## 2 Related Works  
Many scholars have conducted extensive research on optimizing the task offloading scheduling scheme of MEC networks to improve network service quality. Traditional offloading optimization schemes usually establish mathematical programming models to solve optimization problems. For example, the authors established a 0-1 integer programming model for joint optimization of energy consumption and delay in [8], and designed a service request distribution method based on game theory in [9]. However, such schemes ignore the mobility of devices and information observation, making it difficult to adapt to dynamic and highly random large-scale network environments. The authors used a filling method based on linear programming to solve resource allocation problems and conduct joint optimization with dynamic pricing problems in [10]. The authors proposed an asynchronous computing framework, and the general benders decomposition method is used to decompose and iteratively solve the problem of user scheduling and resource allocation in [11]. These traditional optimization schemes typically have high operational efficiency and a solid theoretical foundation but often require accurate system state information, which is challenging in practical MEC systems. Moreover, traditional optimization schemes often struggle to cope with the high-dimensional decision space in optimization problems due to their high computational complexity. Artificial intelligence solutions utilizing neural networks have been proven to be effective for complex task offloading and resource allocation problems. RL techniques are often used to solve offloading decision problems. Due to the fundamental idea of RL being to enable agents to interact with their environment and learn the optimal strategy to achieve their goals, RL is suitable for solving offloading decision problems. In recent years, traditional RL methods have gradually been replaced by deep reinforcement learning (DRL), such as the DRL method used to solve offloading decisions, while also addressing the allocation of bandwidth, cache, and computing power in [12]. The authors combined DRL to solve the optimal offloading strategy and federated learning methods to address data privacy issues in [13]. Compared with the previous optimization methods, this type of scheme does not require the establishment of a mathematical model for solving and can autonomously explore the optimal offloading strategy without prior knowledge, making it more suitable for high-dimensional state spaces in large-scale networks. However, these studies typically require a significant amount of time and data to train neural networks, and all adopt centralized decision architectures, resulting in a high dependence on the central control node, leading to low robustness, poor scalability, and other issues. In addition, DRL has been applied to unmanned aerial vehicles (UAVs) for navigation, trajectory planning, and radio resource management [14; 15]. The performance differences between traditional Q-learning and deep Q network (DQN) algorithms in solving task offloading scheduling problems were compared in [16]. However, such schemes do not consider distributed architecture, which reduces robustness while increasing training difficulty. Therefore, the authors adopt the asynchronous advantage Actor-Critic algorithm to achieve a distributed architecture, which accelerates training while reducing the correlation between state transition samples in [17]. However, unlike parallel RL architectures such as A3C, all nodes in MEC networks are not in independent environments that do not affect each other. Therefore, distributed offloading decision-making is a multi-agent problem, and using traditional single-agent or parallel RL schemes may make it difficult for the algorithm to converge [18]. Furthermore, some scholars have also begun to use multi-agent reinforcement learning (MARL) technology to solve task-offloading decision problems.