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Backdoor attacks on reinforcement learning implant a backdoor in a victim agent's policy.
Design of intentional backdoors in sequential models
Yang, Z.; Iyer, N.; Reimann, J.; and Virani, N. 2019 · 1902
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Optimal and approximate Q-value functions for decentralized POMDPs
Oliehoek, F. A.; Spaan, M. T.; and Vlassis, N. 2008 · 2008
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Baaan: Backdoor attacks against autoencoder and gan-based machine learning models
Salem, A.; Sautter, Y.; Backes, M.; Humbert, M.; and Zhang, Y. 2020 · 2010
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, M. G.; Graves, A.; Riedmiller, M.; Fidjeland, A. K.; Ostrovski, G.; et al. 2015 · 2015
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A concise introduction to decentralized POMDPs
Oliehoek, F. A.; and Amato, C. 2016 · 2016
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Targeted backdoor attacks on deep learning systems using data poisoning
Chen, X.; Liu, C.; Li, B.; Lu, K.; and Song, D. 2017 · 2017
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BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Gu, T.; Dolan-Gavitt, B.; and Garg, S. 2017 · 2017
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Trojaning attack on neural networks
Liu, Y.; Ma, S.; Aafer, Y.; Lee, W.-C.; Zhai, J.; Wang, W.; and Zhang, X. 2017 · 2017
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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
Cited alongside, same era.
Reinforcement learning: An introduction
Sutton, R. S.; and Barto, A. G. 2018 · 2018
Cited alongside, same era.
Analyzing federated learning through an adversarial lens
Bhagoji, A. N.; Chakraborty, S.; Mittal, P.; and Calo, S. 2019 · 2019
Cited alongside, same era.
Exploration by random network distillation
Burda, Y.; Edwards, H.; Storkey, A.; and Klimov, O. 2019 · 2019
Cited alongside, same era.
A survey and critique of multiagent deep reinforcement learning
Hernandez-Leal, P.; Kartal, B.; and Taylor, M. E. 2019 · 2019
The StarCraft Multi-Agent Challenge
Samvelyan, M.; Rashid, T.; Schroeder de Witt, C.; Farquhar, G.; Nardelli, N.; Rudner, T. G. J.; Hung, C.-M.; Torr, P. H. S.; Foerster, J.; and Whiteson, S. 2019 · 2019
Later among the works it cites.
Dba: Distributed backdoor attacks against federated learning
Xie, C.; Huang, K.; Chen, P.-Y.; and Li, B. 2019 · 2019
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Attack of the tails: Yes, you really can backdoor federated learning
Wang, H.; Sreenivasan, K.; Rajput, S.; Vishwakarma, H.; Agarwal, S.; Sohn, J.-y.; Lee, K.; and Papailiopoulos, D. 2020 · 2020
Later among the works it cites.
Poisoning Deep Reinforcement Learning Agents with In-Distribution Triggers
Ashcraft, C.; and Karra, K. 2021 · 2021
Later among the works it cites.
Badnl: Backdoor attacks against nlp models
Chen, X.; Salem, A.; Backes, M.; Ma, S.; and Zhang, Y. 2021 · 2021
Later among the works it cites.
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Cited alongside, same era.
TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents
Kiourti, P.; Wardega, K.; Jha, S.; and Li, W. 2019 · 2019
Cited alongside, same era.
BACKDOORL: Backdoor Attack against Competitive Reinforcement Learning
Wang, L.; Javed, Z.; Wu, X.; Guo, W.; Xing, X.; and Song, D. 2021a
Cited in the paper.
Stop-and-Go: Exploring Backdoor Attacks on Deep Reinforcement Learning-Based Traffic Congestion Control Systems
Wang, Y.; Sarkar, E.; Li, W.; Maniatakos, M.; and Jabari, S. E. 2021b
Cited in the paper.
MARNet: Backdoor Attacks Against Cooperative Multi-Agent Reinforcement Learning
Chen, Y.; Zheng, Z.; and Gong, X. 2022 · 2022
Closest in time.
A Temporal-Pattern Backdoor Attack to Deep Reinforcement Learning
Yu, Y.; Liu, J.; Li, S.; Huang, K.; and Feng, X. 2022 · 2022
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