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Recent studies have shown that cooperative multi-agent deep reinforcement learning (c-MADRL) is under the threat of backdoor attacks.
F. A. Oliehoek, M. T. J. Spaan, and N. Vlassis, “Optimal and approximate q-value functions for decentralized pomdps,” J. Artif. Intell. Res. , vol. 32, pp. 289–353, 2008
2008
Earlier work this paper cites.
M. J. Hausknecht and P. Stone, “Deep recurrent q-learning for partially observable mdps,” in AAAI Fall Symposia , 2015, pp. 29–37
2015
Earlier work this paper cites.
L. Kraemer and B. Banerjee, “Multi-agent reinforcement learning as a rehearsal for decentralized planning,” Neurocomputing , vol. 190, pp. 82–94, 2016
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. K. Gupta, M. Egorov, and M. J. Kochenderfer, “Cooperative multi-agent control using deep reinforcement learning,” in AAMAS Workshops , vol. 10642, 2017, pp. 66–83
2017
Earlier work this paper cites.
B. Tran, J. Li, and A. Madry, “Spectral signatures in backdoor attacks,” in NeurIPS , 2018, pp. 8011–8021
2018
Earlier work this paper cites.
T. Rashid, M. Samvelyan, C. S. de Witt, G. Farquhar, J. N. Foerster, and S. Whiteson, “QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning,” in ICML , vol. 80, 2018, pp. 4292–4301
2018
Earlier work this paper cites.
J. N. Foerster, G. Farquhar, T. Afouras, N. Nardelli, and S. Whiteson, “Counterfactual multi-agent policy gradients,” in AAAI , 2018, pp. 2974–2982
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
N. Jaques, A. Lazaridou, E. Hughes, C. Gulcehre, P. Ortega, D. Strouse, J. Z. Leibo, and N. De Freitas, “Social influence as intrinsic motivation for multi-agent deep reinforcement learning,” in ICML . PMLR, 2019, pp. 3040–3049
2019
Earlier work this paper cites.
B. Chen, W. Carvalho, N. Baracaldo, H. Ludwig, B. Edwards, T. Lee, I. M. Molloy, and B. Srivastava, “Detecting backdoor attacks on deep neural networks by activation clustering,” in SafeAI@AAAI , ser. CEUR Workshop Proceedings, vol. 2301, 2019
2019
Earlier work this paper cites.
B. Wang, Y. Yao, S. Shan, H. Li, B. Viswanath, H. Zheng, and B. Y. Zhao, “Neural cleanse: Identifying and mitigating backdoor attacks in neural networks,” in IEEE Symposium on Security and Privacy , 2019, pp. 707–723
2019
Earlier work this paper cites.
P. Kiourti, K. Wardega, S. Jha, and W. Li, “Trojdrl: Evaluation of backdoor attacks on deep reinforcement learning,” in DAC , 2020, pp. 1–6
2020
Cited alongside, same era.
L. Wang, Z. Javed, X. Wu, W. Guo, X. Xing, and D. Song, “Backdoorl: Backdoor attack against competitive reinforcement learning,” in IJCAI , 2021, pp. 3699–3705
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Y. Wang, E. Sarkar, W. Li, M. Maniatakos, and S. E. Jabari, “Stop-and-go: Exploring backdoor attacks on deep reinforcement learning-based traffic congestion control systems,” IEEE TIFS , vol. 16, pp. 4772–4787, 2021
2021
Cited alongside, same era.
H. Zheng, X. Li, J. Chen, J. Dong, Y. Zhang, and C. Lin, “One4all: Manipulate one agent to poison the cooperative multi-agent reinforcement learning,” Computers & Security , vol. 124, p. 103005, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Yu, J. Liu, H. Guo, B. Mao, and N. Kato, “A spatiotemporal backdoor attack against behavior-oriented decision makers in metaverse: From perspective of autonomous driving,” IEEE JSAC , 2023
2023
Later among the works it cites.
