## 4 Experimental Evaluation
### Performance Evaluation
The MADQN algorithm converges around the 15 th, 23 rd, and 32 nd cycles under the same link changes, while the SR-DRL algorithm does so around the 35 th, 42 nd, and 54 th cycles. The MADDPG algorithm collaboratively trains multiple agents to make it converge faster. At the same time, CNN takes advantage of high-dimensional data processing and can effectively accelerate the convergence efficiency of the algorithm. The convolutional layers in CNN can share weight parameters, and the neural network can reuse the same weights to process different areas of the input data, reducing the number of network parameters and model complexity. The faster re-convergence in dynamic topology scenarios (Figures 7 and 8) stems from two key factors: (1) experience transfer--reusing valid routing decisions from the previous topology via the replay buffer. (2) multi-agent coordination--agents collaboratively update policies by exchanging topology change information, which reduces redundant exploration compared to the single-agent SR-DRL. Comparing Figures 7 and 8, the ERA-MADDPG algorithm achieves faster re-convergence in link change scenarios (12-22 cycles) than in node change scenarios (10-22 cycles). This is likely due to the fact that link changes preserve node connectivity, allowing the algorithm to reuse more prior routing policies compared to node failures, which require more extensive path reconfiguration. Across both Figures 7 and 8, ERA-MADDPG demonstrates a 25-50% faster re-convergence rate compared to baselines as the topology change severity increases (5-15%). This scalability is rooted in its three-layer SDN architecture, which separates policy generation (multi-agent layer) from topology management (controller layer), enabling rapid adaptation without full retraining. In contrast, single-agent and centralized algorithms face exponential complexity growth with increasing changes, underscoring the necessity of multi-agent collaboration for resilient routing. Comparison of the performance of the ERA-MADDPG algorithm in terms of delay, throughput, and packet loss rate  
After the convergence of the algorithm is verified, this paper tests the algorithm's performance. Testing includes statistical analysis of latency, throughput, and packet loss rate after algorithm convergence following 5%, 10%, and 15% changes in topology nodes and links. The average values are then calculated. The performance comparison is shown in Table 5. \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
5\% link change & \begin{tabular}{c} ERA-MADDPG \\ MADQN \\ SR-DRL \\ \end{tabular} & \
\begin{tabular}{c} 12 \(\pm\) 0.9 \\ 15 \(\pm\) 1.3 \\ 35 \(\pm\) 2.7 \\ 13 \(\pm\) 1.1 \\ 23 \(\pm\) 1.8 \\ 42 \(\pm\) 3.2 \\ 22 \(\pm\) 1.6 \\ 32 \(\pm\) 2.5 \\ 54 \(\pm\) 4.1 \\ \end{tabular} &
\begin{tabular}{c} - \\ +3 \\ +23 \\ +10 \\ +32 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Re-convergence speed comparison after link changes. As shown in Table 5, the ERA-MADDPG algorithm achieves 76 Mbps throughput after retraining under node changes, which is comparable to its initial training performance (75 Mbps under link changes), demonstrating that experience transfer maintains high efficiency without full random initialization. Simulation results, averaged over 30 independent runs with 95% confidence intervals, demonstrate that the ERA-MADDPG algorithm consistently outperforms baseline methods in convergence speed and routing stability under dynamic topology changes. The ERA-MADDPG algorithm outperforms the SR-DRL algorithm and the MADQN algorithm in performance while ensuring a better adaptation to dynamic changes in network topology and accelerating the retraining rate. The main reason is that the ERA-MADDPG algorithm can use other agents' observations and strategies to assist each other in training.