###### Abstract
The architecture of MEC usually consists of the user layer and the mobile edge layer [24; 25; 26; 27], as shown in Figure 2. In the MEC paradigm, the user layer consists of mobile device terminals, which contain various applications and functions and also have certain computing capabilities. When processing each computing task, the device terminal can choose to process it on its own device in addition to offloading the task to the mobile edge layer or cloud layer through data transfer. The mobile edge layer consists of edge servers near the device terminals, where computing resources are more abundant than  
Figure 1: The framework of the conventional mechanical fault diagnosis system in which the terminal uploads the monitoring data to a central server through the network cable for processing. those of the device terminals. Through computing offload technology, information can be interacted with in real-time to meet the computing needs of different types of application scenarios. The MEC architecture has a wide range of application scenarios in the IoT, such as 5G communication, virtual reality, Internet of Vehicles, smart city, smart factory, etc. The MEC architecture has the advantages of low time delay, green and energy efficiency, security, location, content awareness, etc., which makes it easier to access AI methods and blockchain methods. Computing offloading as one of the core techniques of the MEC has received great attention recently. For simple, indivisible, or highly integrated tasks, binary offloading strategies are generally adopted, and tasks can only be computed locally or all offloaded to the servers [28]. The authors in [29] formulated the binary computation offloading decision problem as a convex problem, which minimizes the transmission energy consumption under the time delay constraint. The computation offloading model studied in [30] assumed that the application has to complete the computing task with a given probability within a specified time interval, for which the optimization goal is the sum of local and offloading energy consumption. This work concluded that offloading computing tasks to the MEC servers can be more efficient in some cases. In practice, offloading decisions can be more flexible. The computation tasks can be divided into two parts performed in parallel: one part is processed locally, and the other is offloaded to the MEC servers for processing [31]. A task-call graph model is proposed to illustrate the dependency between the terminal and MEC servers, in which decisions and latencies are investigated by the joint offloading scheduling and formulated as a linear programming problem [32]. RL has been employed as a new solution to the problem of MEC offloading, which is a model-free machine learning algorithm that can perform self-iterative training based on the data it generates [33; 34; 35; 36]. Task processing delay is a vital optimization parameter for time-sensitive systems. The authors studied the problem of computation offloading in an IoT network in [37], in which the Q-learning-based RL approach was proposed for an IoT device to select a proper device and determine the proportion of the computation task to offload. The authors in [38] investigated joint communication, caching, and computing for vehicular mobility networks. A deep Q-learning-based RL with a multi-timescale framework was developed to solve the joint online optimization problem. In [39], the  
Figure 2: Architecture, applications, and advantages of MEC. authors studied the offloading for the energy harvesting (EH) MEC network. An after-state RL algorithm was proposed to address the large time complexity problem, and polynomial value function approximation was introduced to accelerate the learning process. In [40], the authors also studied the MEC network with the EH device. The authors proposed hybrid-based actor-critic learning for optimizing the offloading ratio, local computation capacity, and server selection.