# Faulty Links' Fast Recovery Method Based on Deep Reinforcement Learning
## 3 Faulty-Path Recovery Scheme Based on DDPG-LBBP
### DDPG-LBBP Agent Interacts with the Environment
In contrast, the training times of DDPG-GRU and DDPG-LBBP were significantly shortened to 214.6 s and 187.2 s, respectively, because of the presence of the GRU. Moreover, DDPG-LBBP's training time was further reduced thanks to its optimized reward regulation m \begin{table}
\begin{tabular}{l c c c c} \hline \hline  & \multirow{2}{*}{**Training Time (s)**} & **Average Recovery Delay (ms)** & **Average Packet Loss Rate (\%)** & **Average Recovery Success Rate (\%)** \\ \hline DDPG-RNN & 356.8 \(\pm\) 23.5 & 10.5 \(\pm\) 1.2 & 6.5 \(\pm\) 0.8 & 85.2 \(\pm\) 3.5 \\ \hline DDPG-GRU & 214.6 \(\pm\) 15.8 & 8.3 \(\pm\) 0.9 & 4.2 \(\pm\) 0.6 & 91.5 \(\pm\) 2.8 \\ \hline DDPG-LBBP & 187.2 \(\pm\) 12.4 & 6.8 \(\pm\) 0.7 & 3.5 \(\pm\) 0.5 & 94.5 \(\pm\) 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The results of the ablation experiments. of variance showed that there were significant differences in training time among the three (F (2, 5997) = 1023.6, \(p<0.001\)). Regarding the average recovery delay, DDPG-RNN performed the worst at 10.5 ms. The value of DDPG-GRU decreased to 8.3 ms, and DDPG-LBBP performed best at only 6.8 ms. The ANOVA indicated significant differences (F (2, 5997) = 876.5, \(p<0.001\)), suggesting that the synergy between GRU and the reward regulation mechanism could accelerate the recovery of faulty links. In terms of the average packet loss rate, DDPG-RNN had a rate of 6.5%, DDPG-GRU had a rate of 4.2%, and DDPG-LBBP had the lowest rate of 3.5%. The significant differences (F (2, 5997) = 765.4, \(p<0.001\)) reflected the role of the reward regulation mechanism in balancing network load and reducing the packet loss rate. The average recovery success rate of DDPG-RNN was 85.2%, DDPG-GRU's rate increased to 91.5%, and DDPG-LBBP's rate reached the highest at 94.5%. The significant differences (F (2, 5997) = 689.3, \(p<0.001\)) indicated that the synergy of all elements could improve the recovery success rate. In summary, through the adjustment and comparative analysis of the key elements of the DDPG-LBBP algorithm in the ablation experiments, the significant impacts of the GRU, reward regulation, and path design on the algorithm's performance were accurately analyzed. The GRU, with its gating mechanism, effectively alleviated the problems of gradient vanishing or explosion. It not only significantly shortened the training time but also enhanced the algorithm's ability to capture long-term dependencies when processing sequential data. As a result, it reduced the recovery delay and packet loss rate and increased the recovery success rate. The reward regulation mechanism comprehensively considered and optimized factors such as propagation delay, link utilization, and packet loss rate in the network topology. While balancing the network load, it optimized the training time, reduced the recovery delay, and lowered the packet loss rate, enhancing the algorithm's performance in all aspects. The maximum disjoint backup path design collaborated with the former two. By avoiding the overlapping of nodes and links in the primary and backup paths, it fundamentally solved the problems of backup path congestion and secondary failures caused by path overlap. This further improved the reliability and stability of the algorithm in faulty-link recovery, ensured the fast and accurate transmission of vector data, and greatly enhanced the overall practicality of the algorithm. ## 5 Conclusions

Aiming at the high faulty-link recovery delay and link congestion problems existing in current network link failure fast recovery algorithms in a WAMS communication network application, this paper introduced SDN into the WAMS communication network and proposed a fault-link fast recovery method (DDPG-LBBP) based on deep reinforcement learning, which solved the problems of high faulty-link recovery delay and link congestion.