Parameterized Deep Reinforcement Learning With Hybrid Action Space for Edge Task Offloading
T Wang, Y Deng, Z Yang, Y Wang… - IEEE Internet of Things …, 2023 - ieeexplore.ieee.org
Multiaccess edge computing (MEC) has emerged as a promising solution that can enable
low-end terminal devices to run large complex applications by offloading their tasks to edge …
low-end terminal devices to run large complex applications by offloading their tasks to edge …
Towards efficient task offloading at the edge based on meta-reinforcement learning with hybrid action space
As a critical concern of multi-access edge computing (MEC), task offloading has received
extensive attention. Although deep reinforcement learning (DRL) has achieved great …
extensive attention. Although deep reinforcement learning (DRL) has achieved great …
DMRO: A deep meta reinforcement learning-based task offloading framework for edge-cloud computing
With the explosive growth of mobile data and the unprecedented demand for computing
power, resource-constrained edge devices cannot effectively meet the requirements of …
power, resource-constrained edge devices cannot effectively meet the requirements of …
Fast adaptive task offloading in edge computing based on meta reinforcement learning
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to
reduce network traffic and service latency. A fundamental problem in MEC is how to …
reduce network traffic and service latency. A fundamental problem in MEC is how to …
Deadline-aware task offloading with partially-observable deep reinforcement learning for multi-access edge computing
Over the past years, computationally-intensive mobile applications, such as interactive
games and augmented reality, have gained enormous popularity. This phenomenon has …
games and augmented reality, have gained enormous popularity. This phenomenon has …
MR-DRO: A fast and efficient task offloading algorithm in heterogeneous edge/cloud computing environments
With the rapid development of Internet of Things (IoT) and next-generation communication
technologies, resource-constrained mobile devices (MDs) fail to meet the demand of …
technologies, resource-constrained mobile devices (MDs) fail to meet the demand of …
Multi-agent DRL for edge computing: A real-time proportional compute offloading
K Jia, H Xia, R Zhang, Y Sun, K Wang - Computer Networks, 2024 - Elsevier
Abstract In the Industrial Internet of Things, devices with limited computing power and
energy storage often rely on offloading tasks to edge servers for processing. However …
energy storage often rely on offloading tasks to edge servers for processing. However …
Deep reinforcement learning and markov decision problem for task offloading in mobile edge computing
X Gao, MC Ang, SA Althubiti - Journal of Grid Computing, 2023 - Springer
Abstract Mobile Edge Computing (MEC) offers cloud-like capabilities to mobile users,
making it an up-and-coming method for advancing the Internet of Things (IoT). However …
making it an up-and-coming method for advancing the Internet of Things (IoT). However …
Federated Deep Reinforcement Learning-based task offloading system in edge computing environment
H Merakchi, M Bagaa, AO Messaoud… - … 2023-2023 IEEE …, 2023 - ieeexplore.ieee.org
Nowadays, Internet of Things (IoT) devices are gaining momentum globally. However, due
to their limited size, these devices have limited battery capacity, computational resources …
to their limited size, these devices have limited battery capacity, computational resources …
FLIRRAS: fast learning with integrated reward and reduced action space for online multitask offloading
With the rapid development of edge data intelligence, task offloading (TO) and resource
allocation (RA) optimization in multiaccess edge computing networks can significantly …
allocation (RA) optimization in multiaccess edge computing networks can significantly …