mlsanityData to 7 Oct 2026

Enhancing the Robustness of QMIX against State-adversarial Attacks

arXiv 2307.00907 ↗First posted 3 Jul 2023cs.LG cs.CR cs.MAcited by 3

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Abstract (arXiv)

Deep reinforcement learning (DRL) performance is generally impacted by state-adversarial attacks, a perturbation applied to an agent's observation. Most recent research has concentrated on robust single-agent reinforcement learning (SARL) algorithms against state-adversarial attacks. Still, there has yet to be much work on robust multi-agent reinforcement learning. Using QMIX, one of the popular cooperative multi-agent reinforcement algorithms, as an example, we discuss four techniques to improve the robustness of SARL algorithms and extend them to multi-agent scenarios. To increase the robustness of multi-agent reinforcement learning (MARL) algorithms, we train models using a variety of attacks in this research. We then test the models taught using the other attacks by subjecting them to the corresponding attacks throughout the training phase. In this way, we organize and summarize techniques for enhancing robustness when used with MARL.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 30 references that are in the map. First posted on arXiv 3 Jul 2023.

Builds on

18 of 30 references.

References · 18

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  2. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  3. 2017Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environmentscited by 1,519
  4. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  5. 2019The StarCraft Multi-Agent Challengecited by 589
  6. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 475
  7. 2020Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observationscited by 235
  8. 2017Robust Deep Reinforcement Learning with Adversarial Attackscited by 152
  9. 2021Robust Reinforcement Learning on State Observations with Learned Optimal Adversarycited by 133
  10. 2017Delving into adversarial attacks on deep policiescited by 104
  11. 2020Robust Deep Reinforcement Learning through Adversarial Losscited by 73
  12. 2021Who Is the Strongest Enemy? Towards Optimal and Efficient Evasion Attacks in Deep RLcited by 54
6 more
  1. 2020On the Robustness of Cooperative Multi-Agent Reinforcement Learningcited by 39
  2. 2022Towards Comprehensive Testing on the Robustness of Cooperative Multi-agent Reinforcement Learningcited by 21
  3. 2022What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?cited by 21
  4. 2018Sequential Attacks on Agents for Long-Term Adversarial Goalscited by 16
  5. 2022RoMFAC: A robust mean-field actor-critic reinforcement learning against adversarial perturbations on statescited by 16
  6. 2022Sparse Adversarial Attack in Multi-agent Reinforcement Learningcited by 8

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025PNAct: Crafting Backdoor Attacks in Safe Reinforcement Learningcited by 2
  2. 2025Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defensescited by 2
  3. 2026Distributionally Robust Cooperative Multi-Agent Reinforcement Learning via Robust Value Factorizationcited by 0