Reinforcement Learning for Adaptive Routing

Leonid Peshkin; Virginia Savova

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Reinforcement Learning for Adaptive Routing

Leonid Peshkin·Virginia Savova

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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.

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BibTeX RIS

Leonid Peshkin, Virginia Savova. 2007-03-28. Reinforcement Learning for Adaptive Routing. https://arxiv.org/abs/cs/0703138

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