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Much of recent Deep Reinforcement Learning success is owed to the neural architecture's potential to learn and use effective internal representations of the world.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N.; Hinton, G.; Krizhevsky, A.; Sutskever, I.; and Salakhutdinov, R. 2014 · 1958
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Reinforcement learning with augmented data
Laskin, M.; Lee, K.; Stooke, A.; Pinto, L.; Abbeel, P.; and Srinivas, A. 2020 · 2004
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Rethinking positional encoding in language pre-training
Ke, G.; He, D.; and Liu, T.-Y. 2020 · 2006
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Linformer: Self-attention with linear complexity
Wang, S.; Li, B. Z.; Khabsa, M.; Fang, H.; and Ma, H. 2020 · 2006
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An image is worth 16x16 words: Transformers for image recognition at scale
Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. 2020 · 2010
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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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Playing atari with deep reinforcement learning
Mnih, V.; Kavukcuoglu, K.; Silver, D.; Graves, A.; Antonoglou, I.; Wierstra, D.; and Riedmiller, M. 2013 · 2013
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Recurrent models of visual attention
Mnih, V.; Heess, N.; Graves, A.; et al. 2014 · 2014
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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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Deep attention recurrent Q-network
Sorokin, I.; Seleznev, A.; Pavlov, M.; Fedorov, A.; and Ignateva, A. 2015 · 2015
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On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
Keskar, N. S.; Mudigere, D.; Nocedal, J.; Smelyanskiy, M.; and Tang, P. T. P. 2017 · 2017
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Attention is all you need
Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, Ł.; and Polosukhin, I. 2017 · 2017
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A visual attention operator for playing Pac-Man
Gregor, M.; Nemec, D.; Janota, A.; and Pirník, R. 2018 · 2018
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Revisiting small batch training for deep neural networks
Masters, D.; and Luschi, C. 2018 · 2018
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Experiment Tracking with Weights and Biases
Biewald, L. 2020 · 2020
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Revisiting spatial invariance with low-rank local connectivity
Elsayed, G.; Ramachandran, P.; Shlens, J.; and Kornblith, S. 2020 · 2020
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Stabilizing transformers for reinforcement learning
Parisotto, E.; Song, F.; Rae, J.; Pascanu, R.; Gulcehre, C.; Jayakumar, S.; Jaderberg, M.; Kaufman, R. L.; Clark, A.; Noury, S.; et al. 2020 · 2020
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Decision transformer: Reinforcement learning via sequence modeling
Chen, L.; Lu, K.; Rajeswaran, A.; Lee, K.; Grover, A.; Laskin, M.; Abbeel, P.; Srinivas, A.; and Mordatch, I. 2021 · 2021
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Stable, fast and accurate: Kernelized attention with relative positional encoding
Luo, S.; Li, S.; Cai, T.; He, D.; Peng, D.; Zheng, S.; Ke, G.; Wang, L.; and Liu, T.-Y. 2021 · 2021
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Image transformer
Parmar, N.; Vaswani, A.; Uszkoreit, J.; Kaiser, L.; Shazeer, N.; Ku, A.; and Tran, D. 2018 · 2018
Cited alongside, same era.
An initial attempt of combining visual selective attention with deep reinforcement learning
Yuezhang, L.; Zhang, R.; and Ballard, D. H. 2018 · 2018
Cited alongside, same era.
Deep reinforcement learning with relational inductive biases
Zambaldi, V.; Raposo, D.; Santoro, A.; Bapst, V.; Li, Y.; Babuschkin, I.; Tuyls, K.; Reichert, D.; Lillicrap, T.; Lockhart, E.; et al. 2018 · 2018
Cited alongside, same era.
Efficient transformers in reinforcement learning using actor-learner distillation
Parisotto, E.; and Salakhutdinov, R. 2021 · 2021
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Do Vision Transformers See Like Convolutional Neural Networks?
Raghu, M.; Unterthiner, T.; Kornblith, S.; Zhang, C.; and Dosovitskiy, A. 2021 · 2021
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Decoupling representation learning from reinforcement learning
Stooke, A.; Lee, K.; Abbeel, P.; and Laskin, M. 2021 · 2021
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