Recently, there has been an explosion of mobile applications that perform computationally intensive tasks such as video streaming, data mining, virtual reality, augmented reality, image processing, video processing, face recognition, and online gaming. However, user devices (UDs), such as tablets and smartphones, have a limited ability to perform the computation needs of the tasks. Mobile edge computing (MEC) has emerged as a promising technology to meet the increasing computing demands of UDs. Task offloading in MEC is a strategy that meets the demands of UDs by distributing tasks between UDs and MEC servers. Deep reinforcement learning (DRL) is gaining attention in task-offloading problems because it can adapt to dynamic changes and minimize online computational complexity. However, the various types of continuous and discrete resource constraints on UDs and MEC servers pose challenges to the design of an efficient DRL-based task-offloading strategy. Existing DRL-based task-offloading algorithms focus on the constraints of the UDs, assuming the availability of enough storage resources on the server. Moreover, existing multiagent DRL (MADRL)--based task-offloading algorithms are homogeneous agents and consider homogeneous constraints as a penalty in their reward function. We proposed a novel combinatorial client-master MADRL (CCM\_MADRL) algorithm for task offloading in MEC (CCM\_MADRL\_MEC) that enables UDs to decide their resource requirements and the server to make a combinatorial decision based on the requirements of the UDs. CCM\_MADRL\_MEC is the first MADRL in task offloading to consider server storage capacity in addition to the constraints in the UDs. By taking advantage of the combinatorial action selection, CCM\_MADRL\_MEC has shown superior convergence over existing MADDPG and heuristic algorithms.

翻译：近年来，执行视频流、数据挖掘、虚拟现实、增强现实、图像处理、视频处理、人脸识别和在线游戏等计算密集型任务的移动应用呈现爆炸式增长。然而，平板电脑和智能手机等用户设备（UDs）执行任务计算需求的能力有限。移动边缘计算（MEC）已成为满足用户设备日益增长计算需求的一项有前景的技术。MEC中的任务卸载是一种通过在用户设备和MEC服务器之间分配任务来满足用户设备需求的策略。深度强化学习（DRL）因其能适应动态变化并最小化在线计算复杂度，在任务卸载问题中正受到关注。然而，用户设备和MEC服务器上各类连续与离散资源约束，对设计基于DRL的高效任务卸载策略提出了挑战。现有基于DRL的任务卸载算法主要关注用户设备的约束，并假设服务器拥有足够的存储资源。此外，现有基于多智能体DRL（MADRL）的任务卸载算法采用同质智能体，并将同质约束作为惩罚项纳入其奖励函数。我们提出了一种新颖的组合式客户端-主节点MADRL（CCM_MADRL）算法用于MEC任务卸载（CCM_MADRL_MEC），该算法使用户设备能够决定其资源需求，并让服务器基于用户设备需求做出组合决策。CCM_MADRL_MEC是首个在任务卸载中除考虑用户设备约束外，还同时考虑服务器存储容量的MADRL算法。通过利用组合式动作选择的优势，CCM_MADRL_MEC相比现有MADDPG及启发式算法展现出更优的收敛性能。