# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 2 Related Work  
One of the main goals of MEC networks is to offload computing tasks to network edge servers for processing, reduce the computing load on terminal devices, and thus improve application performance and system efficiency. Edge computing devices have certain computing, storage, and communication capabilities, which can provide effective computing support for mobile devices. Therefore, task offloading has become a core component of MEC. Task offloading can alleviate the computing pressure on terminal devices, reduce network load, optimize bandwidth utilization, and provide users with low-latency computing services [9]. The goals of task offloading decisions usually have three aspects: reducing latency, reducing energy consumption, and weighing the pros and cons between latency and energy consumption [10]. In terms of reducing latency, [PERSON] [11] solved the problem of the low processing capacity of edge servers by offloading some tasks of certain mobile terminal users to the central cloud for computing. This effectively reduced the processing delay of tasks, thus avoiding network congestion. [PERSON] [12] attached great importance to the peak aggregation of different edge servers, extensively explored the trade-off between communication delay and computation delay in task offloading, and proposed a hierarchical task offloading and resource optimization mechanism based on tree hierarchy. Reasonably scheduling tasks based on the processing capacity of each server significantly improved network resource utilization. [PERSON] [13] studied the task offloading scheme for power terminals with limited computer resources in the smart grid environment. This scheme used an enhanced adaptive genetic algorithm to solve the optimal task offloading strategy. [PERSON] [14] proposed the concept of end-to-end collaboration based on cloud-edge collaboration. Although the experimental results showed that the total delay of task execution could be reduced, the computation of the task scheduling (CTS) algorithm in this article was relatively complex. Reference [15] used Markov decision theory to solve the problem of minimizing delay. Reference [16] established a WPMECN network model based on deep reinforcement learning, which has significant advantages in CPU processing latency. In terms of reducing energy consumption, Reference [17] proposed a home energy management system based on artificial neural networks, which significantly reduces household electricity costs and carbon dioxide emissions by integrating solar photovoltaic and maximum power point tracking (MPPT) technologies. This research showed how to optimize household energy use through intelligent algorithms and provides a useful reference for energy management in mobile edge computing. In the MEC environment, similar intelligent energy management strategies can be used to optimize the energy consumption of edge devices. Reference [18] proposed a new adaptive particle swarm optimization algorithm, which has better energy-saving effects. Reference [19] focused on the issues of power control and computing resource allocation and proposed the E2 PC (Energy-saving Power Control) algorithm. Although this algorithm has significant advantages in reducing energy consumption, it is difficult to apply in practice. In terms of balancing delay and energy consumption, Reference [20] proposed a joint optimization strategy to achieve the dual objectives of energy consumption and delay optimization, which is of great significance for promoting energy efficiency improvement in MEC applications. Reference [21] proposed an optimization framework for computing offloading and resource allocation to improve network computing efficiency and save energy. Reference [22] investigated the offloading decision problem of MEC networks in multi-layer 6G network environments. The service needs of users, network status, and computing resources were fully considered to optimize network performance and user experience.