# Offloading of Atomic Tasks in Satellite Networks:
## 2 Related Work  
With the rapid development of edge computing technology, task offloading has become a widely studied problem. Research on task offloading decisions can be divided into three categories. Task prediction decision methods based on deep learning, task decision methods based on reinforcement learning and task decision methods based on deep reinforcement learning. However, the task decision based on the deep learning method can predict the task state change in period and update the decision of task unloading plan in time. However, these method requires a large amount of computing power and more training data and the computation is time-consuming. The method obviously cannot satisfy the requirements for some scenarios with real-time requirements. Researchers have proposed a reinforcement learning method for scenarios with high requirements on timeliness. The reinforcement learning method can solve the problem of long computation time, but it requires a long training time and a relatively long excitation function. It is challenging to initiate the values of the parameters and local optima can quickly appear. In addition, reinforcement learning methods do not learn new data well as the scene changes rapidly. To address these problems, researchers have proposed a deep reinforcement learning model by combining the advantages of the above methods. The deep reinforcement learning model solves the problems of excessive time consumption and low accuracy to a certain extent, but the algorithm still has some drawbacks in time consumption for those sensitive to timeliness and real-time scenes. To explore the effective offloading decision and resource allocation methods of mobile edge computing, [PERSON] et al. [10] developed a collaborative computing system and designed a new computing offloading strategy based on Q-learning to achieve the optimal resource allocation and offloading scheme. [PERSON] et al. [11] Use Lyapunov optimization technology to transform time slots and propose an online algorithm in each time slot to determine the task offloading strategy. [PERSON] et al. [12] proposed an online energy-saving task allocation and computing offloading strategy, taking into account service delay constraints, etc., adaptively determining task allocation and computing resource allocation. [PERSON] et al. [13] Proposed an efficient Lyapunov online algorithm to perform joint task offloading and dynamic data caching strategies for computing tasks, reducing-edge computing latency, and energy consumption. [PERSON] et al. [14] proposed deep reinforcement learning to solve the offloading problem of cluster multi-service nodes and multi-dependency of mobile tasks in a large-scale heterogeneous mobile edge computing environment. [PERSON] et al. [15] proposed a device-level and edge-level task offloading joint optimization scheme based on deep reinforcement learning, which achieved a good balance between task delay and task energy consumption. [PERSON] et al. [16] taking the choice of the target server and the amount of data unloading as learning goals, a deep reinforcement learning strategy based on multi-agents to solve the problem of multi-agent environment instability. Due to the strong coupling between combined offloading decision and task execution, the efficiency is very low when the numerical scale is large. [PERSON] et al. [17] proposed a deep reinforcement learning framework based on the deep reinforcement learning structure. [PERSON] et al. [18] proposed a random mixed integer nonlinear programming problem to optimize task offloading decisions, flexible computing resource scheduling, and wireless resource allocation. From the perspective of offloading decisions to maximize revenue. [PERSON] et al. [19] proposed a mobile carrier-based IoT edge cloud computing offloading scheme. Use the sensing device to generate tasks and transfer the tasks to the device, and then the device can decide to calculate the task locally on the MEC server or in the cloud center.