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The generalization gap in reinforcement learning (RL) has been a significant obstacle that prevents the RL agent from learning general skills and adapting to varying environments.
Using confidence bounds for exploitation-exploration trade-offs
P. Auer · 2002
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Model-free reinforcement learning with continuous action in practice
T. Degris, P. M. Pilarski, and R. S. Sutton · 2012
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2013
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Explaining and harnessing adversarial examples
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2014
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Mastering the game of go with deep neural networks and tree search
D. Silver, A. Huang, C. J. Maddison, A. Guez, L. Sifre, G. Van Den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, et al · 2016
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Mastering the game of go without human knowledge
D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, et al · 2017
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Starcraft ii: A new challenge for reinforcement learning
O. Vinyals, T. Ewalds, S. Bartunov, P. Georgiev, A. S. Vezhnevets, M. Yeo, A. Makhzani, H. Küttler, J. Agapiou, J. Schrittwieser, et al · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Tactics of adversarial attack on deep reinforcement learning agents
Y.-C. Lin, Z.-W. Hong, Y.-H. Liao, M.-L. Shih, M.-Y. Liu, and M. Sun · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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A closer look at memorization in deep networks
D. Arpit, S. Jastrzebski, N. Ballas, D. Krueger, E. Bengio, M. S. Kanwal, T. Maharaj, A. Fischer, A. Courville, Y. Bengio, et al · 2017
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mixup: Beyond empirical risk minimization
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz · 2017
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Towards deep learning models resistant to adversarial attacks
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu · 2017
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Certifying some distributional robustness with principled adversarial training
A. Sinha, H. Namkoong, R. Volpi, and J. Duchi · 2017
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Minimax statistical learning with wasserstein distances
J. Lee and M. Raginsky · 2017
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Threat of adversarial attacks on deep learning in computer vision: A survey
N. Akhtar and A. Mian · 2018
Network randomization: A simple technique for generalization in deep reinforcement learning
K. Lee, K. Lee, J. Shin, and H. Lee · 2019
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Adversarial policies: Attacking deep reinforcement learning
A. Gleave, M. Dennis, C. Wild, N. Kant, S. Levine, and S. Russell · 2019
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Adversarial examples: Attacks and defenses for deep learning
X. Yuan, P. He, Q. Zhu, and X. Li · 2019
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Leveraging procedural generation to benchmark reinforcement learning
K. Cobbe, C. Hesse, J. Hilton, and J. Schulman · 2020
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Reinforcement learning with augmented data
M. Laskin, K. Lee, A. Stooke, L. Pinto, P. Abbeel, and A. Srinivas · 2020
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Cited alongside, same era.
Generalizing to unseen domains via adversarial data augmentation
R. Volpi, H. Namkoong, O. Sener, J. Duchi, V. Murino, and S. Savarese · 2018
Cited alongside, same era.
Impala: Scalable distributed deep-rl with importance weighted actor-learner architectures
L. Espeholt, H. Soyer, R. Munos, K. Simonyan, V. Mnih, T. Ward, Y. Doron, V. Firoiu, T. Harley, I. Dunning, et al · 2018
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Dota 2 with large scale deep reinforcement learning
C. Berner, G. Brockman, B. Chan, V. Cheung, P. Debiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, et al · 2019
Cited alongside, same era.
Observational overfitting in reinforcement learning
X. Song, Y. Jiang, S. Tu, Y. Du, and B. Neyshabur · 2019
Cited alongside, same era.
Quantifying generalization in reinforcement learning
K. Cobbe, O. Klimov, C. Hesse, T. Kim, and J. Schulman · 2019
Cited alongside, same era.
A survey on image data augmentation for deep learning
C. Shorten and T. M. Khoshgoftaar · 2019
Cited alongside, same era.
A study on overfitting in deep reinforcement learning
C. Zhang, O. Vinyals, R. Munos, and S. Bengio
Cited in the paper.
Automatic data augmentation for generalization in reinforcement learning
R. Raileanu, M. Goldstein, D. Yarats, I. Kostrikov, and R. Fergus · 2020
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Improving generalization in reinforcement learning with mixture regularization
K. Wang, B. Kang, J. Shao, and J. Feng · 2020
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Blackbox attacks on reinforcement learning agents using approximated temporal information
Y. Zhao, I. Shumailov, H. Cui, X. Gao, R. Mullins, and R. Anderson · 2020
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Prioritized level replay
M. Jiang, E. Grefenstette, and T. Rocktäschel · 2021
Closest in time.
Decoupling value and policy for generalization in reinforcement learning
R. Raileanu and R. Fergus · 2021
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P. J. Ball, C. Lu, J. Parker-Holder, and S. Roberts · 2021
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