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Learning from raw high dimensional data via interaction with a given environment has been effectively achieved through the utilization of deep neural networks.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A.; Sutskever, I.; and E. Hinton, G. 2012 · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G.; Naddaf, Y.; Veness, J.; and Bowling, M. 2013 · 2013
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Intriguing properties of neural networks
Szegedy, C.; Zaremba, W.; Sutskever, I.; Bruna, J.; Erhan, D.; Goodfellow, I.; and Fergus, R. 2014 · 2014
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Explaning and Harnessing Adversarial Examples
Goodfellow, I.; Shelens, J.; and Szegedy, C. 2015 · 2015
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Human-level control through deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Rusu, A. A.; Veness, J.; Bellemare, a. G.; Graves, A.; Riedmiller, M.; Fidjeland, A.; Ostrovski, G.; Petersen, S.; Beattie, C.; Sadik, A.; Antonoglou; King, H.; Kumaran, D.; Wierstra, D.; Legg, S.; and Hassabis, D. 2015 · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K.; and Zisserman, A. 2015 · 2015
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Brockman, G.; Cheung, V.; Pettersson, L.; Schneider, J.; Schulman, J.; Tang, J.; and Zaremba, W. 2016 · 2016
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Deep Reinforcement Learning with Double Q-Learning
Hasselt, H. v.; Guez, A.; and Silver, D. 2016 · 2016
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SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and¡ 0.5 MB model size
Iandola, F. N.; Han, S.; Moskewicz, M. W.; Ashraf, K.; J. Dally, W.; and Keutzer, K. 2016 · 2016
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Adversarial examples in the physical world
Kurakin, A.; Goodfellow, I.; and Bengio, S. 2016 · 2016
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Prioritized Experience Replay
Schaul, T.; Quan, J.; Antonogloua, I.; and Silver, D. 2016 · 2016
Cited alongside, same era.
Towards Evaluating the robustness of neural networks
Carlini, N.; and Wagner, D. 2017 · 2017
Cited alongside, same era.
Adversarial Attacks on Neural Network Policies
Huang, S.; Papernot, N.; Goodfellow, Y., Ian an Duan; and Abbeel, P. 2017 · 2017
Cited alongside, same era.
Delving Into Adversarial Attacks on Deep Policies
Kos, J.; and Song, D. 2017 · 2017
Cited alongside, same era.
Adversarially Robust Policy Learning: Active Construction of Physically-Plausible Perturbations
Mandlekar, A.; Zhu, Y.; Garg, A.; Fei-Fei, L.; and Savarese, S. 2017 · 2017
Cited alongside, same era.
Robust Adversarial Reinforcement Learning
Pinto, L.; Davidson, J.; Sukthankar, R.; and Gupta, A. 2017 · 2017
Cited alongside, same era.
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R.; Isola, P.; Efros, A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
Vinyals, O.; Babuschkin, I.; Czarnecki, W. M.; Mathieu, M.; Dudzik, A.; Chung, J.; Choi, D. H.; Powell, R.; Ewalds, T.; Georgiev, P.; Oh, J.; Horgan, D.; Kroiss, M.; Danihelka, I.; Huang, A.; Sifre, L.; Cai, T.; Agapiou, J. P.; Jaderberg, M.; Vezhnevets, A. S.; Leblond, R.; Pohlen, T.; Dalibard, V.; Budden, D.; Sulsky, Y.; Molloy, J.; Paine, T. L.; Gülçehre, Ç.; Wang, Z.; Pfaff, T.; Wu, Y.; Ring, R.; Yogatama, D.; Wünsch, D.; McKinney, K.; Smith, O.; Schaul, T.; Lillicrap, T. P.; Kavukcuoglu, K.; Hassabis, D.; Apps, C.; and Silver, D. 2019 · 2019
Later among the works it cites.
Adversarial Policies: Attacking Deep Reinforcement Learning
Gleave, A.; Dennis, M.; Wild, C.; Neel, K.; Levine, S.; and Russell, S. 2020 · 2020
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Robust Deep Renforcement Learning against Adversaral Perturbations on State Observations
Huan, Z.; Chen, H.; Xiao, C.; Li, B.; Liu, M.; Boning, D. S.; and Hseh, C. 2020 · 2020
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Proximal policy optimization algorithms
Schulman, J.; Wolski, F.; Dhariwal, P.; Radford, A.; and Klimov, O. 2017 · 2017
Cited alongside, same era.
Tactics of Advrsarial Attack on Deep Reinforcement Learning Agnts
Yen-Chen, L.; Zhag-Wei, H.; Lao, Y.-H.; Shih, M.-L.; ing Yu Lu; and Sun, M. 2017 · 2017
Cited alongside, same era.
Towards Deep Learning Models Resistant to Adversarial Attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2018 · 2018
Cited alongside, same era.
Ensemble Adversarial Training: Attacks and Defenses
Tramèr, F.; Kurakin, A.; Papernot, N.; Goodfellow, I. J.; Boneh, D.; and McDaniel, P. D. 2018 · 2018
Cited alongside, same era.
Inaccuracy of State-Action Value Function for Non-Optimal Actions in Adversarially Trained Deep Neural Policies
Korkmaz, E. 2021a
Cited in the paper.
Investigating Vulnerabilities of Deep Neural Policies
Korkmaz, E. 2021b
Cited in the paper.
Nesterov Momentum Adversarial Perturbations in the Deep Reinforcement Learning Domain
Korkmaz, E. 2020 · 2020
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Mastering Atari, Go, chess and shogi by planning with a learned model
Schrittwieser, J.; Antonoglou, I.; Hubert, T.; Simonyan, K.; Sifre, L.; Schmitt, S.; Guez, A.; Lockhart, E.; Hassabis, D.; Graepel, T.; Lillicrap, T. P.; and Silver, D. 2020 · 2020
Later among the works it cites.
Stealthy and efficient advrsarial attacks aganst deep reinforcement learning
Sun, J.; Zhang, T.; Xiafei, L.; Ma, X.; Zheng, Y.; Chen, K.; and Liu, Y. 2020 · 2020
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Intriguing Properties of Adversarial Training at Scale
Xie, C.; and Yuille, A. L. 2020 · 2020
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Robust Deep Reinforcement Learning with Adversarial Attacks
Pattanaik, A.; Tang, Z.; Liu, S.; and Gautham, B. 2018 · 2042
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