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Supporting state-of-the-art AI research requires balancing rapid prototyping, ease of use, and quick iteration, with the ability to deploy experiments at a scale traditionally associated with production systems.Deep learning frameworks such as TensorFlow, PyTorch and JAX allow users to transparently make use of accelerators, such as TPUs and GPUs, to offload the more computationally intensive parts of training and inference in modern deep learning systems.
Analytical differentiation on a digital computer
Nolan · 1953
Earlier work this paper cites.
Recursive functions of symbolic expressions and their computation by machine, part i
J. McCarthy · 1960
Earlier work this paper cites.
Steps toward artificial intelligence
M. Minsky · 1961
Earlier work this paper cites.
A simple automatic derivative evaluation program
R. E. Wengert · 1964
Earlier work this paper cites.
Some computer organizations and their effectiveness
M. J. Flynn · 1972
Earlier work this paper cites.
A micro controlled peripheral processor
E. T. Barron and R. M. Glorioso · 1973
Earlier work this paper cites.
Compiling fast partial derivatives of functions given by algorithms
B. Speelpenning · 1980
Earlier work this paper cites.
Proving program refinements and transformations
M. Ward · 1989
Earlier work this paper cites.
Pattern Recognition and Machine Learning (Information Science and Statistics)
C. M. Bishop · 2006
Earlier work this paper cites.
Inside the python gil
D. Beazley · 2009
Earlier work this paper cites.
Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, M. Mao, M. a. Ranzato, A. Senior, P. Tucker, K. Yang, Q. Le, and A. Ng · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Machine Learning: A Probabilistic Perspective
K. P. Murphy · 2012
Cited alongside, same era.
Reinforcement learning in continuous state and action spaces
H. Van Hasselt · 2012
Cited alongside, same era.
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
Cited alongside, same era.
Massively parallel methods for deep reinforcement learning
A. Nair, P. Srinivasan, S. Blackwell, C. Alcicek, R. Fearon, A. D. Maria, V. Panneershelvam, M. Suleyman, C. Beattie, S. Petersen, S. Legg, V. Mnih, K. Kavukcuoglu, and D. Silver · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
Cited alongside, same era.
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. G. Azar, and D. Silver · 2018
Later among the works it cites.
Distributed prioritized experience replay
D. Horgan, J. Quan, D. Budden, G. Barth-Maron, M. Hessel, H. van Hasselt, and D. Silver · 2018
Later among the works it cites.
RLlib: Abstractions for distributed reinforcement learning
E. Liang, R. Liaw, R. Nishihara, P. Moritz, R. Fox, K. Goldberg, J. Gonzalez, M. Jordan, and I. Stoica · 2018
Later among the works it cites.
Openai rapid
M. Petrov, S. Sidor, S. Zhang, J. Pachocki, P. D˛ebiak, F. Wolski, C. Dennison, H. Pondé, G. Brockman, J. Tang, D. Farhi, B. Chan, and J. Raiman · 2018
Later among the works it cites.
Accelerated methods for deep reinforcement learning
A. Stooke and P. Abbeel · 2018
Later among the works it cites.
Reinforcement learning: An introduction
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
Cited alongside, same era.
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
Cited alongside, same era.
In-datacenter performance analysis of a tensor processing unit
N. P. Jouppi, C. Young, N. Patil, D. Patterson, G. Agrawal, R. Bajwa, S. Bates, S. Bhatia, N. Boden, A. Borchers, R. Boyle, P. luc Cantin, C. Chao, C. Clark, J. Coriell, M. Daley, M. Dau, J. Dean, B. Gelb, T. V. Ghaemmaghami, R. Gottipati, W. Gulland, R. Hagmann, C. R. Ho, D. Hogberg, J. Hu, R. Hundt, D. Hurt, J. Ibarz, A. Jaffey, A. Jaworski, A. Kaplan, H. Khaitan, D. Killebrew, A. Koch, N. Kumar, S. Lacy, J. Laudon, J. Law, D. Le, C. Leary, Z. Liu, K. Lucke, A. Lundin, G. MacKean, A. Maggiore, M. Mahony, K. Miller, R. Nagarajan, R. Narayanaswami, R. Ni, K. Nix, T. Norrie, M. Omernick, N. Penukonda, A. Phelps, J. Ross, M. Ross, A. Salek, E. Samadiani, C. Severn, G. Sizikov, M. Snelham, J. Souter, D. Steinberg, A. Swing, M. Tan, G. Thorson, B. Tian, H. Toma, E. Tuttle, V. Vasudevan, R. Walter, W. Wang, E. Wilcox, and D. H. Yoon · 2017
Cited alongside, same era.
Mastering chess and shogi by self-play with a general reinforcement learning algorithm
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, T. P. Lillicrap, K. Simonyan, and D. Hassabis · 2017
Cited alongside, same era.
Xla — tensorflow, compiled
X. team · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs
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
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, S. Legg, and K. Kavukcuoglu · 2018
Cited alongside, same era.
R. S. Sutton and A. G. Barto · 2018
Later among the works it cites.
Seed rl: Scalable and efficient deep-rl with accelerated central inference
L. Espeholt, R. Marinier, P. Stanczyk, K. Wang, and M. Michalski · 2020
Later among the works it cites.
Array programming with NumPy
C. R. Harris, K. J. Millman, S. J. van der Walt, R. Gommers, P. Virtanen, D. Cournapeau, E. Wieser, J. Taylor, S. Berg, N. J. Smith, R. Kern, M. Picus, S. Hoyer, M. H. van Kerkwijk, M. Brett, A. Haldane, J. F. del R’ıo, M. Wiebe, P. Peterson, P. G’erard-Marchant, K. Sheppard, T. Reddy, W. Weckesser, H. Abbasi, C. Gohlke, and T. E. Oliphant · 2020
Later among the works it cites.
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, T. Lillicrap, and D. Silver · 2020
Later among the works it cites.
Meta-gradient reinforcement learning with an objective discovered online, 2020
Z. Xu, H. van Hasselt, M. Hessel, J. Oh, S. Singh, and D. Silver · 2020
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A self-tuning actor-critic algorithm, 2020
T. Zahavy, Z. Xu, V. Veeriah, M. Hessel, J. Oh, H. van Hasselt, D. Silver, and S. Singh · 2020
Later among the works it cites.
Discovering reinforcement learning algorithms, 2021
J. Oh, M. Hessel, W. M. Czarnecki, Z. Xu, H. van Hasselt, S. Singh, and D. Silver · 2021
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