training makes convergence faster. The advantages of the MADDPG algorithm and the MADQN algorithm over the single-agent algorithm mainly lie in their ability to achieve collaborative learning, experience sharing, and coordinated cooperation among multiple agents, making them more suitable for network traffic scheduling scenarios. Figure 6: Algorithm convergence comparison plot. Comparison of recovery training with topology node changes of 5%, 10%, and 15%  
In order to verify the ability of the ERA-MADDPG algorithm to adapt to the new network environment and maintain efficient routing decision-making in the face of network changes, in this paper, the data layer network topology nodes are dynamically changed by 5%, 10%, and 15%, respectively, and then the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm are trained again. When the topological nodes change dynamically by 5%, 10%, and 15%, the training processes of the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm tend to be stable again, as shown in Figure 7a-c. When topology changes occur (e.g., 5% node failures), the algorithm initializes the retraining process with the optimal policy from the previous topology, rather than a random policy. This warm-start mechanism reduces the search space for new optimal routes, as shown in Figure 7. The comparison of convergence speed after topological node changes is shown in Table 3.  
As shown in Figure 7, reward values are dimensionless and normalized in Equation (19), which integrates throughput, delay, and packet loss metrics. Higher values indicate better routing performance. The ERA-MADDPG algorithm exhibits consistent convergence acceleration across varying node change magnitudes: when node changes increase from 5% to 15%, the re-convergence cycles rise from 10 to 22, a 120% increase. In contrast, the MADQN algorithm shows a more significant degradation (22 \(\rightarrow\) 35 cycles, 59% increase), and SR-DRL experiences a 58% increase (38 \(\rightarrow\) 60 cycles). This indicates that ERA-MADDPG maintains better scalability under severe topology perturbations. The results in Figure 7a-c demonstrate that under dynamic topology changes of 5%, 10%, and 15% node variations, the ERA-MADDPG algorithm reconverges at approximately the 10 th, 22 nd, and 21 st training cycles, respectively; the MADQN algorithm achieves reconvergence at around the 22 nd, 33 rd, and 35 th cycles; the SR-DRL algorithm stabilizes at about the 38 th, 59 th, and 60 th cycles for corresponding change levels. The ERA-MADDPG algorithm's faster re-convergence under node changes (e.g., 10 cycles for 5% node loss) directly reflects its multi-agent collaborative mechanism. When nodes fail, agents dynamically reroute traffic by exchanging topology change information through the SDN controller layer. This distributed decision-making avoids centralized bottlenecks, unlike MADQN, which relies on a single critic network and exhibits delayed adaptation (22 cycles for 5% node changes). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **First Stabilization** & \
\begin{tabular}{c} **Corresponding** \\ **Training Steps** \\ **(\(\times\)10\({}^{3}\), 95\% CI)** \\ \end{tabular} &
\begin{tabular}{c} **Delay Cycles** \\ **Compared to** \\ **ERA-MADDPG** \\ \end{tabular} \\ \hline ERA-MADDPG & 53 \(\pm\) 2.1 & 53,000 \(\pm\) 1800 & - \\ MADQN & 67 \(\pm\) 3.5 & 67,000 \(\pm\) 2300 & +14 \\ SR-DRL & 87 \(\pm\) 4.8 & 87,000 \(\pm\) 3100 & +34 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Convergence speed comparison of algorithms.