## 6 Conclusions  
Considering the limited energy and resources of the nodes in the system, this section proposes a collaborative cloud-edge joint task offloading and resource allocation optimization problem, utilizing UAVs and LEO satellites to provide computational offloading services for maritime IoT devices, with the aim of minimizing the total system cost. Since this optimization problem is a mixed-integer nonlinear programming problem, this section introduces an MADDPG algorithm based on multi-agent reinforcement learning, which  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Refs** & **Algorithm** & **Optimization Problem** & **Objective** & **Performance Metrics** \\ \hline \multirow{3}{*}{[16]} & Lyapunov-based & & Minimize long-term & \\  & stochastic optimization & Online & expected & Average delay, \\  & and delayed online & task-routing/offloading & task-completion & satellite queue length \\  & learning & & delay & \\ \hline \multirow{3}{*}{[17]} & Gradient-free online & Fog-CPU allocation for & Maximize & Bandwidth saving \\  & scheduling & marine-sensor data & comm.-resource & ratio, data-quality \\  & compression & efficiency under QoS & (PSNR) satisfaction \\ \hline \multirow{3}{*}{[18]} & Hopcroft–Karp matching & Voyage-based offloading & Jointly minimize & \\  & and real-time heuristics & via shipborne edge nodes & delay, energy and & Task delay, UE \\  & & via shipborne edge nodes & traffic cost & energy, backhaul cost \\ \hline \multirow{3}{*}{[19]} & Improved Hungarian & Multivessel offloading & Min-weighted sum & Total terminal energy, \\  & algorithm & decision and server & (Energy + Delay) & execution latency \\ \hline \multirow{3}{*}{[20]} & Multi-agent & Dynamic power control in & Maximize weighted & Network sum-rate, \\  & Deep-Q-Learning & dense wireless links & sum-rate utility & spectral-efficiency \\ \hline \multirow{3}{*}{[21]} & REINFORCE/DQL/DDPG & Downlink power & Maximize & \\  & (actor–critic) & allocation for multi-cell & instantaneous and & \\  & (actor–critic) & IBC & average sum-rate & to CSI error \\ \hline \multirow{3}{*}{[22]} & Double Deep-Q-Learning & Power allocation in 6G & Maximize sum-rate & \\  & with outage guard & interference channel & under link-outage & \\  & & constraints & probability \\ \hline \multirow{3}{*}{[23]} & Federated multi-agent & Joint AeBS deployment & Minimize task delay & Average delay, node \\  & DRL & and offloading & and energy for aerial & energy, convergence \\  & & & MEC & speed \\ \hline \multirow{3}{*}{This work} & MADDPG (continuous & Joint offloading ratio, & \\  & MADDPG (continuous & UAV–terminal association, & \\  & actor–critic, centralized & Tx-power, CPU-frequency & \\  & training/decentralized & allocation in & \\  & execution) & Space-Air-Sea MEC & \\  & & networks & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of the contributions.  
can better find the optimal strategy. Simulation experiments verify the convergence of the proposed algorithm. Compared with other baseline algorithms, the MADDPG algorithm proposed in this section achieves a lower overall system cost under the same conditions. Meanwhile, the limitations are considered. The current model assumes ideal channel state information and static energy constraints. We will consider the effects of channel estimation errors and battery degradation in future work.  
This work was partially supported by the National Natural Science Foundation of China (62271303); The Innovation Program of Shanghai Municipal Education Commission of China (2021-01-07-00-10-E00121); The Natural Science Foundation of Shanghai (20 ZR1423200).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The author declares no conflict of interest.