John Schulman

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All published works

Action	Title	Year	Authors
+ PDF Chat	Measuring short-form factuality in large language models	2024	Jason Lee Nguyen Karina Hyung Won Chung Yunxin Joy Jiao Spencer Papay Amelia Glaese John Schulman William Fedus
+ PDF Chat	Rule Based Rewards for Language Model Safety	2024	Tong Mu Alec Helyar Johannes Heidecke Joshua Achiam Andrea Vallone Ian Kivlichan Molly Lin Alex Beutel John Schulman Lilian Weng
+ PDF Chat	GPT-4o System Card	2024	OpenAI NULL AUTHOR_ID A. M. Hurst Adam Lerer Adam P. Goucher Adam Perelman Aditya Ramesh Aidan Clark AJ Ostrow Akila Welihinda
+	Training Verifiers to Solve Math Word Problems	2021	Karl Cobbe Vineet Kosaraju Mohammad Bavarian Jacob Hilton Reiichiro Nakano Christopher Hesse John Schulman
+	Batch size-invariance for policy optimization.	2021	Jacob Hilton Karl Cobbe John Schulman
+	Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark	2021	Sharada P. Mohanty Jyotish Poonganam Adrien Gaidon Andrey Kolobov Blake Wulfe Dipam Chakraborty Gražvydas Šemetulskis João Schapke Jonas Kubilius Jurgis Pašukonis
+	Unsolved Problems in ML Safety	2021	Dan Hendrycks Nicholas Carlini John Schulman Jacob Steinhardt
+	Phasic Policy Gradient	2020	Karl Cobbe Jacob Hilton Oleg Klimov John Schulman
+	Scaling Laws for Autoregressive Generative Modeling	2020	Tom Henighan Jared Kaplan Mor Katz Mark Chen Christopher Hesse Jacob Jackson Heewoo Jun T. B. Brown Prafulla Dhariwal Scott Gray
+ PDF Chat	Teacher–Student Curriculum Learning	2019	Tambet Matiisen Avital Oliver Taco Cohen John Schulman
+	Policy Gradient Search: Online Planning and Expert Iteration without Search Trees	2019	Thomas Anthony Robert Nishihara Philipp Moritz Tim Salimans John Schulman
+	Semi-Supervised Learning by Label Gradient Alignment	2019	Jacob Jackson John Schulman
+	The MineRL 2019 Competition on Sample Efficient Reinforcement Learning using Human Priors	2019	William H. Guss Mario Ynocente Castro Sam Devlin Brandon Houghton Noboru Sean Kuno Crissman Loomis Stephanie Milani Sharada P. Mohanty Keisuke Nakata Ruslan Salakhutdinov
+	Leveraging Procedural Generation to Benchmark Reinforcement Learning	2019	Karl Cobbe Christopher Hesse Jacob Hilton John Schulman
+	Quantifying Generalization in Reinforcement Learning	2018	Karl Cobbe Oleg Klimov Chris Hesse Taehoon Kim John Schulman
+	Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations	2018	Aravind Rajeswaran Vikash Kumar Abhishek Gupta Giulia Vezzani John Schulman Emanuel Todorov Sergey Levine
+	Gotta Learn Fast: A New Benchmark for Generalization in RL	2018	Alex Nichol Vicki Pfau Christopher Hesse Oleg Klimov John Schulman
+	Reptile: a Scalable Metalearning Algorithm	2018	Alex Nichol John Schulman
+	On First-Order Meta-Learning Algorithms.	2018	Alex Nichol Joshua Achiam John Schulman
+	Model-Based Reinforcement Learning via Meta-Policy Optimization	2018	Ignasi Clavera Jonas Rothfuss John Schulman Yasuhiro Fujita Tamim Asfour Pieter Abbeel
+	Meta Learning Shared Hierarchies	2017	Kevin Frans Jonathan Ho Xi Chen Pieter Abbeel John Schulman
+	Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrations	2017	Aravind Rajeswaran Vikash Kumar Abhishek Gupta John Schulman Emanuel Todorov Sergey Levine
+	Teacher-Student Curriculum Learning	2017	Tambet Matiisen Avital Oliver Taco Cohen John Schulman
+	UCB and InfoGain Exploration via $\boldsymbol{Q}$-Ensembles.	2017	Richard Y. Chen Szymon Sidor Pieter Abbeel John Schulman
