With the rapid development of the Artificial Intelligence of Things (AIoT), mobile edge computing (MEC) becomes an essential technology underpinning AIoT applications. However, multi-angle resource constraints, multi-user task competition, and the complexity of task offloading decisions in dynamic MEC environments present new technical challenges. Therefore, a user-centric deep reinforcement learning (DRL) model splitting inference scheme is proposed to address the problem. This scheme combines model splitting inference technology and designs a UCMS_MADDPG-based offloading algorithm to realize efficient model splitting inference responses in the dynamic MEC environment with multi-angle resource constraints. Specifically, we formulate a joint optimization problem that integrates resource allocation, server selection, and task offloading, aiming to minimize the weighted sum of task execution delay and energy consumption. We also introduce a user-server co-selection algorithm to address the selection issue between users and servers. Furthermore, we design an algorithm centered on user pre-decision to coordinate the outputs of continuous and discrete hybrid decisions, and introduce a priority sampling mechanism based on reward-error trade-off to optimize the experience replay mechanism of the network. Simulation results show that the proposed UCMS_MADDPG-based offloading algorithm demonstrates superior overall performance compared with other benchmark algorithms in dynamic environments.

翻译：随着人工智能物联网（AIoT）的快速发展，移动边缘计算（MEC）成为支撑AIoT应用的关键技术。然而，动态MEC环境中的多维度资源约束、多用户任务竞争以及任务卸载决策的复杂性带来了新的技术挑战。为此，本文提出了一种以用户为中心的深度强化学习（DRL）模型分割推理方案来解决该问题。该方案结合模型分割推理技术，并设计了一种基于UCMS_MADDPG的卸载算法，以在多维度资源约束的动态MEC环境中实现高效的模型分割推理响应。具体而言，我们将资源分配、服务器选择和任务卸载整合为一个联合优化问题，旨在最小化任务执行延迟与能耗的加权和。我们还引入了一种用户-服务器协同选择算法来解决用户与服务器之间的选择问题。此外，我们设计了一种以用户预决策为中心的算法来协调连续与离散混合决策的输出，并引入了一种基于奖励-误差权衡的优先级采样机制来优化网络的经验回放机制。仿真结果表明，在动态环境中，与其它基准算法相比，所提出的基于UCMS_MADDPG的卸载算法展现出更优的综合性能。