# Adversarial attacks in consensus-based multi-agent reinforcement learning

arXiv: https://arxiv.org/abs/2103.06967 · HTML: https://mlsanity.com/paper/2103.06967/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 11 Mar 2021.

## Builds on (most cited first, 6 of 6)

- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithms](https://mlsanity.com/paper/1911.10635/) (2019)
- [Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents](https://mlsanity.com/paper/1802.08757/) (2018)
- [Policy Poisoning in Batch Reinforcement Learning and Control](https://mlsanity.com/paper/1910.05821/) (2019)
- [A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learning](https://mlsanity.com/paper/1907.03053/) (2019)
- [Distributed Reinforcement Learning via Gossip](https://mlsanity.com/paper/1310.7610/) (2013)

## Built on by (most cited first, 17 of 17)

- [Reinforcement Learning for Feedback-Enabled Cyber Resilience](https://mlsanity.com/paper/2107.00783/) (2021)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influence](https://mlsanity.com/paper/2302.03322/) (2023)
- [Assessing the Impact of Distribution Shift on Reinforcement Learning Performance](https://mlsanity.com/paper/2402.03590/) (2024)
- [Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checking](https://mlsanity.com/paper/2212.05337/) (2022)
- [AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2310.16772/) (2023)
- [Decentralized Adversarial Training over Graphs](https://mlsanity.com/paper/2303.13326/) (2023)
- [Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach](https://mlsanity.com/paper/2507.17070/) (2025)
- [Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning](https://mlsanity.com/paper/2307.00268/) (2023)
- [Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximation](https://mlsanity.com/paper/2111.06776/) (2021)
- [Adversarial Attacks in Cooperative AI](https://mlsanity.com/paper/2111.14833/) (2021)
- [Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation](https://mlsanity.com/paper/2203.05774/) (2022)
- [Ad Hoc Teamwork in the Presence of Adversaries](https://mlsanity.com/paper/2208.05071/) (2022)
- [BRNES: Enabling Security and Privacy-aware Experience Sharing in Multiagent Robotic and Autonomous Systems](https://mlsanity.com/paper/2308.01274/) (2023)
- [Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approach](https://mlsanity.com/paper/2401.13947/) (2024)
- [Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Space](https://mlsanity.com/paper/2508.15764/) (2025)
- [Fully Byzantine-Resilient Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2609.25701/) (2026)

## Abstract (arXiv)

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.