# A Novel Deep Reinforcement Learning Approach for Task Offloading in MEC Systems
## 2 Related Work  
Since MEC has made great contributions to mobile communication, several previous efforts have paid attention to the task-offloading problem of MECs. In the related work section, we classify two different categories of research directions, one is for traditional schemes and the other is for those based on DRL. First of all, we introduce the relevant traditional methods. For instance, in [6], the authors focused on multi-data-source tasks and constructed a non-cooperative game-theoretic offloading framework for each edge node to find the optimal decision. In [7], the authors conducted an MEC system with a core cloud, where one task could be divided into several subtasks and could predict the total offloading duration using linear regression. In [8], the authors studied the MEC system with edge clouds and remote clouds to minimize the total energy consumption of multiple MDs, and then proposed an approximation algorithm based on dynamic programming to solve the mentioned problem. In [10], the authors studied a bi-level optimization approach for minimizing the total energy consumption, which is jointly investigated the task-offloading strategies and resource allocation. In [11], the authors proposed a novel computation offloading architecture and developed a heuristic algorithm to solve a mixed-integer linear problem considering the latency and operating cost. In [12], the authors proposed a blockchain-based MEC system framework and discussed two different separate approaches for optimizing user association, data rate allocation, and resource allocation. In [13], the authors designed a new MEC mechanism for the mining task-offloading issue and proposed an efficient task-offloading algorithm based on the Stackelberg game in order to maximize the utilities of mobile devices. In [14], the authors formulated an optimization model as a multiuser decision-making problem and transformed the problem into an unconstrained Lagrangian optimization (ULO) to maximize the expected offloading rate of multiple users. In [15], the authors studied an MEC system considering the proper load on task processing efficiency with the aim of minimizing the processing delay. They presented a two-stage computing offloading scheme by using an aggregative game with a load billing mechanism. In [16], the authors focused on the statistical quality of service (QoS) guarantee for task offloading to save more energy. They formulated the problem as a mixed-integer nonlinear programming problem and used convex optimization theory to ensure the QoS during the offloading process. In [17], the authors jointly optimized offloading decisions and resource allocation with the aim of maximizing the system utility in a NOMA-aided MEC. They used Lyapunov technique to decouple the optimization problem and presented an online algorithm by introducing a series of auxiliary variables. In [18], the authors researched the task scheduling problem in a cloud computing system. They proposed an adaptive load-balanced task approach (ALTS) for maximizing resource utilization. However, these above algorithms demand prior knowledge of the interaction environment and statistics of tasks and channel conditions, which may not always meet the expected requirements in actual use. With the rise of artificial intelligence, reinforcement learning (RL)-based algorithms are able to learn computation offloading policies with no prior knowledge of MEC systems. In [19], the authors modeled a cloud computing environment where they studied the balance between local and global research which played an important role in swarm-intelligence-based meta-heuristics algorithms. They contributed an adaptive particle swarm optimization (PSO) to reduce the task execution time. In [20], the authors presented a space-air-ground-integratednetwork considering the computation offloading problem as an MDP, and then proposed a DRL approach for a UAV user to gain a lower total cost.