# Ship Network Traffic Engineering Based on Reinforcement Learning
## 1 Introduction  
As shown in Figure 1, the ship's network structure is divided into physical, access, and core layers. This design provides reliable communications [1], data transfer, and ship management. This layered structure helps optimize network performance, scalability, and management. In the ship industry, this network structure supports various applications, including  
Figure 1: Network structure on the ship. ship management systems, navigation systems, communication systems, etc. At the same time, it can also adapt to different connection needs, such as communication between the internal equipment of the ship, communication between the ship and shore-based systems, etc. Traffic engineering [2] is a method of network management and optimization that effectively controls and guides data traffic to improve network performance [3], availability, and efficiency. As shown in Figure 2[4], if all tasks choose the shortest path, it will cause congestion in the shortest path and idle other paths. Optimal routes [5] are selected to reduce delay and improve network availability according to the network's topology and actual needs. Traffic engineering is particularly important in scenarios involving large networks, cloud computing environments, and Internet service providers, whose complex data traffic requires meticulous management and optimization to ensure efficient operation. Traffic engineering refers to a series of technologies and methods that improve network performance, increase bandwidth utilization, and ensure service quality by optimizing data transmission and distribution flows. In ship networks, traffic management involves ship communications and data transmission. Ship networks often face limited bandwidth, unstable network connections, high latency, etc. To solve these challenges, traffic engineering aims to effectively manage the ship network's data flow and ensure various ship applications' (such as navigation, communication, monitoring, etc.) performance and reliability under limited resources. In ship networks, traffic engineering ensures effective utilization of bandwidth and meets the needs of different applications by dynamically allocating and adjusting bandwidth [6]. It also involves distributing data traffic across different network paths or communication channels to prevent one path from being overloaded, thereby improving overall network performance. Traffic engineering ensures that critical applications (such as navigation systems) obtain sufficient bandwidth and low latency, guaranteeing their normal operation. In response to network failures or interruptions, traffic engineering uses fault-tolerant mechanisms and backup paths to maintain data transmission continuity and reliability. It also involves real-time ship network monitoring and dynamically adjusting traffic engineering strategies based on traffic conditions and network status. Through effective traffic engineering, ship networks can better adapt to different environments and mission requirements, improving communication reliability and performance. These measures are critical for the proper functioning of various applications and systems on board ships. Figure 2: Schematic diagram of routing optimization. Reinforcement learning (RL) [7] is a branch of machine learning that teaches agents to learn from environmental interaction and maximize accumulation (schematic diagram of reinforcement learning is shown in Figure 3). Reward signal: Reinforcement learning models involve decision-making in unknown environments and learning optimal strategies through trial and error. The core concepts of reinforcement learning include the following: agent, environment, state, action, reward, and policy. The reinforcement learning process usually involves a balance between exploration and exploitation [8]. The agent must use known strategies to maximize rewards while trying new strategies to gain more information. [PERSON] et al. [9] used Q-learning to optimize the ship loading plan and verified its effectiveness in several actual application cases.