Task Offloading in Marine Edge Clouds: A Lyapunov-Guided Multi-Agent DRL Approach

Abstract

Maritime mobile edge computing (MMEC) has emerged as a key enabler for supporting computation-intensive vision applications on uncrewed surface vehicles (USVs). However, volatile maritime channels, heterogeneous computing resources, and mismatched task offloading strategies jointly lead to excessive energy consumption and unstable task queues. In this paper, we investigate a joint optimization problem that minimizes the long-term system cost, defined by weighted delay and energy consumption, while guaranteeing inference accuracy and queue length for USV-based image detection services. The problem is formulated as a mixed-integer nonlinear program (MINLP), which is intractable for real-time decision making. To address this challenge, we develop a Lyapunov-guided multi-agent deep reinforcement learning (MADRL) framework. Specifically, a accuracy estimation Model is first trained to characterize the accuracy–resolution relationship of heterogeneous object detection models, enabling task-driven image preprocessing. The original MINLP is then decomposed via convex optimization and Lyapunov drift-plus-penalty techniques to reduce the decision-space complexity. On this basis, a Lyapunov Guided Multi-Agent Deep Deterministic Policy Gradient (LG-MADDPG) algorithm is designed to jointly optimize transmission delay and offloading strategy under dynamic maritime environments. Large-scale simulations based on real Automatic Identification System (AIS) trajectories demonstrate that, compared with state-of-the-art baseline methods, the proposed approach reduces the average system cost and task queue length by 13.5% and 22.2%, respectively.