Reinforcement learning based routing in networks: Review and classification of approaches
Z Mammeri - Ieee Access, 2019 - ieeexplore.ieee.org
Reinforcement learning (RL), which is a class of machine learning, provides a framework by
which a system can learn from its previous interactions with its environment to efficiently …
which a system can learn from its previous interactions with its environment to efficiently …
Survey on unmanned aerial vehicle networks: A cyber physical system perspective
Unmanned aerial vehicle (UAV) networks are playing an important role in various areas due
to their agility and versatility, which have attracted significant attentions from both the …
to their agility and versatility, which have attracted significant attentions from both the …
Machine learning meets communication networks: Current trends and future challenges
The growing network density and unprecedented increase in network traffic, caused by the
massively expanding number of connected devices and online services, require intelligent …
massively expanding number of connected devices and online services, require intelligent …
QR-SDN: Towards reinforcement learning states, actions, and rewards for direct flow routing in software-defined networks
Flow routing can achieve fine-grained network performance optimizations by routing distinct
packet traffic flows over different network paths. While the centralized control of Software …
packet traffic flows over different network paths. While the centralized control of Software …
Comparing openflow controller paradigms scalability: Reactive and proactive
MP Fernandez - 2013 IEEE 27th International Conference on …, 2013 - ieeexplore.ieee.org
The OpenFlow architecture is a proposal from the Clean Slate initiative to define a new
Internet architecture where the network devices are simple, and the control and …
Internet architecture where the network devices are simple, and the control and …
Learning and generating distributed routing protocols using graph-based deep learning
Automated network control and management has been a long standing target of network
protocols. We address in this paper the question of automated protocol design, where …
protocols. We address in this paper the question of automated protocol design, where …
Decentralized control of partially observable Markov decision processes
Markov decision processes (MDPs) are often used to model sequential decision problems
involving uncertainty under the assumption of centralized control. However, many large …
involving uncertainty under the assumption of centralized control. However, many large …
Principles and applications of swarm intelligence for adaptive routing in telecommunications networks
In the past few years, there has been much research on the application of swarm
intelligence to the problem of adaptive routing in telecommunications networks. A large …
intelligence to the problem of adaptive routing in telecommunications networks. A large …
Distributed policy search reinforcement learning for job-shop scheduling tasks
T Gabel, M Riedmiller - International Journal of production …, 2012 - Taylor & Francis
We interpret job-shop scheduling problems as sequential decision problems that are
handled by independent learning agents. These agents act completely decoupled from one …
handled by independent learning agents. These agents act completely decoupled from one …
An action language for multi-agent domains
The goal of this paper is to investigate an action language, called m A⁎, for representing
and reasoning about actions and change in multi-agent domains. The language, as …
and reasoning about actions and change in multi-agent domains. The language, as …