## 1 Introduction
By replacing centralized cloud computing with edge computing dispersed in the edge network, this approach can better meet the needs of delay-sensitive applications, reduce data transmission load, and improve the overall performance of the network [22]. The underwater wireless sensor network is also applicable to the end-edge-cloud architecture. As a result of the improvement in the computing power of underwater nodes, edge computing will play a greater role in improving the performance of underwater wireless sensor networks [23; 24]. Edge computing has been applied to underwater mobile wireless sensor networks composed of AUV and sensor nodes to reduce the data transmission burden between sensor nodes and the AUV, and between the AUV and shore-based data centers [24; 25]. In the fixed network, the sensor nodes used for data collection can be defined as the information end; the wireless communication network is composed of underwater sensor nodes; communication nodes and sink nodes form the edge network; the shore-based data center network comprises the cloud environment; and the sink node is used as the gateway node connecting the underwater edge network and the cloud environment. During the process of underwater monitoring, the data are collected by the underwater sensor nodes, forwarded to the surface sink nodes via each hop through the underwater communication nodes, and then transmitted to the shore-based data center via satellite. The continuous data transmission demand for underwater monitoring applications has led to a more serious burden on the previously limited underwater wireless communication. However, as time series data and monitoring data generally show a great correlation and redundancy in time and space [26; 27], the new monitoring data can be predicted in the edge network using an edge prediction algorithm and historical data. After edge prediction, only the prediction parameters and correction data that meet the application accuracy requirements should be transmitted, which can effectively compress the actual data transmission volume, thereby reducing the transmission delay and energy consumption, improving the packet-sending success rate, and enhancing the communication performance of underwater wireless sensor networks. Agent modeling is a method used to divide and conquer complex systems. Intelligent agents can respond and process in a collaborative manner based on the intelligent policies by understanding the environment and other agent statuses [28; 29]. The communication of the underwater wireless sensor network is complex and difficult. Each node in the edge network can be defined as an intelligent agent [30]. Through cooperation and communication, intelligent agents can effectively improve the performance of underwater wireless network communication [31; 32]. Mobile agents have been used to enhance the network scalability [12]. Multiple agents have been used to optimize routing selection [28; 29] and to improve the rationality of resource allocation [30; 31]. Reinforcement learning is an important method for improving agent intelligence. It allows agents to learn the optimal strategy through trial-and-error training. As a result of the changes in the transmission channel, transmission node performance, and application transmission requirements, the agent must dynamically adjust the routing strategy and accuracy of data transmission according to the environmental state to ensure the performance of underwater communication. Over a certain period, reinforcement learning can equip agents with this ability [28; 29]. Therefore, the data transmission strategy is based on reinforcement learning, which incorporates the combination of the environmental status and business requirements into the state space, and selects the best data transmission accuracy while selecting the best relay node, to obtain a data transmission strategy.