## 2 Related Works
### Task Scheduling and Resource Management
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Work** & **Problem** & **Method** & **Objective** \\ \hline [PERSON] et al. [18] & Task scheduling and resource allocation & Search-based heuristic method; Annealing fusion algorithm & Minimize time delay and energy consumption \\ \hline [PERSON] et al. [19] & Task scheduling & \begin{tabular}{p{113.8 pt}} Rule-based heuristic method; Set scheduling rules \\ \end{tabular} & Maximize average completion ratio \\ \hline [PERSON] et al. [20] & Workflow scheduling & \begin{tabular}{p{113.8 pt}} Search-based heuristic method; Greedy search algorithm \\ \end{tabular} & Minimize makespan \\ \hline [PERSON] et al. [21] & Job scheduling & \begin{tabular}{p{113.8 pt}} Search-based heuristic method; Ant colony algorithm \\ \end{tabular} & Minimize execution overhead and timeliness \\ \hline [PERSON] et al. [15] & Resource management & \begin{tabular}{p{113.8 pt}} Neural networks generate result; Policy gradient \\ \end{tabular} & Minimize average job slowdown \\ \hline [PERSON] et al. [22] & Resource management & \begin{tabular}{p{113.8 pt}} Neural networks generate result; Policy gradient \\ \end{tabular} & Maximize average resource utilization \\ \hline [PERSON] et al. [23] & Workflow scheduling & \begin{tabular}{p{113.8 pt}} Neural networks generate result; Actor-Critic; Pointer network (RNN-based) \\ \end{tabular} & Minimize makespan \\ \hline [PERSON] et al. [24] & Job scheduling & \begin{tabular}{p{113.8 pt}} Neural networks search result; DRL method; Actor-Critic; LSTM \\ \end{tabular} & Minimize average job slowdown \\ \hline [PERSON] et al. [25] & \begin{tabular}{p{113.8 pt}} Computation offloading and tasks scheduling \\ \end{tabular} & \
\begin{tabular}{p{113.8 pt}} Neural networks search; Actor-Critic; Pointer network (CNN-based) \\ \end{tabular} & Minimize timeout ratio and energy consumption \\ \hline [PERSON] et al. [26] & Satellite range scheduling problem &
\begin{tabular}{p{113.8 pt}} Neural networks generate result and rule-based heuristic method; Actor-Critic; Pointer network \\ (Transformer-based) \\ \end{tabular} & Maximize number of scheduled tasks and profit \\ \hline \end{tabular}
\end{table} 
Table 1: Related work on solving scheduling problems. jobs. They construct an initial feasible solution randomly and then apply the rewriter on the graph to perform a local search, aiming to minimize the makespan as much as possible. [PERSON] et al. [25] utilized the Pointer Network to address the problem of the edge platform being unable to simultaneously meet the real-time requirements of different tasks in the vehicular edge-collaborative environment. They proposed a multi-stage scheduling strategy to determine whether a task should be executed locally and also decide the task scheduling order within the edge server to meet the real-time requirements as much as possible. [PERSON] et al. [26] combined DRL with heuristic methods to solve the satellite range scheduling problem (SRSP). They used neural network to learn the characteristics of satellite missions for task allocation to suitable antennas and used a heuristic method inside the antennas to select appropriate time windows for satellite communication. It can be observed that deep reinforcement learning methods have been applied in various scheduling domains with different scenarios, which also provides a practical foundation for this work.