## 2 Related Work
In [23], the authors investigate joint aerial-base-station (AeBS) deployment and computation offloading in a 6 G aerial edge computing network. They model the problem as minimizing the task-processing delay and energy consumption via both deployment control and offloading decisions, and they design a federated deep reinforcement learning (FedDRL) scheme allowing for fast offline training and quick online adaptation to environmental changes. [PERSON] et al. [24] propose a decentralized deep reinforcement learning mechanism for vehicle-to-vehicle (V2V) communications, supporting both unicast and broadcast modes, with carefully designed state, action, and reward functions, achieving scalable, low-overhead resource allocation. In [25], [PERSON] et al. study the service offloading decision for the Internet of Vehicles (IoV), formulating multi-task offloading with data dependencies as a long-term planning problem, and introduce a knowledge-driven deep reinforcement learning framework, supporting offline pre-training at edge servers and continual online federated learning to directly learn optimal offloading policies that adapt to environmental changes. [PERSON] et al. [26] address the NP-hard two-stage scheduling problem in end-edge-cloud Internet of Everything (IoE) systems by combining [PERSON]'s rule-based presorting of two-stage tasks with a deep reinforcement learning-enhanced scheduler (DRL-TSS), achieving near-optimal makespan minimization across heterogeneous edge executors. In [27], the authors investigate the content distribution in layered fog radio access networks (FRANs), formulating a minimal delay resource allocation model with in-network caching and cloud-edge cooperation, and propose a cross-layer deep reinforcement learning policy to jointly optimize caching and routing decisions for low-latency content delivery. [PERSON] et al. [28] study adaptive resource allocation in dynamic MEC environments, leveraging an SDN-enabled architecture and a DRL-based resource allocation (DRLRA) scheme to jointly allocate network routes and edge computing resources, thereby minimizing the service time and balancing the load under varying request patterns. The authors of [29] examine the beam allocation in maritime communication systems by discretizing the coverage area into a grid and modeling the beam assignment under unknown CSI as a sequential decision process, and they introduce a virtual queue-based DQN (VQDRL) scheme that adaptively allocates beams to maximize the maritime user data rates. [PERSON] et al. [30] study cooperative computation offloading and resource allocation in blockchain-enabled MEC systems, introducing an asynchronous advantage actor-critic (A3C) deep reinforcement learning algorithm that maximizes both the MEC computation rate and the blockchain transaction throughput by jointly optimizing the offloading decisions, power allocation, block size, and block interval to dynamically learn the optimal policy under channel and queue dynamics. In [31], the authors study joint communication and computation resource management in a space-air-ground-sea integrated maritime network and develop a DQN-based solution to jointly optimize task offloading and resource assignment under dynamic maritime conditions, markedly improving latency and resource utilization compared to heuristic baselines. Resource management and computation offloading optimization problems usually involve non-convex optimization with multi-objective coupling, such as power control, user association and arithmetic allocation. To address this problem, several classes of traditional algorithms are applied by researchers. For instance, the decomposition optimization algorithm is used to split the large-scale non-convex programming problem into easily solvable subproblems. In [32], a two-stage edge server placement algorithm is proposed to optimize safety, efficiency, and adaptability in generative AI-enabled Internet of Vehicles systems, which achieves better load balancing, lower energy use, and reduced latency.