# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 1 Introduction  
With the rapid development of mobile Internet and intelligent devices, mobile communication technology has become a kind of infrastructure closely related to people's daily life, which is widely used in social networking, online shopping, entertainment, and many other fields. At the same time, the data and computation required by mobile terminal devices are also showing an exponential growth trend, which has brought tremendous pressure to mobile networks. The limited computing and communication capabilities make it impossible for mobile terminal devices to meet the demands of massive computing tasks. Mobile Cloud Computing (MCC) [1], as an emerging computing model utilizing the high-performance computing and storage capabilities of the cloud to provide computer resources and storage services for mobile terminal devices, has become an effective solution to the problem. However, in recent years, applications such as augmented reality and autonomous driving that require a large amount of computation and bandwidth have emerged. The traditional mobile cloud computing model has significant transmission delays during task processing, which cannot meet the latency-sensitive requirements ofreal-time tasks. The main reason is that the distance between mobile terminal devices and central cloud and edge node devices is relatively long [2]. In order to solve the above problems, the new concept of mobile edge computing (MEC) was proposed [3,4]. This computing method reduces the distance between users and remote ends, reduces task processing delay, and improves the efficiency of task processing by distributing the original centralized computing resources and storage resources on edge node devices close to mobile terminal users. Through this approach, the problem of significant transmission latency in traditional cloud computing models is solved, which has a significant impact on improving user experience. MEC technology allocates computing resources reasonably based on dynamic changes in the network, ensuring service quality while meeting the different latency and energy consumption requirements of different user tasks [5]. Despite the rapid development of MEC technology in multiple fields, MEC still faces many challenges as network size and task complexity continue to increase. When faced with complex computing tasks or excessive workload, it is still necessary to offload the tasks to the central cloud for processing. Therefore, it is necessary to coordinate the processing of computing tasks among the central cloud, edge node devices, and mobile terminal devices [6]. The cloud-side collaboration mechanism can give full play to the powerful computing power of cloud computing and the low-latency advantage of edge computing, but it also needs to consider the limited resources and heterogeneous services of mobile edge servers [7]. Due to the limited computing and storage resources of edge node devices, only some services can be cached, and the caching strategy of the services can affect users' decisions on computing offloading. In addition, uploading pending tasks to the cloud for processing carries the risk of private data leakage during channel transmission [8]. Therefore, optimizing the task offloading strategy under the cloud edge collaboration mechanism while ensuring user data privacy, as well as achieving the comprehensive optimization of latency and energy consumption as much as possible, is a challenging and significant problem. The concept of task offloading is very important in mobile edge computing networks. It reduces the latency of processing tasks, improves response time, and enhances user experience, and it plays an important role in achieving load balancing, reducing device energy consumption, and improving resource utilization. The main contributions of this article include the following:  
1.