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Adversarial attacks in consensus-based multi-agent reinforcement learning

arXiv 2103.06967 ↗First posted 11 Mar 2021eess.SY cs.AI cs.LGcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 11 Mar 2021.

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6 of 21 references.

References · 6

  1. 2016Learning to Communicate with Deep Multi-Agent Reinforcement Learningcited by 561
  2. 2019Multi-Agent Reinforcement Learning: A Selective Overview of Theories and Algorithmscited by 555
  3. 2018Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agentscited by 189
  4. 2019Policy Poisoning in Batch Reinforcement Learning and Controlcited by 70
  5. 2019A Communication-Efficient Multi-Agent Actor-Critic Algorithm for Distributed Reinforcement Learningcited by 16
  6. 2013Distributed Reinforcement Learning via Gossipcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 3; 2023: 5; 2024: 2; 2025: 2; 2026: 1.

17 papers.

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  1. 2021Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligencecited by 18
  2. 2021Reinforcement Learning for Feedback-Enabled Cyber Resiliencecited by 18
  3. 2023Attacking Cooperative Multi-Agent Reinforcement Learning by Adversarial Minority Influencecited by 16
  4. 2022Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model Checkingcited by 5
  5. 2023AI Agent as Urban Planner: Steering Stakeholder Dynamics in Urban Planning via Consensus-based Multi-Agent Reinforcement Learningcited by 4
  6. 2024Assessing the Impact of Distribution Shift on Reinforcement Learning Performancecited by 4
  7. 2023Decentralized Adversarial Training over Graphscited by 3
  8. 2025Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approachcited by 3
  9. 2023Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learningcited by 2
  10. 2021Resilient Consensus-based Multi-agent Reinforcement Learning with Function Approximationcited by 1
  11. 2021Adversarial Attacks in Cooperative AIcited by 1
  12. 2022Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulationcited by 1
5 more
  1. 2022Ad Hoc Teamwork in the Presence of Adversariescited by 1
  2. 2023BRNES: Enabling Security and Privacy-aware Experience Sharing in Multiagent Robotic and Autonomous Systemscited by 1
  3. 2024Peer-to-Peer Energy Trading of Solar and Energy Storage: A Networked Multiagent Reinforcement Learning Approachcited by 1
  4. 2025Distributed Detection of Adversarial Attacks in Multi-Agent Reinforcement Learning with Continuous Action Spacecited by 0
  5. 2026Fully Byzantine-Resilient Multi-Agent Reinforcement Learningcited by 0