Reinforcement Learning for Network Routing

Graduate Project

Pubblico Deposited

Citeable URL: https://ir.library.oregonstate.edu/concern/graduate_projects/6t053g06m

Descriptions

Attribute Name	Values
Creator	Yalamanchi, Hema Jyothi
Abstract	Efficient routing of information packets in dynamically changing communication networks requires routing policies that adapt to changes in load levels, traffic patterns and network topologies. Reinforcement Learning (RL) is an area of artificial intelligence that studies algorithms that dynamically optimize their performance based on experience in an environment. RL, thus, is a promising framework for developing adaptive network routing algorithms and there have been a number of proposed RL-based routing algorithms. In this project, we developed an infrastructure for evaluating RL-based routing mechanisms and use it to evaluate and compare a number of existing algorithms under various network conditions.
Resource Type	Capstone Project
Date Available	2012-08-09T20:22:40+00:00
Date Issued	2007-03-30
Degree Level	Master's
Degree Name	Master of Science (M.S.)
Degree Field	Computer Science
Degree Grantor	Oregon State University
Commencement Year	2007
Advisor	Fern, Alan Nguyen, Thinh P.
Committee Member	Bose, Bella
Academic Affiliation	Electrical Engineering and Computer Science
Subject	Routing (Computer network management) Reinforcement learning
Dichiarazione dei diritti	In Copyright
Publisher	Oregon State Unversity
Peer Reviewed	No
Language	English [eng]
Replaces	http://hdl.handle.net/1957/32143

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