## 4 Experimental Evaluation
### Performance Evaluation  
To verify the performance of ERA-MADDPG, this paper uses a single-agent intelligent routing strategy based on deep reinforcement learning (Smart routing based on deep reinforcement learning, SR-DRL) [24] as a comparison object and a multi-agent learning algorithm based on a deep Q network (Multi-Agent Deep Q-Network, MADQN) [6] for convergence and performance comparison. The experiment compares the topological node changes and link changes after the algorithm converges; among them, the topology node changes are set to a dynamic change of about 5% of network nodes, a dynamic change of network nodes of about 10%, and a dynamic change of network nodes of about 15%. Full connectivity between and within domains is guaranteed when the topology changes. The link dynamic change is set to about 5%, the link dynamic change is set to 10%, and the link dynamic \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Parameters** & **Parameter Value** \\ \hline Training steps \(T\) of the algorithm & 100,000 \\ Actor-Critic learning rate & 0.0001 \\ Reward value discount factor & 0.9 \\ Experience replay pool size & 5000 \\ Traffic load intensity & 80\% \\ Reward value weighting parameters \(n_{1}\),\(n_{2}\), and \(n_{3}\) & 0\(-\)1 \\ Experience replay unit update iteration steps & 200 \\ Training batch size & 128 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation experiment parameter configuration. Figure 5: GEANT network topology. change is set to about 15%. When the link changes, full connectivity within and between domains is guaranteed. For each experimental condition, results are reported as the mean \(\pm\) 95% confidence interval (CI) calculated via Student's t-test, based on 30 independent runs. Confidence intervals are derived from the sample mean and standard deviation (SD), ensuring statistical significance at the \(\alpha=0.05\) level. Dynamic changes in network nodes simulate real-world scenarios such as device failures, new node deployments, or topology reconfiguration due to traffic demand shifts. These changes introduce uncertainty into the network environment, challenging routing algorithms to adapt quickly and maintain optimal performance. By evaluating the ERA-MADDPG algorithm under such dynamic conditions, we aim to quantify its resilience, defined as the ability to re-converge to near-optimal routing policies within minimal training cycles while minimizing performance degradation (e.g., delay, packet loss). This aligns with the core objective of resilient routing: ensuring continuous service quality despite unexpected topology disruptions. 1. Convergence comparison of the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm The training process of the ERA-MADDPG algorithm, MADQN algorithm, and SR-DRL algorithm is shown in Figure 6. The entire training process consists of 100 cycles, and each cycle is divided into 1000 steps. The training process is considered stabilized when the cumulative reward fluctuates within \(\pm\)5% of the moving average over 10 consecutive cycles, and the standard deviation of the reward across episodes is less than 0.1% of the maximum reward value. The training speed is measured by the number of cycles to convergence, defined as the point where the cumulative reward remains within \(\pm\)5% of the moving average over 10 consecutive cycles. The convergence speed is quantified by the number of cycles to reach a stable state, where cumulative reward fluctuates within \(\pm\)5% of the moving average for 10 consecutive cycles. It can be seen from Figure 6 that the first training of the ERA-MADDPG algorithm stabilizes around the 53 rd cycle. The MADQN algorithm first stabilized around the 67 th training cycle. The SR-DRL algorithm first stabilized around the 87 th training cycle. The convergence speed of the algorithms is compared as shown in Table 2. This translates to a 20.9% speed improvement over MADQN and a 39.1% improvement over SR-DRL in the initial training phase.