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Scaling Laws for Autoregressive Generative Modeling

Nov 06, 2020
Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, Sam McCandlish

* 20+17 pages, 33 figures; added appendix with additional language results 

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Phasic Policy Gradient

Sep 09, 2020
Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman


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Leveraging Procedural Generation to Benchmark Reinforcement Learning

Dec 03, 2019
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman


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Policy Gradient Search: Online Planning and Expert Iteration without Search Trees

Apr 07, 2019
Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman


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Semi-Supervised Learning by Label Gradient Alignment

Feb 06, 2019
Jacob Jackson, John Schulman


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Quantifying Generalization in Reinforcement Learning

Dec 20, 2018
Karl Cobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman


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On First-Order Meta-Learning Algorithms

Oct 22, 2018
Alex Nichol, Joshua Achiam, John Schulman


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High-Dimensional Continuous Control Using Generalized Advantage Estimation

Oct 20, 2018
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, Pieter Abbeel


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Equivalence Between Policy Gradients and Soft Q-Learning

Oct 14, 2018
John Schulman, Xi Chen, Pieter Abbeel


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Model-Based Reinforcement Learning via Meta-Policy Optimization

Sep 14, 2018
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel

* First 2 authors contributed equally. Accepted for Conference on Robot Learning (CoRL) 

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Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations

Jun 26, 2018
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine

* Accepted for presentation at Robotics: Science and Systems (RSS) 2018. Project page: https://sites.google.com/view/deeprl-dexterous-manipulation 

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Gotta Learn Fast: A New Benchmark for Generalization in RL

Apr 23, 2018
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman


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#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning

Dec 05, 2017
Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

* 10 pages main text + 10 pages supplementary. Published at NIPS 2017 

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Teacher-Student Curriculum Learning

Nov 29, 2017
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman


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UCB Exploration via Q-Ensembles

Nov 07, 2017
Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman


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Meta Learning Shared Hierarchies

Oct 26, 2017
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman


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Proximal Policy Optimization Algorithms

Aug 28, 2017
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov


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Trust Region Policy Optimization

Apr 20, 2017
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel

* 16 pages, ICML 2015 

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Variational Lossy Autoencoder

Mar 04, 2017
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel

* Added CIFAR10 experiments; ICLR 2017 

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VIME: Variational Information Maximizing Exploration

Jan 27, 2017
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel

* Published in Advances in Neural Information Processing Systems 29 (NIPS), pages 1109-1117 

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RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning

Nov 10, 2016
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel

* 14 pages. Under review as a conference paper at ICLR 2017 

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Concrete Problems in AI Safety

Jul 25, 2016
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané

* 29 pages 

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InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets

Jun 12, 2016
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel


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OpenAI Gym

Jun 05, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba


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Benchmarking Deep Reinforcement Learning for Continuous Control

May 27, 2016
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel

* 14 pages, ICML 2016 

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Theano: A Python framework for fast computation of mathematical expressions

May 09, 2016
The Theano Development Team, Rami Al-Rfou, Guillaume Alain, Amjad Almahairi, Christof Angermueller, Dzmitry Bahdanau, Nicolas Ballas, Frédéric Bastien, Justin Bayer, Anatoly Belikov, Alexander Belopolsky, Yoshua Bengio, Arnaud Bergeron, James Bergstra, Valentin Bisson, Josh Bleecher Snyder, Nicolas Bouchard, Nicolas Boulanger-Lewandowski, Xavier Bouthillier, Alexandre de Brébisson, Olivier Breuleux, Pierre-Luc Carrier, Kyunghyun Cho, Jan Chorowski, Paul Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Mélanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian Goodfellow, Matt Graham, Caglar Gulcehre, Philippe Hamel, Iban Harlouchet, Jean-Philippe Heng, Balázs Hidasi, Sina Honari, Arjun Jain, Sébastien Jean, Kai Jia, Mikhail Korobov, Vivek Kulkarni, Alex Lamb, Pascal Lamblin, Eric Larsen, César Laurent, Sean Lee, Simon Lefrancois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert T. McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Pal, Razvan Pascanu, Mohammad Pezeshki, Colin Raffel, Daniel Renshaw, Matthew Rocklin, Adriana Romero, Markus Roth, Peter Sadowski, John Salvatier, François Savard, Jan Schlüter, John Schulman, Gabriel Schwartz, Iulian Vlad Serban, Dmitriy Serdyuk, Samira Shabanian, Étienne Simon, Sigurd Spieckermann, S. Ramana Subramanyam, Jakub Sygnowski, Jérémie Tanguay, Gijs van Tulder, Joseph Turian, Sebastian Urban, Pascal Vincent, Francesco Visin, Harm de Vries, David Warde-Farley, Dustin J. Webb, Matthew Willson, Kelvin Xu, Lijun Xue, Li Yao, Saizheng Zhang, Ying Zhang

* 19 pages, 5 figures 

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Gradient Estimation Using Stochastic Computation Graphs

Jan 05, 2016
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel

* Advances in Neural Information Processing Systems 28 (NIPS 2015) 

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