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Cooperative multi-agent reinforcement learning (c-MARL) is widely applied in safety-critical scenarios, thus the analysis of robustness for c-MARL models is profoundly important.
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Adversarial attacks on neural network policies
Huang, S.; Papernot, N.; Goodfellow, I.; Duan, Y.; and Abbeel, P. 2017 · 2017
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Kos, J.; and Song, D. 2017 · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
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Value-decomposition networks for cooperative multi-agent learning
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Cooperative multi-agent reinforcement learning for low-level wireless communication
On the robustness of cooperative multi-agent reinforcement learning
Lin, J.; Dzeparoska, K.; Zhang, S. Q.; Leon-Garcia, A.; and Papernot, N. 2020 · 2020
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Lütjens, B.; Everett, M.; and How, J. P. 2020 · 2020
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Eysenbach, B.; and Levine, S. 2021 · 2021
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Kumar, A.; Levine, A.; and Feizi, S. 2021 · 2021
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de Vrieze, C.; Barratt, S.; Tsai, D.; and Sahai, A. 2018 · 2018
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Counterfactual multi-agent policy gradients
Foerster, J.; Farquhar, G.; Afouras, T.; Nardelli, N.; and Whiteson, S. 2018 · 2018
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Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I. J.; and Bengio, S. 2018 · 2018
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Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning
Rashid, T.; Samvelyan, M.; Schroeder, C.; Farquhar, G.; Foerster, J.; and Whiteson, S. 2018 · 2018
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Certified adversarial robustness via randomized smoothing
Cohen, J.; Rosenfeld, E.; and Kolter, Z. 2019 · 2019
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ma-gym: Collection of multi-agent environments based on OpenAI gym
Koul, A. 2019 · 2019
Cited alongside, same era.
Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the Hamming Distance
Ruan, W.; Wu, M.; Sun, Y.; Huang, X.; Kroening, D.; and Kwiatkowska, M. 2019 · 2019
Cited alongside, same era.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H.; Li, J.; Razenshteyn, I.; Zhang, P.; Zhang, H.; Bubeck, S.; and Yang, G. 2019 · 2019
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Wu, F.; Li, L.; Huang, Z.; Vorobeychik, Y.; Zhao, D.; and Li, B. 2021 · 2021
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Enhancing Adversarial Training with Second-Order Statistics of Weights
Jin, G.; Yi, X.; Huang, W.; Schewe, S.; and Huang, X. 2022 · 2022
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3DVerifier: efficient robustness verification for 3D point cloud models
Mu, R.; Ruan, W.; Marcolino, L. S.; and Ni, Q. 2022 · 2022
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Evaluating Robustness of Cooperative MARL: A Model-based Approach
Pham, N. H.; Nguyen, L. M.; Chen, J.; Lam, H. T.; Das, S.; and Weng, T.-W. 2022 · 2022
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Certifiably Robust Policy Learning against Adversarial Communication in Multi-agent Systems
Sun, Y.; Zheng, R.; Hassanzadeh, P.; Liang, Y.; Feizi, S.; Ganesh, S.; and Huang, F. 2022 · 2022
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Deep learning and its adversarial robustness: A brief introduction
Wang, F.; Zhang, C.; Xu, P.; and Ruan, W. 2022 · 2022
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Quantifying safety risks of deep neural networks
Xu, P.; Ruan, W.; and Huang, X. 2022 · 2022
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DIMBA: discretely masked black-box attack in single object tracking
Yin, X.; Ruan, W.; and Fieldsend, J. 2022 · 2022
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RePreM: Representation Pre-training with Masked Model for Reinforcement Learning
Cai, Y.; Zhang, C.; Shen, W.; Zhang, X.; Ruan, W.; and Huang, L. 2023 · 2023
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