This paper explores the application of Deep Reinforcement Learning (DRL) to optimize adaptive observation strategies for multi-AUV systems in complex marine environments. Traditional algorithms struggle with the strong coupling between environmental information and observation modeling, making it challenging to derive optimal strategies. To address this, we designed a DRL framework based on the Dueling Double Deep Q-Network (D3QN), enabling AUVs to interact directly with the environment for more efficient 3D dynamic ocean observation. However, traditional D3QN faces slow convergence and weak action–decision correlation in partially observable, dynamic marine settings. To overcome these challenges, we integrate a Gated Recurrent Unit (GRU) into the D3QN, improving state-space prediction and accelerating reward convergence. This enhancement allows AUVs to optimize observations, leverage ocean currents, and navigate obstacles while minimizing energy consumption. Experimental results demonstrate that the proposed approach excels in safety, energy efficiency, and observation effectiveness. Additionally, experiments with three, five, and seven AUVs reveal that while increasing platform numbers enhances predictive accuracy, the benefits diminish with additional units.

Adaptive sampling of the marine environment may improve the accuracy of marine numerical prediction models. This study considered adaptive sampling path optimization for a three-dimensional (3D) marine observation platform, leading to a path-planning strategy based on evolutionary deep reinforcement learning. The low sampling efficiency of the reinforcement learning algorithm is improved by evolutionary learning. The combination of these two components as a new algorithm has become a current research trend. We first combined the evolutionary algorithm with different reinforcement learning algorithms to verify the effectiveness of the combination of algorithms with different strategies. Experimental results indicate that the fusion of the two algorithms based on a maximum-entropy strategy is more effective for adaptive sampling using a 3D marine observation platform. Data assimilation experiments indicate that adaptive sampling data from a 3D mobile observation platform based on evolutionary deep reinforcement learning improves the accuracy of marine environment numerical prediction systems.