Least-squares policy iteration
The Journal of Machine Learning Research
Adaptive Routing for Sensor Networks using Reinforcement Learning
CIT '06 Proceedings of the Sixth IEEE International Conference on Computer and Information Technology
Design and performance analysis of an inductive QoS routing algorithm
Computer Communications
An Adaptive Mechanism for Multipath Video Streaming over Video Distribution Network (VDN)
MMEDIA '09 Proceedings of the 2009 First International Conference on Advances in Multimedia
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Quality-of-service routing for supporting multimedia applications
IEEE Journal on Selected Areas in Communications
Reinforcement Learning: An Introduction
IEEE Transactions on Neural Networks
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Recently, wealthy network services such as Internet protocol television (IPTV) and Voice over IP (VoIP) are expected to become more pervasive over the Next Generation Network (NGN). In order to serve this purpose, the quality of these services should be evaluated subjectively by users. This is referred to as the quality of experience (QoE). The most important tendency of actual network services is maintaining the best QoE with network functions such as admission control, resource management, routing, traffic control, etc. Among of them, we focus here on routing mechanism. We propose in this paper a protocol integrating QoE measurement in routing paradigm to construct an adaptive and evolutionary system. Our approach is based on Reinforcement Learning concept. More concretely, we have used a least squares reinforcement learning technique called Least Squares Policy Iteration. Experimental results showed a significant performance gain over traditional routing protocols.