# Ship Network Traffic Engineering Based on Reinforcement Learning
## 4 Discussion and Conclusions  
In this study, we address the multiple challenges faced by ship networks, including limited bandwidth, unstable network connections, high latency, and command priority. To solve these problems, we chose the reinforcement learning method and conducted a simulation study of traffic engineering on the ship network. We focused on three aspects, namely, traffic balance, instruction priority, and complex network structure, to evaluate the reinforcement learning performance in these scenarios. The proposed algorithm for ship network traffic engineering aims to optimize network performance by balancing traffic load, prioritizing critical instructions, and adapting to dynamic network conditions. The algorithm seeks to mitigate congestion by optimizing traffic distribution and load balancing across network segments. As a result, the occurrence and severity of congestion-related issues, such as packet drops, increased latency, and degraded throughput, are reduced, leading to smoother and more efficient network operation. The algorithm's ability to prioritize high-importance instructions and optimize routing decisions contributes to minimizing latency in the network. The experimental results show that applying reinforcement learning to ship networks greatly improves the network's quality of service (QoS). Specifically, reinforcement learning optimized network traffic balance, reasonably arranged instruction priorities, and coped with complex network structures. Through an in-depth analysis of the experimental data, we noticed network consumption was reduced by 9.1% under reinforcement learning, which implemented priority routing of high-priority instructions more effectively while reducing the occupancy rate of the edge with the highest occupancy rate in the network by 18.53%. Although the simulation experiment results demonstrated the positive effects of reinforcement learning, it is important to consider whether the experimental environment fully considers the complexity of the real ship network. Future work should consider adding validation with real maritime communication data to fully evaluate the applicability of reinforcement learning in real-world scenarios. Parameter selection in reinforcement learning algorithms can significantly impact the results. To ensure the robustness and reliability of the research results, future research can explore the selection of algorithm parameters and generalized performance under different ship network configurations. It is important to acknowledge that RL is not a novel concept. However, its application to the specific context of ship network optimization represents a novel contribution. While the experimental setup may provide insights into the potential benefits of RL, it is crucial to recognize the limitations of the proposed structure and dataset. Real-world ship networks may exhibit complexities and dynamics not fully captured in the experimental setup. Therefore, while the study provides valuable insights into the feasibility of using RL for ship network optimization, further validation in real-world maritime environments is necessary to confirm its effectiveness and legitimacy. Additionally, future research should explore more diverse and realistic scenarios to ensure the generalizability of the findings. Figure 7: Occupancy rate: Max Occupancy Rate, OSPF, greedy algorithm, RL. **Author Contributions:** Conceptualization, X.Y. and M.L. ([PERSON]); methodology, X.W. ([PERSON]); software, M.L. ([PERSON]); validation, B.H. ; funding acquisition, X.W. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the National Key Research and Development Program under Grant 2022 YFB3104600; the China Postdoctoral Science Foundation Funded Project (2023M740519); Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China (No. ZYGX2021 YGLH213, No.