Reinforcement Learning for Routing in Ad Hoc Networks

Petteri Nurmi

5th International ICST Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks

Research Article

Reinforcement Learning for Routing in Ad Hoc Networks

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.1109/WIOPT.2007.4480049,
    author={Petteri Nurmi},
    title={Reinforcement Learning for Routing in Ad Hoc Networks},
    proceedings={5th International ICST Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks},
    publisher={IEEE},
    proceedings_a={WIOPT},
    year={2008},
    month={3},
    keywords={Ad hoc networks  Communication networks  Costs  Function approximation  Game theory  Learning  Parameter estimation  Routing  Stochastic processes  Uncertainty},
    doi={10.1109/WIOPT.2007.4480049}
}

Petteri Nurmi
Year: 2008
Reinforcement Learning for Routing in Ad Hoc Networks
WIOPT
IEEE
DOI: 10.1109/WIOPT.2007.4480049

Petteri Nurmi¹^,*

1: Helsinki Institute for Information Technology HIIT Department of Computer Science, P.O. Box 68, FI-00014 University of Helsinki, Finland

*Contact email: petteri.nurmi@cs.helsinki.fi

Abstract

We show how routing in ad hoc networks can be modeled as a sequential decision making problem with incomplete information. More precisely, we show how to map routing into a reinforcement learning problem involving a partially observable Markov decision process, and present an algorithm for optimizing the performance of the nodes in this model. We also present simulation results with our model

Keywords: Ad hoc networks Communication networks Costs Function approximation Game theory Learning Parameter estimation Routing Stochastic processes Uncertainty

Published: 2008-03-31
Publisher: IEEE
Modified: 2011-07-28

: http://dx.doi.org/10.1109/WIOPT.2007.4480049

Reinforcement Learning for Routing in Ad Hoc Networks

Abstract

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