Abstract: Mobile Edge Computing (MEC) serves as a key technology to meet the low-latency and low-energy requirements of computation-intensive applications by offloading tasks from user devices to nearby edge servers. However, in heterogeneous multi-server environments, traditional heuristic methods and single-agent Deep Reinforcement Learning (DRL) algorithms suffer from disconnection between perception and decision-making, difficulty in learning high-dimensional action spaces, and inefficiency in constraint handling, resulting in slow convergence and poor adaptability. To address these issues, an Efficient Coupled Collaborative Computing Multi-Agent Deep Reinforcement Learning framework (ECCC-MADRL) is proposed to optimize task offloading and resource allocation in heterogeneous multi-server scenarios. The proposed framework adopts a dual-agent collaborative architecture composed of client and master agents. It integrates an efficient coupled feature extraction module to capture the multidimensional correlation between task and resource features and employs a Per-action DQN decision mechanism to decompose high-dimensional combinatorial actions, enabling dynamic cooperation among multiple users and servers. A “constraint internalization” dimensionality reduction strategy is designed to exclude subchannel identifiers from the state and action spaces, significantly reducing action dimensionality. Furthermore, a heterogeneous multi-server collaborative model is established based on feature matching and load balancing mechanisms to achieve dynamic cross-server resource scheduling. Experimental results show that ECCC-MADRL achieves a 30–37% improvement in reward performance and reduces task deadline violations by 25–55% compared with MAPPO and MADDPG-based baselines across multiple representative scenarios. In energy-constrained settings, it further decreases battery-level violations by around 40%, demonstrating clear advantages in convergence, efficiency, and robustness. The findings indicate that the ECCC-MADRL framework provides an efficient and robust solution for task offloading in heterogeneous edge environments and offers valuable insights for the design and optimization of intelligent edge computing systems.

摘要： 移动边缘计算（MEC）作为应对计算密集型应用低延迟与低能耗需求的关键技术，能够通过任务卸载有效缓解终端计算压力。然而，在异构多服务器环境中，传统启发式方法及单智能体深度强化学习（DRL）算法普遍存在感知与决策脱节、动作空间高维难以学习、约束处理效率低等问题，导致卸载策略收敛缓慢、适应性不足。为此，研究提出一种高效耦合协同计算多智能体深度强化学习框架（ECCC-MADRL），以实现异构多服务器场景下的高效任务卸载与资源分配优化。该框架构建“客户端-主控”双代理协同结构，通过高效耦合特征提取模块捕捉任务与资源的多维耦合特征，并基于Per-action DQN决策机制分解高维组合动作，实现多用户多服务器间的动态协同。框架设计引入“约束内化”降维方法，将子信道编号从状态与动作空间中剥离，显著降低动作维度；同时建立异构多服务器协同模型，以特征匹配度与负载均衡机制实现跨服务器资源的动态调度。实验结果表明，ECCC-MADRL相较于MAPPO与MADDPG系列基线算法在多个典型场景中实现了30–37%的奖励提升，并将任务超期率降低25–55%；在电量紧张场景下，其设备电量越线率亦减少约40%，充分体现了所提方法在收敛性、效率与鲁棒性方面的显著优势。研究表明，ECCC-MADRL框架能够在异构边缘环境中实现高效、鲁棒的任务卸载决策，为智能边缘计算系统的优化设计提供参考。