
As reinforcement learning (RL) systems are deployed in various safety-critical applications, it is imperative to understand how vulnerable they are to adversarial attacks. Of these, an environment-poisoning attack (EPA) is considered particularly insidious, since environment hyper-parameters are significant factors in determining an RL policy, yet prone to be accessed by third parties. The success of existing EPAs relies on comprehensive prior knowledge of the attacked RL system, including RL agent’s learning mechanism and/or its environment model. Unfortunately, such an assumption of prior knowledge creates an unrealistic attack, one that poses little threat to real-world RL systems.

In this paper, we propose a Double-Black-Box EPA framework, only assuming the attacker's ability to alter environment hyper-parameters. Considering that environment alteration comes at a cost, we seek minimal poisoning in an unknown environment, that aims to force a black-box RL agent to learn an attacker-designed policy. To this end, we incorporate an inference module in our framework to capture the internal information of the unknown RL system and, accordingly, learn an adaptive strategy based on an approximation of our attack objective. We empirically show the threat posed by our attack to both tabular-RL and deep-RL algorithms, in both discrete and continuous environments.

Spiking Pitch Black: Poisoning an Unknown Environment to Attack Unknown Reinforcement Learners


Group decisions are often complicated by a deadline. For example, in committee hiring decisions, the deadline might be a budget's start date or the beginning of a semester. It may be that if no candidate is supported by a strong majority, the default is to hire no one---an option that may cost dearly. Hence, committee members might prefer to agree on a reasonable, rather than the best, candidate, to avoid unfilled positions. Here, we propose a model for the above scenario - Consensus Under a Deadline (CUD)-based on a time-bounded iterative voting process. We provide theoretical convergence guarantees and an analysis of the resulting decision quality. An extensive experimental study demonstrates more subtle features of CUDs, e.g., the difference between two simple types of committee member behavior, lazy vs. proactive voters. Finally, a user study examines the differences between the behavior of rational voting bots and human voters, concluding that it may often be best to have bots play on the humans' behalf.

Reaching Consensus Under a Deadline


**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).
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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).

Zinovi Rabinovich

2

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Presentations

Spiking Pitch Black: Poisoning an Unknown Environment to Attack Unknown Reinforcement Learners

Reaching Consensus Under a Deadline

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