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.

近年来，移动应用程序的爆发性增长引起了人们的关注，但用户设备在处理计算需求方面存在局限性。移动边缘计算作为应对用户设备不断增长的计算需求的一项有前途的技术，通过在用户设备和移动边缘计算服务器之间分配任务来满足用户设备的需求。深度强化学习在任务卸载问题中受到关注，因为它能够适应动态变化并减少在线计算复杂性。然而，用户设备和移动边缘计算服务器上的各种连续和离散资源约束给有效基于深度强化学习的任务卸载策略的设计带来了挑战。我们提出了一种新颖的组合客户端 - 主控深度强化学习算法（CCM_MADRL_MEC），用于移动边缘计算中的任务卸载，使得用户设备能够决定其资源需求，并使服务器基于用户设备的需求做出组合决策。CCM_MADRL_MEC 是任务卸载中首个考虑服务器存储容量以及用户设备约束的多智能体深度强化学习算法。通过利用组合动作选择，CCM_MADRL_MEC 相对于现有的多智能体深度确定性策略梯度算法和启发式算法具有更好的收敛性。