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SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning

2023-01-11 04:25:00
Maxwell Standen, Junae Kim, Claudia Szabo
arXiv_AI
             
Abstract

Multi-Agent Reinforcement Learning (MARL) is vulnerable to Adversarial Machine Learning (AML) attacks and needs adequate defences before it can be used in real world applications. We have conducted a survey into the use of execution-time AML attacks against MARL and the defences against those attacks. We surveyed related work in the application of AML in Deep Reinforcement Learning (DRL) and Multi-Agent Learning (MAL) to inform our analysis of AML for MARL. We propose a novel perspective to understand the manner of perpetrating an AML attack, by defining Attack Vectors. We develop two new frameworks to address a gap in current modelling frameworks, focusing on the means and tempo of an AML attack against MARL, and identify knowledge gaps and future avenues of research.

Abstract (translated)

URL

https://arxiv.org/abs/2301.04299

PDF

https://arxiv.org/pdf/2301.04299.pdf

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