Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing
Multi-Agent Reinforcement Learning Based Coded Computation for Mobile Ad Hoc Computing
复制标题
DOI：
10.1109/icc42927.2021.9500600
复制
发表时间：
2021-04
期刊：
影响因子：
--
通讯作者：
Baoqian Wang;Junfei Xie;K. Lu;Yan Wan;Shengli Fu
中科院分区：
文献类型：
--
作者：
Baoqian Wang;Junfei Xie;K. Lu;Yan Wan;Shengli Fu
移动自组织计算（MAHC）允许移动设备直接共享其计算资源，是一种很有前途的解决方案，可以满足移动设备对计算资源日益增长的需求。然而，由于节点移动性、不稳定和未知的通信环境以及这些设备的异构性，导致拓扑频繁变化和链路故障，将计算任务从移动设备卸载到其他移动设备是一项具有挑战性的任务。为了解决这些问题，本文引入了一种基于多智能体强化学习（MARL）的编码计算方案，该方案具有对网络变化的适应性、对不确定系统干扰的高效率和鲁棒性、考虑节点异构性和分散负载分配等优点。综合仿真研究表明，该方法优于当前最先进的分布式计算方案。
Mobile ad hoc computing (MAHC), which allows mobile devices to directly share their computing resources, is a promising solution to address the growing demands for computing resources required by mobile devices. However, offloading a computation task from a mobile device to other mobile devices is a challenging task due to frequent topology changes and link failures because of node mobility, unstable and unknown communication environments, and the heterogeneous nature of these devices. To address these challenges, in this paper, we introduce a novel coded computation scheme based on multi-agent reinforcement learning (MARL), which has many promising features such as adaptability to network changes, high efficiency and robustness to uncertain system disturbances, consideration of node heterogeneity, and decentralized load allocation. Comprehensive simulation studies demonstrate that the proposed approach can outperform state-of-the-art distributed computing schemes.