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2016 | OriginalPaper | Chapter

Preserving Privacy of Agents in Reinforcement Learning for Distributed Cognitive Radio Networks

Authors : Geong Sen Poh, Kok-Lim Alvin Yau

Published in: Neural Information Processing

Publisher: Springer International Publishing

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Abstract

Reinforcement learning (RL) is one of the artificial intelligence approaches that has been deployed effectively to improve performance of distributed cognitive radio networks (DCRNs). However, in existing proposals that involve multi-agents, perceptions of the agents are shared in plain in order to calculate optimal actions. This raises privacy concern where an agent learns private information (e.g. Q-values) of the others, which can then be used to infer, for instance, the actions of these other agents. In this paper, we provide a preliminary investigation and a privacy-preserving protocol on multi-agent RL in DCRNs. The proposed protocol provides RL computations without revealing agents’ private information. We also discuss the security and performance of the protocol.

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Metadata
Title
Preserving Privacy of Agents in Reinforcement Learning for Distributed Cognitive Radio Networks
Authors
Geong Sen Poh
Kok-Lim Alvin Yau
Copyright Year
2016
Book
Neural Information Processing

Print ISBN: 978-3-319-46686-6

Electronic ISBN: 978-3-319-46687-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-46687-3_61

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