+	Equivalence Between Policy Gradients and Soft Q-Learning	2017	John Schulman Xi Chen Pieter Abbeel
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov
+	UCB Exploration via Q-Ensembles	2017	Richard Y. Chen Szymon Sidor Pieter Abbeel John Schulman
+	Meta Learning Shared Hierarchies	2017	Kevin Frans Jonathan Ho Xi Chen Pieter Abbeel John Schulman
+	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets	2016	Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel
+	OpenAI Gym	2016	Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang Wojciech Zaremba
+	Curiosity-driven Exploration in Deep Reinforcement Learning via Bayesian Neural Networks.	2016	Rein Houthooft Xi Chen Yan Duan John Schulman Filip De Turck Pieter Abbeel
+	Benchmarking Deep Reinforcement Learning for Continuous Control	2016	Yan Duan Xi Chen Rein Houthooft John Schulman Pieter Abbeel
+	Theano: A Python framework for fast computation of mathematical expressions	2016	The Theano Development Team Rami Al‐Rfou Guillaume Alain Amjad Almahairi Christof Angermueller Dzmitry Bahdanau Nicolas Ballas Frédéric Bastien Justin Bayer Anatoly Belikov
+	VIME: Variational Information Maximizing Exploration	2016	Rein Houthooft Xi Chen Yan Duan John Schulman Filip De Turck Pieter Abbeel
+	Concrete Problems in AI Safety	2016	Dario Amodei Chris Olah Jacob Steinhardt Paul F. Christiano John Schulman Dan Mané
+	RL$^2$: Fast Reinforcement Learning via Slow Reinforcement Learning	2016	Yan Duan John Schulman Xi Chen Peter L. Bartlett Ilya Sutskever Pieter Abbeel
+	#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning	2016	Haoran Tang Rein Houthooft Davis Foote Adam Stooke Xi Chen Yan Duan John Schulman Filip De Turck Pieter Abbeel
+	Variational Lossy Autoencoder	2016	Xi Chen Diederik P. Kingma Tim Salimans Yan Duan Prafulla Dhariwal John Schulman Ilya Sutskever Pieter Abbeel
+	InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets	2016	Xi Chen Yan Duan Rein Houthooft John Schulman Ilya Sutskever Pieter Abbeel
+	OpenAI Gym	2016	Greg Brockman Vicki Cheung Ludwig Pettersson Jonas Schneider John Schulman Jie Tang Wojciech Zaremba
+	High-Dimensional Continuous Control Using Generalized Advantage Estimation	2015	John Schulman Philipp Moritz Sergey Levine Michael I. Jordan Pieter Abbeel
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel
+	Gradient Estimation Using Stochastic Computation Graphs	2015	John Schulman Nicolas Heess Théophane Weber Pieter Abbeel

Common Coauthors

Coauthor	Papers Together
Pieter Abbeel	17
Yan Duan	8
Christopher Hesse	6
Karl Cobbe	6
Rein Houthooft	6
Ilya Sutskever	5
Jacob Hilton	5
Xi Chen	4
Sergey Levine	4
Alex Nichol	4
Prafulla Dhariwal	4
Oleg Klimov	4
Xi Chen	3
Wojciech Zaremba	3
Greg Brockman	3
Joshua Achiam	3
Philipp Moritz	3
Filip De Turck	3
Alec Radford	3
Avital Oliver	3
Jacob Jackson	2
Xi Chen	2
Vikash Kumar	2
Richard Y. Chen	2
Spencer Papay	2
Taco Cohen	2
Jie Tang	2
Xi Chen	2
Lilian Weng	2
Aditya Ramesh	2
William H. Guss	2
Kevin Frans	2
Heewoo Jun	2
Szymon Sidor	2
Michael I. Jordan	2
Hyung Won Chung	2
Dario Amodei	2
Scott Gray	2
Ludwig Pettersson	2
Aravind Rajeswaran	2
Chris Hallacy	2
Ian Kivlichan	2
Sharada P. Mohanty	2
Jonathan Ho	2
Abhishek Gupta	2
Jacob Steinhardt	2
Tambet Matiisen	2
Vicki Cheung	2
Tim Salimans	2
Emanuel Todorov	2

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+	Proximal Policy Optimization Algorithms	2017	John Schulman Filip Wolski Prafulla Dhariwal Alec Radford Oleg Klimov	11
