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2021 | OriginalPaper | Chapter

Poisoning Attack for Inter-agent Transfer Learning

Authors : Zelei Cheng, Zuotian Li

Published in: Security and Privacy in Communication Networks

Publisher: Springer International Publishing

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Abstract

In reinforcement learning, high sample complexity is a big challenge to deal with. Inter-agent transfer learning is one solution to this challenge that can leverage the experience of other more competent agents. In this paradigm, a student can make a query to the teacher and the teacher will give some action advice given the current state. However, most previous works ignored the instruction reliability problem. In this work, we investigate the instruction reliability issue based on the one-to-one teaching framework and formulate the poisoning attack as an optimization problem. By solving the optimization problem, the attacker can significantly influence the performance of the student in three different query models. Evaluation highlights that we need to consider the instruction reliability when using teacher-student frameworks in reinforcement learning.

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Metadata
Title
Poisoning Attack for Inter-agent Transfer Learning
Authors
Zelei Cheng
Zuotian Li
Copyright Year
2021
Book
Security and Privacy in Communication Networks

Print ISBN: 978-3-030-90021-2

Electronic ISBN: 978-3-030-90022-9

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-90022-9_21

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