## 2 Related Work  
In the multi-access edge computing (MEC) domain, computational offloading emerges as a multifaceted challenge, demanding innovative solutions [12; 13; 14], which are crucial for enhancing network efficiency. Research in this domain has predominantly concentrated on three approaches: mathematical optimization algorithms, control algorithms, and artificial intelligence (AI)-driven optimization algorithms. Among these, AI and machine learning (ML)-based algorithms have shown particular promise in dynamic and complex MEC environments due to their adaptability and learning capabilities [15]. The studies to date have focused on optimizing latency and energy consumption during computational offloading. [PERSON] et al. utilized meta-heuristic algorithms, such as ant colony, whale, and grey wolf optimization, to establish an offloading strategy aimed at minimizing energy and delay, albeit within the confines of a singular MEC server context [16]. While meta-heuristic algorithms are robust, their complexity and numerous parameters often complicate the tuning process. In the realm of control algorithms, [PERSON] et al. proposed drone-assisted dynamic resource allocation leveraging Lyapunov optimization to ensure delay efficiency and reliability of offloading services [17]. [PERSON] et al. contributed a balanced framework using SDN-based controller load balancing with Lyapunov optimization for improved offloading efficiency and reduced latency [18]. The precision of mathematical optimization for linear problems contrasts with its constrained adaptability to dynamic changes, whereas control algorithms excel in system stability and robustness but can be complex and less suitable for non-linear systems. Deep reinforcement learning (DRL), marrying deep learning with reinforcement learning, has emerged as a potent approach in MEC for managing dynamic tasks, with techniques like deep Q-network (DQN) and advantage actor-critic (A2C) algorithms gaining traction [19; 20; 21]. With the endeavor to overcome the limitations of mathematical optimization algorithms and control class algorithms, many researchers have used DRL methods for their self-learning and self-adaptation qualities in MEC resource allocation. In [19], the authors demonstrated the superiorities and applications of ML and deep learning (DL) methods in MEC environments for two key tasks: automatic identification and offloading of \"untrusted tasks\" and task scheduling on MEC servers, in particular using a flow-shop-like scheduling approach. [PERSON] et al. developed an enhanced scheme based on the deep deterministic policy gradient (DDPG), which exhibited notable performance in reducing energy consumption, managing load status, and minimizing latency. However, this scheme did not adequately consider the involved behavioral characteristics of the devices [20]. To minimize the computing delay and energy consumption of UE, [PERSON] et al. used DQN and DDPG to process large-scale state spaces and obtained the task offloading ratio and power allocation suitable for each UE. [PERSON] et al. utilized A2C, empowered UEs to make intelligent offloading decisions, and demonstrated efficacy in simulated OpenAI Gym scenarios but did not fully capture the dynamism of mobile devices [21]. Although DRL demonstrates its superiority in resource management, the challenge of high user concurrency persists, often impacting QoS and QoE due to resource contention, and the challenge of high user concurrency persists, often impacting QoS and QoE due to resource contention [22]. D2D communication's potential in offloading via high-speed, direct connections between devices has thus garnered academic focus. [PERSON] et al. addressed the energy consumption optimization problem for UE-assisted MEC computational offloading in mobile environments and proposed a DRL-based computational offloading model for D2D-MEC. Finally, they demonstrated that this scheme consumes the least amount of energy and cost in continuous time [23]. [PERSON] et al.