Applications of multi-agent reinforcement learning in future internet: A comprehensive survey
Future Internet involves several emerging technologies such as 5G and beyond 5G
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
networks, vehicular networks, unmanned aerial vehicle (UAV) networks, and Internet of …
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 …
Single and multi-agent deep reinforcement learning for AI-enabled wireless networks: A tutorial
Deep Reinforcement Learning (DRL) has recently witnessed significant advances that have
led to multiple successes in solving sequential decision-making problems in various …
led to multiple successes in solving sequential decision-making problems in various …
Survey on reinforcement learning applications in communication networks
Y Qian, J Wu, R Wang, F Zhu… - … of Communications and …, 2019 - ieeexplore.ieee.org
In recent years, intelligent communication has drawn huge research efforts in both academia
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
and industry. With the advent of 5G technology, intelligent wireless terminals and intelligent …
Toward packet routing with fully distributed multiagent deep reinforcement learning
Packet routing is one of the fundamental problems in computer networks in which a router
determines the next-hop of each packet in the queue to get it as quickly as possible to its …
determines the next-hop of each packet in the queue to get it as quickly as possible to its …
Deep reinforcement learning for mobile 5G and beyond: Fundamentals, applications, and challenges
Future-generation wireless networks (5G and beyond) must accommodate surging growth in
mobile data traffic and support an increasingly high density of mobile users involving a …
mobile data traffic and support an increasingly high density of mobile users involving a …
Ns-3 meets openai gym: The playground for machine learning in networking research
P Gawłowicz, A Zubow - Proceedings of the 22nd International ACM …, 2019 - dl.acm.org
Recently, we have seen a boom of attempts to improve the operation of networking protocols
using machine learning techniques. The proposed reinforcement learning (RL) based …
using machine learning techniques. The proposed reinforcement learning (RL) based …
Deep reinforcement learning for Internet of Things: A comprehensive survey
The incumbent Internet of Things suffers from poor scalability and elasticity exhibiting in
communication, computing, caching and control (4Cs) problems. The recent advances in …
communication, computing, caching and control (4Cs) problems. The recent advances in …
Federated reinforcement learning for training control policies on multiple IoT devices
Reinforcement learning has recently been studied in various fields and also used to
optimally control IoT devices supporting the expansion of Internet connection beyond the …
optimally control IoT devices supporting the expansion of Internet connection beyond the …
Multiagent meta-reinforcement learning for adaptive multipath routing optimization
In this article, we investigate the routing problem of packet networks through multiagent
reinforcement learning (RL), which is a very challenging topic in distributed and autonomous …
reinforcement learning (RL), which is a very challenging topic in distributed and autonomous …