Hijacking Robot Teams Through Adversarial CommunicationDownload PDF

Published: 30 Aug 2023, Last Modified: 15 Oct 2023CoRL 2023 OralReaders: Everyone
Keywords: Adversarial Attacks, Multi-Agent Reinforcement Learning
TL;DR: We contribute a novel black-box adversarial method that learns to hijack robot communication in a multi-agent setting without their ground truth reward or access to their policies
Abstract: Communication is often necessary for robot teams to collaborate and complete a decentralized task. Multi-agent reinforcement learning (MARL) systems allow agents to learn how to collaborate and communicate to complete a task. These domains are ubiquitous and include safety-critical domains such as wildfire fighting, traffic control, or search and rescue missions. However, critical vulnerabilities may arise in communication systems as jamming the signals can interrupt the robot team. This work presents a framework for applying black-box adversarial attacks to learned MARL policies by manipulating only the communication signals between agents. Our system only requires observations of MARL policies after training is complete, as this is more realistic than attacking the training process. To this end, we imitate a learned policy of the targeted agents without direct interaction with the environment or ground truth rewards. Instead, we infer the rewards by only observing the behavior of the targeted agents. Our framework reduces reward by 201% compared to an equivalent baseline method and also shows favorable results when deployed in real swarm robots. Our novel attack methodology within MARL systems contributes to the field by enhancing our understanding on the reliability of multi-agent systems.
Student First Author: yes
Supplementary Material: zip
Instructions: I have read the instructions for authors (https://corl2023.org/instructions-for-authors/)
Publication Agreement: pdf
9 Replies