## 2 Related Work
[16] study the long-term task completion delay minimization problem in satellite-enabled space-air-ground integrated MEC networks for 6 G. They formulate a stochastic optimization problem and apply Lyapunov optimization to decompose it into per-satellite deterministic subproblems, then they adapt a delayed online learning technique to predict the dynamic task arrivals and queue lengths for use in the cooperative offloading policy, achieving a reduced average delay while maintaining computation efficiency. [PERSON] [17] study fog computation scheduling for marine data feedback under severely limited maritime communication resources, proposing an online gradient-free scheduling algorithm that compresses data at the fog nodes before transmission, and the proposed scheme requires neither explicit cost nor constraint functions, using first-order and second-order estimation to allocate computation resources and thereby improving the communication efficiency while guaranteeing QoS for diverse applications. Meanwhile, the matching algorithm is applied to the computation offloading problem. In [18], [PERSON] et al. study voyage-based computation offloading in secure maritime edge networks, where both user devices and edge nodes (onships) move along fixed routes. They propose a two-timescale mechanism. On a large timescale, a Hopcroft-Karp-based algorithm precomputes the optimal offloading matches from voyage information, then on a small timescale, real-time task requests use these precalculated matches or local execution based on the resource demand. Simulations with real ship trajectories show marked improvements in the task delay, energy consumption, and traffic cost. [PERSON] et al. [19] study computation task offloading and server selection for vessel terminals in maritime mobile edge computing networks, aiming to minimize both energy consumption and execution delay. They model offloading as a two-step decision and propose a multivessel computation offloading algorithm based on an improved Hungarian matching algorithm, demonstrating significant delay and energy savings via simulation. Although traditional methods are widely used in optimization problems for different communication application scenarios, they often rely on structured assumptions or static models of the problem, showing limitations when facing multi-intelligent agent interactions, partially observable and high-dimension continuous action spaces. Recently, the application of deep reinforcement learning (DRL) to resource management optimization problems has attracted the attention of researchers. Compared with traditional algorithms, DRL enables direct access to optimal policies in high-dimensional, non-convex, dynamic, and partially observable environments through end-to-end learning without explicit modeling or step-by-step solving. For instance, [PERSON] and [PERSON] [20] study the dynamic power allocation problem in wireless networks, where each transmitter must choose its transmission power in real time to maximize a weighted sum-rate utility under delayed and incomplete CSI. They introduce a distributed deep Q-learning scheme where each agent collects local CSI and QoS information from neighbors and learns a power control policy robust to feedback delays and channel variations. In [21], [PERSON] et al. address the downlink sum-rate maximization problem in a multi-user cellular network, formulating it as a near-static optimization under maximum power constraints. They propose a multi-agent framework combining three different DRL algorithms to learn distributed power control policies that outperform model-based benchmarks in both the sum-rate and robustness. [PERSON] et al. [22] tackle the non-convex power allocation problem in 6 G interference channels with per-link outage constraints. They first reformulate the problem as a geometric programming instance, then develop a dual deep Q-network approach to stabilize learning, which achieves near-optimal sum-rate performance with much lower computational cost than classical methods.