John Schulman

Alex Nichol, Joshua Achiam, John Schulman:
On First-Order Meta-Learning Algorithms. CoRR abs/1803.02999 (2018)

John Schulman, Pieter Abbeel, Xi Chen:
Equivalence Between Policy Gradients and Soft Q-Learning. CoRR abs/1704.06440 (2017)

Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman:
UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles. CoRR abs/1706.01502 (2017)

Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman:
Teacher-Student Curriculum Learning. CoRR abs/1707.00183 (2017)

John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov:
Proximal Policy Optimization Algorithms. CoRR abs/1707.06347 (2017)

Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine:
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR abs/1709.10087 (2017)

Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman:
Meta Learning Shared Hierarchies. CoRR abs/1710.09767 (2017)

Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel:
Benchmarking Deep Reinforcement Learning for Continuous Control. CoRR abs/1604.06778 (2016)

Rami Al-Rfou', Guillaume Alain, Amjad Almahairi, Christof Angermüller, 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 F. Christiano, Tim Cooijmans, Marc-Alexandre Côté, Myriam Côté, Aaron C. Courville, Yann N. Dauphin, Olivier Delalleau, Julien Demouth, Guillaume Desjardins, Sander Dieleman, Laurent Dinh, Melanie Ducoffe, Vincent Dumoulin, Samira Ebrahimi Kahou, Dumitru Erhan, Ziye Fan, Orhan Firat, Mathieu Germain, Xavier Glorot, Ian J. Goodfellow, Matthew Graham, Çaglar Gülçehre, 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 Lefrançois, Simon Lemieux, Nicholas Léonard, Zhouhan Lin, Jesse A. Livezey, Cory Lorenz, Jeremiah Lowin, Qianli Ma, Pierre-Antoine Manzagol, Olivier Mastropietro, Robert McGibbon, Roland Memisevic, Bart van Merriënboer, Vincent Michalski, Mehdi Mirza, Alberto Orlandi, Christopher Joseph 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 P. 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:
Theano: A Python framework for fast computation of mathematical expressions. CoRR abs/1605.02688 (2016)

Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks. CoRR abs/1605.09674 (2016)

Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba:
OpenAI Gym. CoRR abs/1606.01540 (2016)

Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel:
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR abs/1606.03657 (2016)

Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, Dan Mané:
Concrete Problems in AI Safety. CoRR abs/1606.06565 (2016)

Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel:
Variational Lossy Autoencoder. CoRR abs/1611.02731 (2016)

Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel:
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR abs/1611.02779 (2016)

Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel:
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning. CoRR abs/1611.04717 (2016)

John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel:
Trust Region Policy Optimization. CoRR abs/1502.05477 (2015)

John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel:
High-Dimensional Continuous Control Using Generalized Advantage Estimation. CoRR abs/1506.02438 (2015)

John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel:
Gradient Estimation Using Stochastic Computation Graphs. CoRR abs/1506.05254 (2015)