Multi-agent deep reinforcement learning for packet routing in tactical mobile sensor networks
Tactical wireless sensor networks (T-WSNs) are used in critical data-gathering military
operations, such as battlefield surveillance, combat monitoring, and intrusion detection …
operations, such as battlefield surveillance, combat monitoring, and intrusion detection …
[HTML][HTML] Reinforcement learning-based virtual network embedding: A comprehensive survey
Virtual network embedding plays a vital role in network virtualization, as it determines the
deployment and connection of virtual networks to the physical network in the 5G and …
deployment and connection of virtual networks to the physical network in the 5G and …
Intelligent routing algorithm for wireless sensor networks dynamically guided by distributed neural networks
Z Liu, Y Liu, X Wang - Computer Communications, 2023 - Elsevier
Using reinforcement learning to adjust the power balance of sensor nodes dynamically is an
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …
essential approach for extending the lifetime of wireless sensor networks (WSNs), which …
Routing optimization meets Machine Intelligence: A perspective for the future network
The future network is expected to support extremely large bandwidth, ultra-low latency or
deterministic delay, extremely high reliability, and massive connectivity for novel forward …
deterministic delay, extremely high reliability, and massive connectivity for novel forward …
A deep reinforcement learning-based multi-optimality routing scheme for dynamic IoT networks
With the development of Internet of Things (IoT) and 5G technologies, more and more
applications, such as autonomous vehicles and tele-medicine, become more sensitive to …
applications, such as autonomous vehicles and tele-medicine, become more sensitive to …
AI-assisted framework for green-routing and load balancing in hybrid software-defined networking: Proposal, challenges and future perspective
R Etengu, SC Tan, LC Kwang, FM Abbou… - IEEE …, 2020 - ieeexplore.ieee.org
The explosive growth of IP networks, the advent of cloud computing, and the rapid progress
in wireless communications witnessed today reflect significant progress towards meeting the …
in wireless communications witnessed today reflect significant progress towards meeting the …
GDDR: GNN-based data-driven routing
We explore the feasibility of combining Graph Neural Network-based policy architectures
with Deep Reinforcement Learning as an approach to problems in systems. This fits …
with Deep Reinforcement Learning as an approach to problems in systems. This fits …
Robust and scalable routing with multi-agent deep reinforcement learning for MANETs
Highly dynamic mobile ad-hoc networks (MANETs) are continuing to serve as one of the
most challenging environments to develop and deploy robust, efficient, and scalable routing …
most challenging environments to develop and deploy robust, efficient, and scalable routing …
DeepCQ+: Robust and scalable routing with multi-agent deep reinforcement learning for highly dynamic networks
Highly dynamic mobile ad-hoc networks (MANETs) remain as one of the most challenging
environments to develop and deploy robust, efficient, and scalable routing protocols. In this …
environments to develop and deploy robust, efficient, and scalable routing protocols. In this …
面向动态拓扑网络的深度强化学习路由技术.
伍元胜 - Telecommunication Engineering, 2021 - search.ebscohost.com
针对现有智能路由技术无法适用于动态拓扑的不足, 提出了一种面向动态拓扑的深度强化学习
智能路由技术, 通过使用图神经网络近似PPO (Proximal Policy Optimization) …
智能路由技术, 通过使用图神经网络近似PPO (Proximal Policy Optimization) …