Large-Scale Computation Offloading Using a Multi-Agent Reinforcement Learning in Heterogeneous Multi-Access Edge Computing

Gao, Zhen; Yang, Lei; Yu, Dongmei

doi:10.1109/tmc.2022.3141080

Cited by 47 publications

(7 citation statements)

References 31 publications

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“…These studies are usually model-based traditional optimization algorithms, which require a large amount of a priori knowledge to construct an accurate mathematical model, a situation that leads to poor generalization ability of the model and makes it difficult to adapt to the new states that appear in dynamic environments [18] . To cope with the UAV group task migration problem in this dynamic task environment, some researches have proposed task migration methods based on multi-agent reinforcement learning, which uses deep neural networks to end-toend the interaction relationship of multi-agent within the UAV group, and adaptively learn to update the strategy by interacting with the environment to improve the generalization ability of the model.…”

Section: Related Workmentioning

confidence: 99%

Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning

Jiang,

Di,

Qian

et al. 2025

Tsinghua Sci. Technol.

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Recently, with the increasing complexity of multiplex Unmanned Aerial Vehicles (multi-UAVs) collaboration in dynamic task environments, multi-UAVs systems have shown new characteristics of intercoupling among multiplex groups and intra-correlation within groups. However, previous studies often overlooked the structural impact of dynamic risks on agents among multiplex UAV groups, which is a critical issue for modern multi-UAVs communication to address. To address this problem, we integrate the influence of dynamic risks on agents among multiplex UAV group structures into a multi-UAVs task migration problem and formulate it as a partially observable Markov game. We then propose a Hybrid Attention Multi-agent Reinforcement Learning (HAMRL) algorithm, which uses attention structures to learn the dynamic characteristics of the task environment, and it integrates hybrid attention mechanisms to establish efficient intra-and inter-group communication aggregation for information extraction and group collaboration.Experimental results show that in this comprehensive and challenging model, our algorithm significantly outperforms state-of-the-art algorithms in terms of convergence speed and algorithm performance due to the rational design of communication mechanisms.

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Section: Related Workmentioning

confidence: 99%

Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning

Jiang,

Di,

Qian

et al. 2025

Tsinghua Sci. Technol.

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“…A number of computations are redirected to edge devices that are more suited to handle them, such as those with GPUs, or to devices with bigger energy reserves, or even directly to the cloud. These edge systems are equipped to monitor energy usage and can intelligently distribute tasks to suitable edge devices using offloading algorithms, often integrating machine learning methods for optimized decision-making [102,104].…”

Section: Energymentioning

confidence: 99%

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Jouini,

Sethom,

Namoun

et al. 2024

Technologies

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Internet of Things (IoT) devices often operate with limited resources while interacting with users and their environment, generating a wealth of data. Machine learning models interpret such sensor data, enabling accurate predictions and informed decisions. However, the sheer volume of data from billions of devices can overwhelm networks, making traditional cloud data processing inefficient for IoT applications. This paper presents a comprehensive survey of recent advances in models, architectures, hardware, and design requirements for deploying machine learning on low-resource devices at the edge and in cloud networks. Prominent IoT devices tailored to integrate edge intelligence include Raspberry Pi, NVIDIA’s Jetson, Arduino Nano 33 BLE Sense, STM32 Microcontrollers, SparkFun Edge, Google Coral Dev Board, and Beaglebone AI. These devices are boosted with custom AI frameworks, such as TensorFlow Lite, OpenEI, Core ML, Caffe2, and MXNet, to empower ML and DL tasks (e.g., object detection and gesture recognition). Both traditional machine learning (e.g., random forest, logistic regression) and deep learning methods (e.g., ResNet-50, YOLOv4, LSTM) are deployed on devices, distributed edge, and distributed cloud computing. Moreover, we analyzed 1000 recent publications on “ML in IoT” from IEEE Xplore using support vector machine, random forest, and decision tree classifiers to identify emerging topics and application domains. Hot topics included big data, cloud, edge, multimedia, security, privacy, QoS, and activity recognition, while critical domains included industry, healthcare, agriculture, transportation, smart homes and cities, and assisted living. The major challenges hindering the implementation of edge machine learning include encrypting sensitive user data for security and privacy on edge devices, efficiently managing resources of edge nodes through distributed learning architectures, and balancing the energy limitations of edge devices and the energy demands of machine learning.

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“…Based on above-mentioned discussions, we propose the AB-MAPPO algorithm, which is summarized in Algorithm 1. The complexity of attention module is O(I 2 V ), where V is the length of feature-value vectors, according to [35]. For an MLP, the computational complexity…”

Section: Complexity Analysismentioning

confidence: 99%

Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

Liu

Xie

et al. 2023

IEEE Trans. Wireless Commun.

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With the high flexibility of supporting resourceintensive and time-sensitive applications, unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) is proposed as an innovational paradigm to support the mobile users (MUs). As a promising technology, digital twin (DT) is capable of timely mapping the physical entities to virtual models, and reflecting the MEC network state in real-time. In this paper, we first propose an MEC network with multiple movable UAVs and one DT-empowered ground base station to enhance the MEC service for MUs. Considering the limited energy resource of both MUs and UAVs, we formulate an online problem of resource scheduling to minimize the weighted energy consumption of them. To tackle the difficulty of the combinational problem, we formulate it as a Markov decision process (MDP) with multiple types of agents. Since the proposed MDP has huge state space and action space, we propose a deep reinforcement learning approach based on multi-agent proximal policy optimization (MAPPO) with Beta distribution and attention mechanism to pursue the optimal computation offloading policy. Numerical results show that our proposed scheme is able to efficiently reduce the energy consumption and outperforms the benchmarks in performance, convergence speed and utilization of resources.

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Large-Scale Computation Offloading Using a Multi-Agent Reinforcement Learning in Heterogeneous Multi-Access Edge Computing

Cited by 47 publications

References 31 publications

Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning

Optimizing Risk-Aware Task Migration Algorithm Among Multiplex UAV Groups Through Hybrid Attention Multi-Agent Reinforcement Learning

A Survey of Machine Learning in Edge Computing: Techniques, Frameworks, Applications, Issues, and Research Directions

Energy Efficient Computation Offloading in Aerial Edge Networks With Multi-Agent Cooperation

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