Minimizing the Cost of UAV-Assisted Marine Mobile Edge Computing System Based on Deep Reinforcement Learning

Abstract

To enable compute-intensive and delay-sensitive maritime services, unmanned surface vessels (USVs) can offload tasks to mobile edge computing (MEC) servers mounted on unmanned aerial vehicles (UAVs). However, jointly minimizing energy consumption and latency is challenging due to the strong coupling between communication, computation, and mobility under stringent quality-of-service (QoS) requirements. To capture this trade-off, we formulate a weighted energy–delay minimization problem that jointly optimizes one-to-one UAV–USV scheduling, task partitioning, and UAV trajectory. The resulting problem is particularly difficult due to a hybrid discrete–continuous decision space and strong temporal coupling under stringent feasibility constraints. To address this mixed-integer nonconvex optimization problem, we reformulate it as a Markov decision process (MDP) and develop a constraint-aware OU–TD3 algorithm that integrates differentiable scheduling relaxation, feasibility-aware action mapping, and adaptive OU–Gaussian mixed exploration for stable learning in high-dimensional continuous control. We further extend the formulation and solution to a cooperative multi-UAV MEC setting with signal-to-interference-plus-noise ratio (SINR)-coupled interference and coordination constraints. Extensive simulations with statistical evaluation demonstrate stable convergence and up to 54.2% cost reduction over baseline schemes, while maintaining robustness under realistic maritime disturbances.