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.
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Chris Hesse, Jaccob Hilton, John Schulman Paper (arXiv) / Blog post
Semi-supervised Learning by Label Gradient Alignment
Jacob Jackson, John Schulman Paper (arXiv)
Quantifying Generalization in Reinforcement Learning
Karl Kobbe, Oleg Klimov, Chris Hesse, Taehoon Kim, John Schulman Paper (arXiv) / Blog post
Model-based Reinforcement Learning Via Meta-Policy Optimization
Ignasi Clavera, Jonas Rothfuss, John Schulman, Yasuhiro Fujita, Tamim Asfour, Pieter Abbeel Paper (arXiv)
Gotta Learn Fast: A New Benchmark for Generalization in RL
Alex Nichol, Vicki Pfau, Christopher Hesse, Oleg Klimov, John Schulman Paper (arXiv) / Blog post
On First-Order Meta-Learning Algorithms
Alex Nichol, Joshua Achiam, John Schulman Paper (arXiv) / Blog post
Meta Learning Shared Hierarchies
Kevin Frans, Jonathan Ho, Xi Chen, Pieter Abbeel, John Schulman Paper (arXiv) / Blog post
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov Paper (arXiv) / Blog post
Teacher-Student Curriculum Learning
Tambet Matiisen, Avital Oliver, Taco Cohen, John Schulman Paper (arXiv)
UCB Exploration via Q-Ensembles
Richard Chen, Szymon Sidor, Pieter Abbeel, John Schulman Paper (arXiv)
Equivalence Between Policy Gradients and Soft Q-Learning
John Schulman, Xi Chen, Pieter Abbeel Paper (arXiv)
Optimizing Expectations: From Deep Reinforcement Learning to Stochastic Computation Graphs PhD Dissertation, 2016 Paper (PDF)
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Yan Duan, John Schulman, Xi Chen, Peter L. Bartlett, Ilya Sutskever,
Pieter Abbeel Paper (arXiv)
#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 Paper (arXiv)
Variational Lossy Autoencoder
Xi Chen, Diederik Kingma, Tim Salimans, Yan Duan, Prafulla Dhariwal, John Schulman, Ilya Sutskever, Pieter Abbeel Paper (arXiv)
OpenAI Gym
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, Wojciech Zaremba Paper (arXiv)
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 Paper (arXiv)
Variational Information Maximizing Exploration
Rein Houthooft, Xi Chen, Yan Duan, Filip De Turck, John Schulman, Pieter Abbeel. Neural Information Processing Systems (NIPS), 2016 Paper (arXiv)
Benchmarking Deep Reinforcement Learning for Continuous Control
Yan Duan, Xi Chen, Rein Houthooft, John Schulman, Pieter Abbeel. International Conference of Machine Learning (ICML), 2016 Paper (arXiv)
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 Paper (arXiv) / Video
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 Paper
Gradient Estimation Using Stochastic Computation Graphs
John Schulman, Nicolas Heess, Theophane Weber, Pieter Abbeel Neural Information Processing System (NIPS), 2015 Paper (arXiv)
Trust Region Policy Optimization
John Schulman, Sergey Levine, Philipp Moritz, Michael I. Jordan, Pieter Abbeel International Conference on Machine Learning (ICML), 2015 Paper (arXiv) / Videos
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 Paper
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 Paper
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 Paper
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 Paper / Videos
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 Paper / Videos
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 Paper