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Adversarial attacks in consensus-based multi-agent reinforcement learning

2021-03-11 21:44:18
Martin Figura, Krishna Chaitanya Kosaraju, Vijay Gupta
arXiv_AI
         
Abstract

Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature. In this work, we study the effect of adversarial attacks on a network that employs a consensus-based MARL algorithm. We show that an adversarial agent can persuade all the other agents in the network to implement policies that optimize an objective that it desires. In this sense, the standard consensus-based MARL algorithms are fragile to attacks.

Abstract (translated)

URL

https://arxiv.org/abs/2103.06967

PDF

https://arxiv.org/pdf/2103.06967.pdf

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