+	Adam: A Method for Stochastic Optimization	2014	Diederik P. Kingma Jimmy Ba	9
+	Trust Region Policy Optimization	2015	John Schulman Sergey Levine Philipp Moritz Michael I. Jordan Pieter Abbeel	8
+	Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift	2015	Sergey Ioffe Christian Szegedy	6
+	Benchmarking Deep Reinforcement Learning for Continuous Control	2016	Yan Duan Xi Chen Rein Houthooft John Schulman Pieter Abbeel	6
+ PDF Chat	The Arcade Learning Environment: An Evaluation Platform for General Agents	2013	Marc G. Bellemare Yavar Naddaf Joel Veness Michael Bowling	6
+	Continuous control with deep reinforcement learning	2015	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	6
+	High-Dimensional Continuous Control Using Generalized Advantage Estimation	2015	John Schulman Philipp Moritz Sergey Levine Michael I. Jordan Pieter Abbeel	5
+	Incentivizing Exploration In Reinforcement Learning With Deep Predictive Models	2015	Bradly C. Stadie Sergey Levine Pieter Abbeel	4
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Timothy Lillicrap Tim Harley David Silver Koray Kavukcuoglu	4
+	End-to-End Training of Deep Visuomotor Policies	2015	Sergey Levine Chelsea Finn Trevor Darrell Pieter Abbeel	4
+	Weight Uncertainty in Neural Networks	2015	Charles Blundell Julien Cornebise Koray Kavukcuoglu Daan Wierstra	3
+	Deep Exploration via Bootstrapped DQN	2016	Ian Osband Charles Blundell Alexander Pritzel Benjamin Van Roy	3
+	Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks	2017	Chelsea Finn Pieter Abbeel Sergey Levine	3
+	Gotta Learn Fast: A New Benchmark for Generalization in RL	2018	Alex Nichol Vicki Pfau Christopher Hesse Oleg Klimov John Schulman	3
+	End to End Learning for Self-Driving Cars	2016	Mariusz Bojarski Davide Del Testa Daniel Dworakowski Bernhard Firner Beat Flepp Prasoon Goyal Lawrence D. Jackel Mathew Monfort Urs Müller Jiakai Zhang	3
+ PDF Chat	Deep Residual Learning for Image Recognition	2016	Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun	3
+	Dota 2 with Large Scale Deep Reinforcement Learning	2019	Christopher Berner Greg Brockman Brooke Chan Vicki Cheung Przemyslaw Debiak Christy Dennison David Farhi Quirin Fischer Shariq Hashme Christopher Hesse	3
+	VIME: Variational Information Maximizing Exploration	2016	Rein Houthooft Xi Chen Yan Duan John Schulman Filip De Turck Pieter Abbeel	3
+	Emergence of Locomotion Behaviours in Rich Environments	2017	Nicolas Heess Dhruva Tb Sriram Srinivasan Jay Lemmon Josh Merel Greg Wayne Yuval Tassa Tom Erez Ziyu Wang S. M. Ali Eslami	3
+	Neural GPUs Learn Algorithms	2015	Łukasz Kaiser Ilya Sutskever	3
+	IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures	2018	Lasse Espeholt Hubert Soyer Rémi Munos Karen Simonyan Volodymir Mnih Tom Ward Yotam Doron Vlad Firoiu Tim Harley Iain Dunning	3
+	Continuous control with deep reinforcement learning	2016	Timothy Lillicrap Jonathan J. Hunt Alexander Pritzel Nicolas Heess Tom Erez Yuval Tassa David Silver Daan Wierstra	2
+	A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning	2010	Stéphane Ross Geoffrey J. Gordon J. Andrew Bagnell	2
+	Asynchronous Methods for Deep Reinforcement Learning	2016	Volodymyr Mnih Adrià Puigdomènech Badia Mehdi Mirza Alex Graves Tim Harley Timothy Lillicrap David Silver Koray Kavukcuoglu	2
+	Reinforcement Learning through Asynchronous Advantage Actor-Critic on a GPU	2016	Mohammad Babaeizadeh Iuri Frosio Stephen Tyree Jason Clemons Jan Kautz	2
