John Schulman

Publication Activity (10 Years)

Years Active: 2013-2024
Publications (10 Years): 54

Top Topics

Reinforcement Learning

Top Venues

Publications

Hunter Lightman, Vineet Kosaraju, Yuri Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Let's Verify Step by Step. ICLR (2024)
Hunter Lightman, Vineet Kosaraju, Yura Burda, Harrison Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, Karl Cobbe
Let's Verify Step by Step. CoRR (2023)
Leo Gao, John Schulman, Jacob Hilton
Scaling Laws for Reward Model Overoptimization. ICML (2023)
Jacob Hilton, Jie Tang, John Schulman
Scaling laws for single-agent reinforcement learning. CoRR (2023)
Leo Gao, John Schulman, Jacob Hilton
Scaling Laws for Reward Model Overoptimization. CoRR (2022)
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe
Training language models to follow instructions with human feedback. NeurIPS (2022)
Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, John Schulman, Christine McLeavey, Jerry Tworek, Mark Chen
Efficient Training of Language Models to Fill in the Middle. CoRR (2022)
Jacob Hilton, Karl Cobbe, John Schulman
Batch size-invariance for policy optimization. NeurIPS (2022)
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F. Christiano, Jan Leike, Ryan Lowe
Training language models to follow instructions with human feedback. CoRR (2022)
William H. Guss, Mario Ynocente Castro, Sam Devlin, Brandon Houghton, Noboru Sean Kuno, Crissman Loomis, Stephanie Milani, Sharada P. Mohanty, Keisuke Nakata, Ruslan Salakhutdinov, John Schulman, Shinya Shiroshita, Nicholay Topin, Avinash Ummadisingu, Oriol Vinyals
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors. CoRR (2021)
Sharada P. Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Grazvydas Semetulskis, João Schapke, Jonas Kubilius, Jurgis Pasukonis, Linas Klimas, Matthew J. 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
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. CoRR (2021)
Jacob Hilton, Karl Cobbe, John Schulman
Batch size-invariance for policy optimization. CoRR (2021)
Dan Hendrycks, Nicholas Carlini, John Schulman, Jacob Steinhardt
Unsolved Problems in ML Safety. CoRR (2021)
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, John Schulman
Training Verifiers to Solve Math Word Problems. CoRR (2021)
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, John Schulman
WebGPT: Browser-assisted question-answering with human feedback. CoRR (2021)
Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
Phasic Policy Gradient. ICML (2021)
Sharada P. Mohanty, Jyotish Poonganam, Adrien Gaidon, Andrey Kolobov, Blake Wulfe, Dipam Chakraborty, Grazvydas Semetulskis, João Schapke, Jonas Kubilius, Jurgis Pasukonis, Linas Klimas, Matthew J. 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
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark. NeurIPS (Competition and Demos) (2020)
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
Leveraging Procedural Generation to Benchmark Reinforcement Learning. ICML (2020)
Heewoo Jun, Rewon Child, Mark Chen, John Schulman, Aditya Ramesh, Alec Radford, Ilya Sutskever
Distribution Augmentation for Generative Modeling. ICML (2020)
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
Teacher-Student Curriculum Learning. IEEE Trans. Neural Networks Learn. Syst. 31 (9) (2020)
Karl Cobbe, Jacob Hilton, Oleg Klimov, John Schulman
Phasic Policy Gradient. CoRR (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
Scaling Laws for Autoregressive Generative Modeling. CoRR (2020)
Thomas Anthony, Robert Nishihara, Philipp Moritz, Tim Salimans, John Schulman
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees. CoRR (2019)
Jacob Jackson, John Schulman
Semi-Supervised Learning by Label Gradient Alignment. CoRR (2019)
Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman
Quantifying Generalization in Reinforcement Learning. ICML (2019)
Karl Cobbe, Christopher Hesse, Jacob Hilton, John Schulman
Leveraging Procedural Generation to Benchmark Reinforcement Learning. CoRR (2019)
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman
Gotta Learn Fast: A New Benchmark for Generalization in RL. CoRR (2018)
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
Meta Learning Shared Hierarchies. ICLR (Poster) (2018)
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRR (2018)
Karl Cobbe, Oleg Klimov, Christopher Hesse, Taehoon Kim, John Schulman
Quantifying Generalization in Reinforcement Learning. CoRR (2018)
Alex Nichol, Joshua Achiam, John Schulman