J. Guo, A. Li, L. Wang, and C. Liu, “Policycleanse: Backdoor detection and mitigation for competitive reinforcement learning,” in ICCV , 2023, pp. 4676–4685
2023
Later among the works it cites.
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J. K. Terry, B. Black, N. Grammel, M. Jayakumar, A. Hari, R. Sullivan, L. S. Santos, C. Dieffendahl, C. Horsch, R. Perez-Vicente, N. L. Williams, Y. Lokesh, and P. Ravi, “Pettingzoo: Gym for multi-agent reinforcement learning,” in NeurIPS , 2021, pp. 15 032–15 043
2021
Cited alongside, same era.
2022
Cited alongside, same era.
C. Yu, A. Velu, E. Vinitsky, J. Gao, Y. Wang, A. M. Bayen, and Y. Wu, “The surprising effectiveness of PPO in cooperative multi-agent games,” in NeurIPS , 2022
2022
Cited alongside, same era.
Y. Yu, J. Liu, S. Li, K. Huang, and X. Feng, “A temporal-pattern backdoor attack to deep reinforcement learning,” in IEEE GLOBECOM , 2022, pp. 2710–2715
2022
Cited alongside, same era.
A. Oroojlooy and D. Hajinezhad, “A review of cooperative multi-agent deep reinforcement learning,” Applied Intelligence , vol. 53, no. 11, pp. 13 677–13 722, 2023
2023
Cited alongside, same era.
D. Chen, M. R. Hajidavalloo, Z. Li, K. Chen, Y. Wang, L. Jiang, and Y. Wang, “Deep multi-agent reinforcement learning for highway on-ramp merging in mixed traffic,” IEEE TITS , vol. 24, no. 11, pp. 11 623–11 638, 2023
2023
Cited alongside, same era.
Z. Ji, S. Wu, and C. Jiang, “Cooperative multi-agent deep reinforcement learning for computation offloading in digital twin satellite edge networks,” IEEE JSAC , vol. 41, no. 11, pp. 3414–3429, 2023
2023
Cited alongside, same era.
H. Zhang, M. Liu, and Y. Chen, “Backdoor attacks on multi-agent reinforcement learning-based spectrum management,” in IEEE GLOBECOM , 2023, pp. 3361–3365
2023
Cited alongside, same era.
X. Chen, W. Guo, G. Tao, X. Zhang, and D. Song, “BIRD: generalizable backdoor detection and removal for deep reinforcement learning,” in NeurIPS , 2023
2023
Later among the works it cites.
J. Cui, Y. Han, Y. Ma, J. Jiao, and J. Zhang, “Badrl: Sparse targeted backdoor attack against reinforcement learning,” in AAAI , 2024, pp. 11 687–11 694
2024
Later among the works it cites.
C. Gong, Z. Yang, Y. Bai, J. Shi, J. He, K. Li, B. Xu, A. Sinha, X. Hou, D. Lo, and T. Wang, “BAFFLE: Hiding backdoors in offline reinforcement learning datasets,” in IEEE Symposium on Security and Privacy , 2024, pp. 218–218
2024
Later among the works it cites.
Y. Yu, S. Yan, and J. Liu, “A spatiotemporal stealthy backdoor attack against cooperative multi-agent deep reinforcement learning,” in IEEE GLOBECOM , 2024
2024
Later among the works it cites.
Y. Li, Y. Jiang, Z. Li, and S. Xia, “Backdoor learning: A survey,” IEEE TNNLS , vol. 35, no. 1, pp. 5–22, 2024
2024
Later among the works it cites.
H. Zhu, Y. Zhao, S. Zhang, and K. Chen, “Neuralsanitizer: Detecting backdoors in neural networks,” IEEE TIFS , vol. 19, pp. 4970–4985, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
P. Sunehag, G. Lever, A. Gruslys, W. M. Czarnecki, V. F. Zambaldi, M. Jaderberg, M. Lanctot, N. Sonnerat, J. Z. Leibo, K. Tuyls, and T. Graepel, “Value-decomposition networks for cooperative multi-agent learning based on team reward,” in AAMAS , 2018, pp. 2085–2087
2087
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