A2M-KS: Adaptive Attention-Based MADRL Strategy With Koch Snowflake Cluster Structure for Cross Sea-Air Optical Communication Alignment

Optical communication alignment between an unmanned aerial vehicle (UAV) and an autonomous underwater vehicle (AUV) is crucial for achieving cross sea-air optical communication. However, the high directivity of laser beams and sea surface fluctuations pose significant challenges to precise alignment. To address this issue, this paper first establishes a dynamic sea surface model and a cross sea-air optical communication channel model, utilizing the beam splitting method to determine the light spot center position on the UAV receiving plane. To achieve rapid and stable alignment, we propose an adaptive attention-based multi-agent deep reinforcement learning (MADRL) strategy with Koch Snowflake cluster structure for cross sea-air optical communication alignment (A2M-KS). This algorithm dynamically optimizes the evaluation network through an adaptive learning rate and employs an attention mechanism to enable agents to selectively focus on relevant information, thereby achieving rapid convergence and higher accuracy. Furthermore, by designing an attractive reward function, the UAV can rapidly and accurately track the light spot center, completing the alignment task with the AUV. Experimental results demonstrate that the A2M-KS algorithm outperforms baseline methods in terms of communication reliability, convergence speed, and accuracy.