Applications of deep reinforcement learning in communications and networking: A survey
This paper presents a comprehensive literature review on applications of deep
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
reinforcement learning (DRL) in communications and networking. Modern networks, eg …
Deep learning in mobile and wireless networking: A survey
The rapid uptake of mobile devices and the rising popularity of mobile applications and
services pose unprecedented demands on mobile and wireless networking infrastructure …
services pose unprecedented demands on mobile and wireless networking infrastructure …
A DRL agent for jointly optimizing computation offloading and resource allocation in MEC
This article studies the joint optimization problem of computation offloading and resource
allocation (JCORA) in mobile-edge computing (MEC). Deep reinforcement learning (DRL) is …
allocation (JCORA) in mobile-edge computing (MEC). Deep reinforcement learning (DRL) is …
Federated reinforcement learning: Techniques, applications, and open challenges
This paper presents a comprehensive survey of Federated Reinforcement Learning (FRL),
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
an emerging and promising field in Reinforcement Learning (RL). Starting with a tutorial of …
Survey on machine learning for intelligent end-to-end communication toward 6G: From network access, routing to traffic control and streaming adaption
The end-to-end quality of service (QoS) and quality of experience (QoE) guarantee is quite
important for network optimization. The current 5G and conceived 6G network in the future …
important for network optimization. The current 5G and conceived 6G network in the future …
A gentle introduction to reinforcement learning and its application in different fields
Due to the recent progress in Deep Neural Networks, Reinforcement Learning (RL) has
become one of the most important and useful technology. It is a learning method where a …
become one of the most important and useful technology. It is a learning method where a …
Performance optimization for blockchain-enabled industrial Internet of Things (IIoT) systems: A deep reinforcement learning approach
Recent advances in the industrial Internet of things (IIoT) provide plenty of opportunities for
various industries. To address the security and efficiency issues of the massive IIoT data …
various industries. To address the security and efficiency issues of the massive IIoT data …
RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN
Network modeling is a key enabler to achieve efficient network operation in future self-
driving Software-Defined Networks. However, we still lack functional network models able to …
driving Software-Defined Networks. However, we still lack functional network models able to …
Knowledge-driven service offloading decision for vehicular edge computing: A deep reinforcement learning approach
The smart vehicles construct Internet of Vehicle (IoV), which can execute various intelligent
services. Although the computation capability of a vehicle is limited, multi-type of edge …
services. Although the computation capability of a vehicle is limited, multi-type of edge …
Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case
Abstract Deep Reinforcement Learning (DRL) has shown a dramatic improvement in
decision-making and automated control problems. Consequently, DRL represents a …
decision-making and automated control problems. Consequently, DRL represents a …