# 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
The DDPG-LBBP method achieved two objectives: (1) By improving the DDPG algorithm, it realized the load balancing of the links in the communication network of the wide-area measurement system and output the optimal link weight for the design of the backup path. (2) By using the maximum disjoint path, it avoided the overlapping of nodes and links in the primary path and the backup path, thus avoiding the secondary network link failures. This paper compared the DDPG-LBBP fault recovery method with (1+2\(\epsilon\))-BPCA, FFRLI, and LIR, respectively. The results showed that the DDPG-LBBP fault recovery method had the advantages of a low faulty-link recovery delay, low packet loss rate, and high recovery success rate. Compared with the (1+2\(\epsilon\))-BPCA algorithm, the recovery delay was reduced by about 12.26%, and the faulty-link recovery success rate was increased by about 6.91%. Compared with the FFRLI method, the packet loss rate in the network topology after faulty-link recovery was reduced by about 15.31%. This paper mainly considered the performance of the algorithm in the IEEE 30 and IEEE 57 benchmark power system communication networks, but the improved DDPG algorithm in various IEEE benchmark power system communication networks took a long time to train. Therefore, it is of great importance to study how the proposed fault recovery algorithm can quickly adapt to different scales of IEEE benchmark power system communication networks. In subsequent research work, we will further study a fast fault recovery algorithm that can be easily migrated in different networks. Conceptualization, W.H., W.G. and Y.L. ; methodology, W.H., W.G. and Y.L. ; software, W.H., W.G. and Y.L. ; validation, W.H., W.G. and Y.L. ; formal analysis, Q.L., J.Z. and X.H. ; investigation, [PERSON], [PERSON] and Y.L. ; resources, Q.L., [PERSON] and X.H. ; data curation, Q.L., J.Z. and X.H. ; writing--original draft preparation, [PERSON], W.G. and Y.L. ; writing--review and editing, [PERSON], W.G., Y.L., Q.L., J.Z. and X.H. ; supervision, W.H. and Y.L. All authors have read and agreed to the published version of the manuscript. This research was funded by the Major Science and Technology Programs in Henan Province (No. 241100210100), the Henan Provincial Science and Technology Research Project (No. 242102211068, No. 232102210078), the Special Project for Research and Development in Key areas of Guangdong Province (No. 2021 ZDZX1098), the China Higher Education Institution Industry-University-Research Innovation Fund (No. 2021 FNB3001, No. 2022 IT020), and the Stabilization Support Program of Science, Technology and Innovation Commission of Shenzhen Municipality (No. 20231128083944001). The data presented in this study are available on request from the corresponding author due to privacy. Author [PERSON] was employed by the company Henan Xinda Wangyu Technology Co., Ltd. Author [PERSON] was employed by the company Zhengzhou Xinda Institute of Advanced Technology. Author [PERSON] was employed by the company Henan Jiuyu Tenglong Information Engineering Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships. ## Appendix A