Wanyue Li, Shuyang Li, Jie Hao, Qiang Wu, Ran Wang
As the maritime industry evolves, applications such as real-time navigation, ocean monitoring, and emergency rescue increasingly require reliable communication. They also demand efficient computation offloading to support AI-driven services. However, terrestrial networks offer sparse coverage and unstable links in open-sea environments, severely constraining both connectivity and the execution of computation-intensive tasks. Although low Earth orbit (LEO) satellites extend coverage over oceans, their frequent handovers and high operating costs hinder stable, low-latency communication and efficient computation offloading. Unmanned aerial vehicle-based relays can enhance connectivity, but their limited endurance and environmental vulnerability hinder large-scale deployment. In contrast, high-altitude platform stations (HAPS) offer quasi-stationary positioning, broad coverage, and long operational duration, making them promising intermediaries between LEO satellites and maritime users. Building on this motivation, we design a space–air–ground–sea integrated network architecture in which HAPS function as relay nodes. We model the multi-layer task offloading and resource allocation as a partially observable Markov decision process to capture the uncertainty and dynamics of maritime environments. To solve it, we adopt multi-agent proximal policy optimization, which enables centralized training with decentralized execution. Furthermore, we incorporate an exact potential game mechanism into the reward design to enhance agent coordination and ensure alignment with system-wide objectives. Simulation results show that our method outperforms three representative baselines; under maximum task load, it reduces average latency, energy consumption, and overall cost by 8.7%, 2.0%, and 18.6%, respectively, verifying its effectiveness for computation-intensive maritime services.