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Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark


Mar 29, 2021
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Gražvydas Šemetulskis, João Schapke, Jonas Kubilius, Jurgis Pašukonis, Linas Klimas, Matthew Hausknecht, Patrick MacAlpine, Quang Nhat Tran, Thomas Tumiel, Xiaocheng Tang, Xinwei Chen, Christopher Hesse, Jacob Hilton, William Hebgen Guss, Sahika Genc, John Schulman, Karl Cobbe


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The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors


Jan 26, 2021
William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals

* 37 pages, initial submission, accepted at NeurIPS. arXiv admin note: substantial text overlap with arXiv:1904.10079 

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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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