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| Language models are unsupervised multitask learners A Radford, J Wu, R Child, D Luan, D Amodei, I Sutskever OpenAI blog 1 (8), 9, 2019 | 14526 | 2019 |
| Improving language understanding by generative pre-training A Radford | 11802 | 2018 |
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| OpenAI Gym G Brockman arXiv preprint arXiv:1606.01540, 2016 | 8238 | 2016 |
| 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 | 5612 | 2016 |
| Multi-agent actor-critic for mixed cooperative-competitive environments R Lowe, YI Wu, A Tamar, J Harb, OAI Pieter Abbeel, I Mordatch Advances in neural information processing systems 30, 2017 | 5484 | 2017 |
| Glow: Generative flow with invertible 1x1 convolutions DP Kingma, P Dhariwal Advances in neural information processing systems 31, 2018 | 3514 | 2018 |
| Domain randomization for transferring deep neural networks from simulation to the real world J Tobin, R Fong, A Ray, J Schneider, W Zaremba, P Abbeel 2017 IEEE/RSJ international conference on intelligent robots and systems …, 2017 | 3505 | 2017 |
| Deep reinforcement learning from human preferences PF Christiano, J Leike, T Brown, M Martic, S Legg, D Amodei Advances in neural information processing systems 30, 2017 | 3150 | 2017 |
| Hindsight experience replay M Andrychowicz, F Wolski, A Ray, J Schneider, R Fong, P Welinder, ... Advances in neural information processing systems 30, 2017 | 3004 | 2017 |
| Concrete problems in AI safety D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané arXiv preprint arXiv:1606.06565, 2016 | 3002 | 2016 |
| Weight normalization: A simple reparameterization to accelerate training of deep neural networks T Salimans, DP Kingma Advances in neural information processing systems 29, 2016 | 2351 | 2016 |
| Improved variational inference with inverse autoregressive flow DP Kingma, T Salimans, R Jozefowicz, X Chen, I Sutskever, M Welling Advances in neural information processing systems 29, 2016 | 2170 | 2016 |
| Dota 2 with large scale deep reinforcement learning C Berner, G Brockman, B Chan, V Cheung, P Dębiak, C Dennison, ... arXiv preprint arXiv:1912.06680, 2019 | 2013 | 2019 |
| Generating long sequences with sparse transformers R Child, S Gray, A Radford, I Sutskever arXiv preprint arXiv:1904.10509, 2019 | 1937 | 2019 |
| Learning dexterous in-hand manipulation OpenAI, M Andrychowicz, B Baker, M Chociej, R Józefowicz, B McGrew, ... arXiv preprint arXiv:1808.00177, 2018 | 1851* | 2018 |
| Evolution strategies as a scalable alternative to reinforcement learning T Salimans, J Ho, X Chen, S Sidor, I Sutskever arXiv preprint arXiv:1703.03864, 2017 | 1818 | 2017 |
| Sim-to-real transfer of robotic control with dynamics randomization XB Peng, M Andrychowicz, W Zaremba, P Abbeel 2018 IEEE international conference on robotics and automation (ICRA), 3803-3810, 2018 | 1641 | 2018 |
| Fine-tuning language models from human preferences DM Ziegler, N Stiennon, J Wu, TB Brown, A Radford, D Amodei, ... arXiv preprint arXiv:1909.08593, 2019 | 1394 | 2019 |
Pieter AbbeelUC Berkeley | CovariantVerified email at cs.berkeley.edu
