
Recent studies show that Deep Reinforcement Learning (DRL) models are vulnerable to
adversarial attacks. Most of these adversarial attacks have been conducted against DRL
by adding small perturbations to the observations. However, some attacks hold 
strong assumptions on the availability of the victim model, and some other attacks require a huge
amount of computation, which makes them less feasible for real world applications. In this work,
we expand on the existing literature by studying other aspects of performing such
attacks and provide a further exploration for vulnerabilities of DRL with realistic and efficient attacks. First, for the
plausible scenario where the attacker does not have access to the model parameters, we 
adapt and propose efficient black-box attack techniques to DRL settings. Second, to 
address the high computational demands of existing attacks, we introduce an efficient online 
sequential attacks that exploits temporal consistency across consecutive steps. Third, 
we explore the possibility of an attacker perturbing other quantities in the DRL 
setting, such as the environment dynamics. Finally, to account for imperfections in 
how an attacker would inject a perturbation in the physical world, we devise a method
for generating a robust physical perturbation to be printed, placed under various physical 
conditions, and observed by a real-world agent from different viewpoints. We conduct
extensive experiments both in simulation such as Atari games, robotics and
autonomous driving, and on real-world robotics, to compare the effectiveness of
the proposed attacks with several baseline approaches. To the best of our knowledge, we are the first to apply adversarial attacks on DRL systems to physical robots.

Characterizing Attacks on Deep Reinforcement Learning


**Session Chair:** <br><br> Mark Reynolds

Oral Session 3B6-2

technical paper

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

The AAMAS conference series was initiated in 2002 in Bologna, Italy as a joint event comprising the 6th International Conference on Autonomous Agents (AA), the 5th International Conference on Multiagent Systems (ICMAS), and the 9th International Workshop on Agent Theories, Architectures, and Languages (ATAL).

Subsequent AAMAS conferences have been held in Melbourne, Australia (July 2003), New York City, NY, USA (July 2004), Utrecht, The Netherlands (July 2005), Hakodate, Japan (May 2006), Honolulu, Hawaii, USA (May 2007), Estoril, Portugal (May 2008), Budapest, Hungary (May 2009), Toronto, Canada (May 2010), Taipei, Taiwan (May 2011), Valencia, Spain (June 2012), Minnesota, USA (May 2013), Paris, France (May 2014), Istanbul, Turkey (May 2015), Singapore (May 2016), São Paulo (2017) and Stockholm, Sweden (2018), Montreal (May 2019), Auckland (May 2020, Virtual), London (May 2021, Virtual).
<br>
<br>



A ticket is required to attend this event, please register using the link below:

https://aamas2022-conference.auckland.ac.nz/attending/registration/

Registration Is Required

AAMAS 2022

AAMAS (International Conference on Autonomous Agents and Multiagent Systems) is the largest and most influential conference in the area of agents and multiagent systems. The aim of the conference is to bring together researchers and practitioners in all areas of agent technology and to provide a single, high-profile, internationally renowned forum for research in the theory and practice of autonomous agents and multiagent systems. AAMAS is the flagship conference of the non-profit International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS).

Xinlei Pan

1

4

SHORT BIO

Presentations

Characterizing Attacks on Deep Reinforcement Learning

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES