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Neural network architectures have a large impact in machine learning.
Mastering atari, go, chess and shogi by planning with a learned model, 2019
J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, T. Lillicrap, and D. Silver · 1911
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Training very deep networks
R. K. Srivastava, K. Greff, and J. Schmidhuber · 2015
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J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
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Deep Residual Learning for Image Recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Gaussian Error Linear Units (GELUs)
D. Hendrycks and K. Gimpel · 2016
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Deep reinforcement learning with double q-learning
H. Van Hasselt, A. Guez, and D. Silver · 2016
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Dueling network architectures for deep reinforcement learning
Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas · 2016
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A distributional perspective on reinforcement learning
M. G. Bellemare, W. Dabney, and R. Munos · 2017
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Noisy networks for exploration
M. Fortunato, M. G. Azar, B. Piot, J. Menick, I. Osband, A. Graves, V. Mnih, R. Munos, D. Hassabis, O. Pietquin, et al · 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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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang · 2018
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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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An introduction to deep reinforcement learning
V. François-Lavet, P. Henderson, R. Islam, M. G. Bellemare, J. Pineau, et al · 2018
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Soft actor-critic algorithms and applications
T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel, et al · 2018
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Rainbow: Combining improvements in deep reinforcement learning
M. Hessel, J. Modayil, H. Van Hasselt, T. Schaul, G. Ostrovski, W. Dabney, D. Horgan, B. Piot, M. Azar, and D. Silver · 2018
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Recurrent experience replay in distributed reinforcement learning
S. Kapturowski, G. Ostrovski, J. Quan, R. Munos, and W. Dabney · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
Cited alongside, same era.
Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto · 2018
Cited alongside, same era.
A. Bhatt, D. Palenicek, B. Belousov, M. Argus, A. Amiranashvili, T. Brox, and J. Peters · 2019
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2019
Cited alongside, same era.
Learning latent dynamics for planning from pixels
D. Hafner, T. Lillicrap, I. Fischer, R. Villegas, D. Ha, H. Lee, and J. Davidson · 2019
Cited alongside, same era.
EnvPool: A highly parallel reinforcement learning environment execution engine
J. Weng, M. Lin, S. Huang, B. Liu, D. Makoviichuk, V. Makoviychuk, Z. Liu, Y. Song, T. Luo, Y. Jiang, Z. Xu, and S. Yan · 2022
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Atari-5: Distilling the arcade learning environment down to five games
M. Aitchison, P. Sweetser, and M. Hutter · 2023
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Mastering diverse domains through world models
D. Hafner, J. Pasukonis, J. Ba, and T. Lillicrap · 2023
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Jaxmarl: Multi-agent rl environments in jax
A. Rutherford, B. Ellis, M. Gallici, J. Cook, A. Lupu, G. Ingvarsson, T. Willi, A. Khan, C. S. de Witt, A. Souly, S. Bandyopadhyay, M. Samvelyan, M. Jiang, R. T. Lange, S. Whiteson, B. Lacerda, N. Hawes, T. Rocktaschel, C. Lu, and J. N. Foerster · 2023
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Bigger, better, faster: Human-level atari with human-level efficiency
M. Schwarzer, J. S. O. Ceron, A. Courville, M. G. Bellemare, R. Agarwal, and P. S. Castro · 2023
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D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba · 2020
Cited alongside, same era.
Mastering Atari with Discrete World Models
D. Hafner, T. Lillicrap, M. Norouzi, and J. Ba · 2020
Cited alongside, same era.
Curl: Contrastive unsupervised representations for reinforcement learning
M. Laskin, A. Srinivas, and P. Abbeel · 2020
Cited alongside, same era.
Mastering atari, go, chess and shogi by planning with a learned model
J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, et al · 2020
Cited alongside, same era.
Data-efficient reinforcement learning with self-predictive representations
M. Schwarzer, A. Anand, R. Goel, R. D. Hjelm, A. Courville, and P. Bachman · 2020
Cited alongside, same era.
Randomized ensembled double q-learning: Learning fast without a model
X. Chen, C. Wang, Z. Zhou, and K. Ross · 2021
Cited alongside, same era.
Brax - a differentiable physics engine for large scale rigid body simulation, 2021
C. D. Freeman, E. Frey, A. Raichuk, S. Girgin, I. Mordatch, and O. Bachem · 2021
Cited alongside, same era.
Later among the works it cites.
The dormant neuron phenomenon in deep reinforcement learning
G. Sokar, R. Agarwal, P. S. Castro, and U. Evci · 2023
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Storm: Efficient stochastic transformer based world models for reinforcement learning
W. Zhang, G. Wang, J. Sun, Y. Yuan, and G. Huang · 2023
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Learning to play atari in a world of tokens
P. Agarwal, S. Andrews, and S. E. Kahou · 2024
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Diffusion for world modeling: Visual details matter in atari
E. Alonso, A. Jelley, V. Micheli, A. Kanervisto, A. J. Storkey, T. Pearce, and F. Fleuret · 2024
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Jumanji: a diverse suite of scalable reinforcement learning environments in jax, 2024
C. Bonnet, D. Luo, D. Byrne, S. Surana, S. Abramowitz, P. Duckworth, V. Coyette, L. I. Midgley, E. Tegegn, T. Kalloniatis, O. Mahjoub, M. Macfarlane, A. P. Smit, N. Grinsztajn, R. Boige, C. N. Waters, M. A. Mimouni, U. A. M. Sob, R. de Kock, S. Singh, D. Furelos-Blanco, V. Le, A. Pretorius, and A. Laterre · 2024
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Simplifying deep temporal difference learning
M. Gallici, M. Fellows, B. Ellis, B. Pou, I. Masmitja, J. N. Foerster, and M. Martin · 2024
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Hadamard representations: Augmenting hyperbolic tangents in rl, 2024
J. E. Kooi, M. Hoogendoorn, and V. François-Lavet · 2024
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Simba: Simplicity bias for scaling up parameters in deep reinforcement learning
H. Lee, D. Hwang, D. Kim, H. Kim, J. J. Tai, K. Subramanian, P. R. Wurman, J. Choo, P. Stone, and T. Seno · 2024
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Bigger, regularized, optimistic: scaling for compute and sample-efficient continuous control
M. Nauman, M. Ostaszewski, K. Jankowski, P. Miłoś, and M. Cygan · 2024
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Bridging state and history representations: Understanding self-predictive rl
T. Ni, B. Eysenbach, E. Seyedsalehi, M. Ma, C. Gehring, A. Mahajan, and P.-L. Bacon · 2024
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In deep reinforcement learning, a pruned network is a good network
J. Obando-Ceron, A. Courville, and P. S. Castro · 2024
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Mixtures of experts unlock parameter scaling for deep rl
J. Obando-Ceron, G. Sokar, T. Willi, C. Lyle, J. Farebrother, J. Foerster, G. K. Dziugaite, D. Precup, and P. S. Castro · 2024
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Efficientzero v2: Mastering discrete and continuous control with limited data
S. Wang, S. Liu, W. Ye, J. You, and Y. Gao · 2024
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Hadamard product in deep learning: Introduction, advances and challenges
G. G. Chrysos, Y. Wu, R. Pascanu, P. Torr, and V. Cevher · 2025
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