John Schulman
John Schulman
Research Scientist, OpenAI
Verified email at openai.com - Homepage
TitleCited byYear
Trust region policy optimization
J Schulman, S Levine, P Abbeel, M Jordan, P Moritz
International conference on machine learning, 1889-1897, 2015
14922015
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X Chen, Y Duan, R Houthooft, J Schulman, I Sutskever, P Abbeel
Advances in neural information processing systems, 2172-2180, 2016
12812016
Proximal policy optimization algorithms
J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov
arXiv preprint arXiv:1707.06347, 2017
11832017
OpenAI Gym
G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ...
arXiv preprint arXiv:1606.01540, 2016
9502016
Benchmarking deep reinforcement learning for continuous control
Y Duan, X Chen, R Houthooft, J Schulman, P Abbeel
International Conference on Machine Learning, 1329-1338, 2016
5832016
High-dimensional continuous control using generalized advantage estimation
J Schulman, P Moritz, S Levine, M Jordan, P Abbeel
arXiv preprint arXiv:1506.02438, 2015
5602015
Concrete problems in AI safety
D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané
arXiv preprint arXiv:1606.06565, 2016
4402016
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv preprint arXiv:1605.02688, 2016
4232016
Finding Locally Optimal, Collision-Free Trajectories with Sequential Convex Optimization.
J Schulman, J Ho, AX Lee, I Awwal, H Bradlow, P Abbeel
Robotics: science and systems 9 (1), 1-10, 2013
2952013
OpenAI Baselines
P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu
2772017
Spike sorting for large, dense electrode arrays
C Rossant, SN Kadir, DFM Goodman, J Schulman, MLD Hunter, ...
Nature neuroscience 19 (4), 634, 2016
2682016
Motion planning with sequential convex optimization and convex collision checking
J Schulman, Y Duan, J Ho, A Lee, I Awwal, H Bradlow, J Pan, S Patil, ...
The International Journal of Robotics Research 33 (9), 1251-1270, 2014
2612014
Vime: Variational information maximizing exploration
R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel
Advances in Neural Information Processing Systems, 1109-1117, 2016
232*2016
Variational lossy autoencoder
X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ...
arXiv preprint arXiv:1611.02731, 2016
2302016
RL^2: Fast Reinforcement Learning via Slow Reinforcement Learning
Y Duan, J Schulman, X Chen, PL Bartlett, I Sutskever, P Abbeel
arXiv preprint arXiv:1611.02779, 2016
1922016
Gradient estimation using stochastic computation graphs
J Schulman, N Heess, T Weber, P Abbeel
Advances in Neural Information Processing Systems, 3528-3536, 2015
1542015
On first-order meta-learning algorithms
A Nichol, J Achiam, J Schulman
arXiv preprint arXiv:1803.02999, 2018
153*2018
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
H Tang, R Houthooft, D Foote, A Stooke, OAIX Chen, Y Duan, J Schulman, ...
Advances in Neural Information Processing Systems, 2750-2759, 2017
1332017
Theano: A Python framework for fast computation of mathematical expressions
TTD Team, R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, ...
arXiv preprint arXiv:1605.02688, 2016
1042016
Learning from demonstrations through the use of non-rigid registration
J Schulman, J Ho, C Lee, P Abbeel
Robotics Research, 339-354, 2016
102*2016
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