John Schulman
John Schulman
Research Scientist, OpenAI
Verified email at - Homepage
Cited by
Cited by
Proximal policy optimization algorithms
J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov
arXiv preprint arXiv:1707.06347, 2017
Trust region policy optimization
J Schulman, S Levine, P Abbeel, M Jordan, P Moritz
International conference on machine learning, 1889-1897, 2015
OpenAI Gym
G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ...
arXiv preprint arXiv:1606.01540, 2016
Training language models to follow instructions with human feedback
L Ouyang, J Wu, X Jiang, D Almeida, C Wainwright, P Mishkin, C Zhang, ...
Advances in neural information processing systems 35, 27730-27744, 2022
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 29, 2016
High-dimensional continuous control using generalized advantage estimation
J Schulman, P Moritz, S Levine, M Jordan, P Abbeel
arXiv preprint arXiv:1506.02438, 2015
On first-order meta-learning algorithms
A Nichol, J Achiam, J Schulman
arXiv preprint arXiv:1803.02999, 2018
Concrete problems in AI safety
D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané
arXiv preprint arXiv:1606.06565, 2016
Gpt-4 technical report
J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ...
arXiv preprint arXiv:2303.08774, 2023
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
Training verifiers to solve math word problems
K Cobbe, V Kosaraju, M Bavarian, M Chen, H Jun, L Kaiser, M Plappert, ...
arXiv preprint arXiv:2110.14168, 2021
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
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A Rajeswaran, V Kumar, A Gupta, G Vezzani, J Schulman, E Todorov, ...
arXiv preprint arXiv:1709.10087, 2017
OpenAI Baselines
P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu
Vime: Variational information maximizing exploration
R Houthooft, X Chen, Y Duan, J Schulman, F De Turck, P Abbeel
Advances in neural information processing systems 29, 2016
Theano: A Python framework for fast computation of mathematical expressions
R Al-Rfou, G Alain, A Almahairi, C Angermueller, D Bahdanau, N Ballas, ...
arXiv e-prints, arXiv: 1605.02688, 2016
Stable baselines
A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ...
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
Spike sorting for large, dense electrode arrays
C Rossant, SN Kadir, DFM Goodman, J Schulman, MLD Hunter, ...
Nature neuroscience 19 (4), 634-641, 2016
Webgpt: Browser-assisted question-answering with human feedback
R Nakano, J Hilton, S Balaji, J Wu, L Ouyang, C Kim, C Hesse, S Jain, ...
arXiv preprint arXiv:2112.09332, 2021
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