+	Assessing Generalization in Deep Reinforcement Learning	2018	Charles Packer Katelyn Gao Jernej Kos Philipp Krähenbühl Vladlen Koltun Dawn Song	2
+	Learning Continuous Control Policies by Stochastic Value Gradients	2015	Nicolas Heess Greg Wayne David Silver Timothy Lillicrap Yuval Tassa Tom Erez	2
+	Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles	2016	Balaji Lakshminarayanan Alexander Pritzel Charles Blundell	2
+ PDF Chat	Revisiting the Arcade Learning Environment: Evaluation Protocols and Open Problems for General Agents	2018	Marlos C. Machado Marc G. Bellemare Erik Talvitie Joel Veness Matthew Hausknecht Michael Bowling	2
+	Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation	2018	Niels Justesen Rubén Rodríguez Torrado Philip Bontrager Ahmed Khalifa Julian Togelius Sebastian Risi	2
+	An Empirical Model of Large-Batch Training	2018	Sam McCandlish Jared Kaplan Dario Amodei OpenAI Dota Team	2
+	Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction	2017	Wen Sun Arun Venkatraman Geoffrey J. Gordon Byron Boots J. Andrew Bagnell	2
+ PDF Chat	A Deep Hierarchical Approach to Lifelong Learning in Minecraft	2017	Chen Tessler Shahar Givony Tom Zahavy Daniel J. Mankowitz Shie Mannor	2
+ PDF Chat	Deep Reinforcement Learning That Matters	2018	Peter Henderson Riashat Islam Philip Bachman Joëlle Pineau Doina Precup David Meger	2
+	Learning from Demonstrations for Real World Reinforcement Learning	2017	Todd Hester Matej Vecerík Olivier Pietquin Marc Lanctot Tom Schaul Bilal Piot Andrew Sendonaris Gabriel Dulac-Arnold Ian Osband John Agapiou	2
+	Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm	2017	Chelsea Finn Sergey Levine	2
+ PDF Chat	Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments	2011	Yi Sun Faustino Gomez Jürgen Schmidhuber	2
+	Intrinsic Motivation and Automatic Curricula via Asymmetric Self-Play	2017	Sainbayar Sukhbaatar Zeming Lin Ilya Kostrikov Gabriel Synnaeve Arthur Szlam Rob Fergus	2
+	Matching Networks for One Shot Learning	2016	Oriol Vinyals Charles Blundell Timothy Lillicrap Koray Kavukcuoglu Daan Wierstra	2
+	Multi-Armed Bandits for Intelligent Tutoring Systems	2013	Benjamin Clément Didier Roy Pierre-Yves Oudeyer Manuel Lopes	2
+ PDF Chat	Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates	2017	Shixiang Gu Ethan Holly Timothy Lillicrap Sergey Levine	2
+	Sample Efficient Actor-Critic with Experience Replay	2016	Ziyu Wang Victor Bapst Nicolas Heess Volodymyr Mnih Rémi Munos Koray Kavukcuoglu Nando de Freitas	2
+	Automatic Goal Generation for Reinforcement Learning Agents	2017	Carlos Florensa David Held Xinyang Geng Pieter Abbeel	2
+	Reinforcement Learning from Imperfect Demonstrations	2018	Yang Gao Huazhe Xu Ji Lin Fisher Yu Sergey Levine Trevor Darrell	2
+	Leveraging Demonstrations for Deep Reinforcement Learning on Robotics Problems with Sparse Rewards	2017	Matej Vecerík Todd Hester Jonathan Scholz Fumin Wang Olivier Pietquin Bilal Piot Nicolas Heess Thomas Rothörl Thomas Lampe Martin Riedmiller	2
+ PDF Chat	On the shortest spanning subtree of a graph and the traveling salesman problem	1956	Joseph B. Kruskal	2
+	A Dissection of Overfitting and Generalization in Continuous Reinforcement Learning	2018	Amy Zhang Nicolas Ballas Joëlle Pineau	2
+	Generalization and Regularization in DQN	2018	Jesse Farebrother Marlos C. Machado Michael Bowling	2
+	A Deep Hierarchical Approach to Lifelong Learning in Minecraft	2016	Chen Tessler Shahar Givony Tom Zahavy Daniel J. Mankowitz Shie Mannor	2