Pervasive edge computing refers to one kind of edge computing that merely relies on edge devices with sensing, storage and communication abilities to realize peer-to-peer offloading without centralized management. Due to lack of unified coordination, users always pursue profits by maximizing their own utilities. However, on one hand, users may not make appropriate scheduling decisions based on their local observations. On the other hand, how to guarantee the fairness among different edge devices in the fully decentralized environment is rather challenging. To solve the above issues, we propose a decentrailized computation offloading algorithm with the purpose of minimizing average task completion time in the pervasive edge computing networks. We first derive a Nash equilibrium among devices by stochastic game theories based on the full observations of system states. After that, we design a traffic offloading algorithm based on partial observations by integrating general adversarial imitation learning. Multiple experts can provide demonstrations, so that devices can mimic the behaviors of corresponding experts by minimizing the gaps between the distributions of their observation-action pairs. At last, theoretical and performance results show that our solution has a significant advantage compared with other representative algorithms.

中文翻译：

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普适边缘计算的多智能体模仿学习：一种去中心化计算卸载算法


普适边缘计算是指仅依靠具有感知、存储和通信能力的边缘设备实现点对点卸载、无需集中管理的一种边缘计算。由于缺乏统一协调，用户总是通过自身效用最大化来追求利润。然而，一方面，用户可能无法根据他们的本地观察做出适当的调度决策。另一方面，如何在完全去中心化的环境下保证不同边缘设备之间的公平性是相当具有挑战性的。为了解决上述问题，我们提出了一种去中心化计算卸载算法，其目的是最小化普遍边缘计算网络中的平均任务完成时间。我们首先基于对系统状态的全面观察，通过随机博弈论推导出设备之间的纳什均衡。之后，我们通过集成一般对抗性模仿学习，设计了一种基于部分观察的流量卸载算法。多个专家可以提供演示，以便设备可以通过最小化观察-行动对的分布之间的差距来模仿相应专家的行为。最后，理论和性能结果表明，我们的解决方案与其他代表性算法相比具有显着的优势。