## 2 Related Work
### Task Offloading Algorithm  
In recent years, many studies have focused on the task offloading problem in mobile edge computing. In order to efficiently utilize limited resources, [PERSON] et al. [15] investigated the problems of data user association, task assignment, and data center-oriented virtual machine placement. The algorithm complexity is reduced, and the application response time is shortened by converting these nonlinear problems into a mixed-integer linear programming problem to solve, improving the user experience across a large number of tasks. [PERSON] et al. [16] studied the problem of spectrum access, computational offloading, and radio resource allocation in a multiuser environment with multiple edge access points and proposed a genetic algorithm-based task offloading strategy. It divides the problem into two steps, offloading decisions and resource allocation, and then solves these two steps separately using an optimized genetic algorithm. [PERSON] et al. [17] proposed an efficient resource allocation and computation offloading model for a multiuser, multi-drone supported mobile edge cloud computing system that is scalable to support the increase in network traffic without degrading performance, considering computation offloading and resource allocation issues. In addition, the network deploys multistage mobile edge computing (MEC) technology to provide computing capabilities at the edge of the radio access network (RAN). The model problems presented in the article are transformed into various types of optimization problems, which are then solved using traditional optimization algorithms such as integer programming, nonlinear programming, convex optimization, and heuristic algorithms to obtain theoretical or approximate optimal solutions. The main problem is that the MEC environment needs to be modeled, and the algorithms are too complicated to be used directly in the real world, especially in situations where real-time performance is important. Reinforcement learning is also widely used in task offloading problems because of its self-learning and adaptive features, as well as its better global search capability and advantages in solving large-scale complex problems. [PERSON] et al. [18] studied task scheduling strategies in MEC scenarios where the mobile device movement is unknown and proposed using a reinforcement learning technique called Deep Q-Network (DQN) to learn the optimal unloading strategy for mobile devices. The proposed DQN-based offloading algorithm does not require the mobile device's movement pattern or the environment model, which eliminates the discretization error shown in previous work and allows the proposed algorithm to summarize previous experience, which is especially useful when dealing with a large number of states. [PERSON] et al. [19] considered a graph offloading model with DAG dependencies for subtasks and used long short-term memory networks, post hoc experience playback, and candidate networks to improve the DQN algorithm separately in order to optimize the task offloading strategy. Given the inability of existing cloud computing paradigms to handle real-time and latency-sensitive ASA (automatic speech analysis) tasks, [PERSON] et al. [20] exploited mobile edge computing and deep learning (DL) to investigate the DL-enabled ASA task offloading problem in mobile edge cloud computing networks to minimize the total time to process ASA tasks, thus providing an agile service response, which enables edge servers to derive user tolerance limits through linear regression models, further improving the quality of user experience. The above study achieved adaptation to the environment through deep reinforcement learning (DRL), which is able to summarize the optimal strategy through learning experience without knowing the state of the environment.