Reinforcement Learning for Adaptive Routing

Preprint

28 March 2007

preprint
Published by arXiv in arXiv

https://doi.org/10.48550/arXiv.cs/0703138

Abstract

Reinforcement learning means learning a policy--a mapping of observations into actions--based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. We present an application of gradient ascent algorithm for reinforcement learning to a complex domain of packet routing in network communication and compare the performance of this algorithm to other routing methods on a benchmark problem.

Keywords

OBSERVATIONS INTO ACTIONS
REINFORCEMENT LEARNING
ROUTING REINFORCEMENT
LEARNING FOR ADAPTIVE
ADAPTIVE ROUTING
MEANS LEARNING
ALGORITHM

All Related Versions

Version 1, 2007-03-28, ArXiv

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