## 1 Introduction
Under the requirements of underwater monitoring data accuracy, the number of transmitted correction data will be dynamically adjusted according to the change in the transmission environment. When the transmission environment is good, more correction data will be transmitted to ensure higher transmission accuracy. When the transmission environment is poor, the number of transmitted correction data can also be dynamically reduced to ensure a higher packet-transmission success rate. When selecting the best relay node, the data transmission strategy comprehensively considers the location of neighbor nodes, residual energy, and transmission channel delay. In order to avoid too many nodes participating in reinforcement learning and improve the convergence speed of reinforcement learning, a virtual pipeline is built between the source node and the target node based on the residual energy, and the relay node is only selected from the virtual pipeline. The environment and neighbor agent statuses are updated by the monitor and control channel established based on the broadcast between neighbor nodes. The agent based on reinforcement learning can independently find the best data transmission strategy according to the change in the environmental state, and realize efficient and reliable underwater wireless network communication. In the end-edge-cloud and agent modeling architecture, with the comprehensive design of all function layers of underwater network, this paper presents an adaptive data transmission algorithm (EP-ADTA) based on edge prediction and reinforcement learning. The main contributions include the following:  
(1) The end-edge-cloud architecture is used to define the fixed three-dimensional underwater wireless sensor network, and edge computing is applied to the data transmission of the underwater wireless sensor network. A new method of realizing the efficient data transmission of the underwater wireless sensor network by replacing communication with computing is proposed. (2) Taking underwater time series data monitoring as an example, a set of edge predictions based on the ARMA model is proposed to predict the new monitoring data at the edge network. Edge prediction cannot only automatically match the optimal prediction parameters, but also dynamically adjust the amount of correction data to be transmitted according to the accuracy changes in the application data. (3) Based on agent modeling technology, a data transmission algorithm based on reinforcement learning is proposed. The intelligent agent located in the edge network can sense the changes in the transmission channels' delay, the neighbor nodes' location and residual energy, automatically select the best transmission route, and adapt the data transmission accuracy to ensure efficient and reliable data transmission in underwater wireless sensor networks. (4) The simulation results show that the edge prediction algorithm can meet the accuracy requirements of underwater time series data monitoring, and the data transmission algorithm based on reinforcement learning can dynamically adapt to the changes in the transmission environment, which can greatly reduce the data transmission delay and energy consumption of underwater wireless sensor networks, and effectively improve the packet-sending success rate. The remainder of this paper is organized as follows: Section 2 introduces the related research work; Section 3 introduces the framework of the edge prediction-based adaptive data transmission algorithm (EP-ADTA); Section 4 introduces the edge prediction algorithm based on ARMA (EP-ARMA); Section 5 introduces the data transmission algorithm based on reinforcement learning (RL-ADTA); Section 6 introduces the simulation experiment and performance analysis; Section 7 is the conclusion.