Publications

This listing can also be found on my Google Scholar Profile. But here you can find handy links to related material like blog posts and presentation slides.

  • Semi-supervised Learning by Label Gradient Alignment
    Jacob Jackson, John Schulman
  • Quantifying Generalization in Reinforcement Learning
    Karl Kobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman
  • Model-based Reinforcement Learning Via Meta-Policy Optimization
    Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel
  • Gotta Learn Fast: A New Benchmark for Generalization in RL
    Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman
  • On First-Order Meta-Learning Algorithms
    Alex Nichol, Joshua Achiam, John Schulman
  • Meta Learning Shared Hierarchies
    Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman
  • Proximal Policy Optimization Algorithms
    John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
  • Teacher-Student Curriculum Learning
    Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman
  • UCB Exploration via Q-Ensembles
    Richard Chen, Szymon Sidor, Pieter Abbeel, John Schulman
  • Equivalence Between Policy Gradients and Soft Q-Learning
    John Schulman, Xi Chen, Pieter Abbeel
  • Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs
    PhD Dissertation, 2016
  • RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
    Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever,
    Pieter Abbeel
  • #Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
    Haoran Tang, Rein Houthooft, Davis Foote, Adam Stooke, Xi Chen, Yan Duan, John Schulman, Filip De Turck, Pieter Abbeel
  • Variational Lossy Autoencoder
    Xi Chen, Diederik Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel
  • Concrete Problems in AI Safety
    Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, Dan Mané
  • OpenAI Gym
    Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba
  • InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
    Xi Chen, Yan Duan, Rein Houthooft, John Schulman, Ilya Sutskever, Pieter Abbeel
    Neural Information Processing Systems (NIPS), 2016
  • Variational Information Maximizing Exploration
    Rein Houthooft, Xi Chen, Yan Duan, Filip De Turck, John Schulman, Pieter Abbeel.
    Neural Information Processing Systems (NIPS), 2016
  • Benchmarking Deep Reinforcement Learning for Continuous Control
    Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel.
    International Conference of Machine Learning (ICML), 2016
  • High-Dimensional Continuous Control Using Generalized Advantage Estimation
    John Schulman, Philipp Moritz, Sergey Levine, Michael I. Jordan, Pieter Abbeel
    International Conference of Learning Representations (ICLR), 2016
  • Spike Sorting for Large, Dense Electrode Arrays
    Cyrille Rossant, Shabnam Kadir, Dan F. M. Goodman, John Schulman,
    Mariano Belluscio, Gyorgy Buzsaki, Kenneth D. Harris
    Nature Neuroscience, 2016
  • Gradient Estimation Using Stochastic Computation Graphs
    John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel
    Neural Information Processing System (NIPS), 2015
  • Trust Region Policy Optimization
    John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel
    International Conference on Machine Learning (ICML), 2015
  • Scaling up Gaussian Belief Space Planning Through Covariance-Free Trajectory Optimization and Automatic Differentiation
    Sachin Patil, Greg Kahn, Michael Laskey, John Schulman, Ken Goldberg, Pieter Abbeel.
    Workshop on Algorithm Foundations of Robotics (WAFR), 2014
  • Motion Planning with Sequential Convex Optimization and Convex Collision Checking
    John Schulman, Yan Duan, Jonathan Ho, Alex Lee, Ibrahim Awwal, Henry Bradlow, Jia Pan, Sachin Patil, Ken Goldberg, Pieter Abbeel.
    International Journal of Robotics Research (IJRR), 2014
  • Planning Locally Optimal, Curvature-Constrained Trajectories in 3D Using Sequential Convex Optimization
    Yan Duan, Sachin Patil, John Schulman, Ken Goldberg, Pieter Abbeel.
    International Conference on Robotics and Automation (ICRA), 2014
  • Generalization in Robotic Manipulation Through the Use of Non-Rigid Registration
    John Schulman, Jonathan Ho, Cameron Lee, and Pieter Abbeel
    International Symposium on Robotics Research (ISRR), 2013
  • A Case Study of Trajectory Transfer Through Non-Rigid Registration for a Simplified Suturing Scenario
    John Schulman, Ankush Gupta, Sibi Venkatesan, Mallory Tayson-Frederick, Pieter Abbeel
    International Conference on Intelligent Robots and Systems (IROS), 2013
  • Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization
    John Schulman, Jonathan Ho, Alex Lee, Ibrahim Awwal, Henry Bradlow, Pieter Abbeel
    Robotics: Science and Systems (RSS), 2013
    Paper / Documentation / Github / Videos / Slides (With & Without Notes)
  • Tracking Deformable Objects with Point Clouds
    John Schulman, Alex Lee, Jonathan Ho, Pieter Abbeel
    International Conference on Robotics and Automation (ICRA), 2013,
    Winner of Best Vision Paper
    Paper / Website / Video (Youtube, MP4) / Slides (With & Without Notes)
  • Grasping and Fixturing as Submodular Coverage Problems
    John Schulman, Ken Goldberg, Pieter Abbeel
    International Symposium on Robotics Research (ISRR), 2011