On First-Order Meta-Learning Algorithms. CoRR (2018)
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
Model-Based Reinforcement Learning via Meta-Policy Optimization. CoRL (2018)
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov, Sergey Levine
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. Robotics: Science and Systems (2018)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. ICLR (Poster) (2017)
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
Meta Learning Shared Hierarchies. CoRR (2017)
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. NIPS (2017)
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
Teacher-Student Curriculum Learning. CoRR (2017)
Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, John Schulman, Emanuel Todorov, Sergey Levine
Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations. CoRR (2017)
Richard Y. Chen, Szymon Sidor, Pieter Abbeel, John Schulman
UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles. CoRR (2017)
John Schulman, Pieter Abbeel, Xi Chen
Equivalence Between Policy Gradients and Soft Q-Learning. CoRR (2017)
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
Proximal Policy Optimization Algorithms. CoRR (2017)
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 (2016)
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. NIPS (2016)
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba
OpenAI Gym. CoRR (2016)
John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
High-Dimensional Continuous Control Using Generalized Advantage Estimation. ICLR (Poster) (2016)
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
Benchmarking Deep Reinforcement Learning for Continuous Control. ICML (2016)
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel
Benchmarking Deep Reinforcement Learning for Continuous Control. CoRR (2016)
Rein Houthooft, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
VIME: Variational Information Maximizing Exploration. NIPS (2016)
Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets. CoRR (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 (2016)
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, Dan Mané
Concrete Problems in AI Safety. CoRR (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 (2016)
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever, Pieter Abbeel
RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning. CoRR (2016)
Xi Chen, Diederik P. Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
Variational Lossy Autoencoder. CoRR (2016)
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Gradient Estimation Using Stochastic Computation Graphs. CoRR (2015)
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
Trust Region Policy Optimization. CoRR (2015)
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
Gradient Estimation Using Stochastic Computation Graphs. NIPS (2015)
John Schulman, Sergey Levine, Pieter Abbeel, Michael I. Jordan, Philipp Moritz
Trust Region Policy Optimization. ICML (2015)
John Schulman, Yan Duan, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel
Motion planning with sequential convex optimization and convex collision checking. Int. J. Robotics Res. 33 (9) (2014)
Sachin Patil, Yan Duan, John Schulman, Ken Goldberg, Pieter Abbeel
Gaussian belief space planning with discontinuities in sensing domains. ICRA (2014)
Sachin Patil, Gregory Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel
Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation. WAFR (2014)
Yan Duan, Sachin Patil, John Schulman, Kenneth Y. Goldberg, Pieter Abbeel
Planning locally optimal, curvature-constrained trajectories in 3D using sequential convex optimization. ICRA (2014)
John Schulman, Jonathan Ho, Cameron Lee, Pieter Abbeel
Learning from Demonstrations Through the Use of Non-rigid Registration. ISRR (2013)
John Schulman, Jonathan Ho, Alex X. Lee, Ibrahim Awwal, Henry Bradlow, Pieter Abbeel
Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization. Robotics: Science and Systems (2013)
John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel
A case study of trajectory transfer through non-rigid registration for a simplified suturing scenario. IROS (2013)
John Schulman, Alex X. Lee, Jonathan Ho, Pieter Abbeel
Tracking deformable objects with point clouds. ICRA (2013)
Alex X. Lee, Yan Duan, Sachin Patil, John Schulman, Zoe McCarthy, Jur van den Berg, Ken Goldberg, Pieter Abbeel
Sigma hulls for Gaussian belief space planning for imprecise articulated robots amid obstacles. IROS (2013)