| Proximal policy optimization algorithms J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov arXiv preprint arXiv:1707.06347, 2017 | 24353 | 2017 |
| 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 | 12910 | 2022 |
| Trust region policy optimization J Schulman, S Levine, P Abbeel, M Jordan, P Moritz International conference on machine learning, 1889-1897, 2015 | 9415 | 2015 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 9276 | 2023 |
| OpenAI Gym G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ... arXiv preprint arXiv:1606.01540, 2016 | 8828 | 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 | 5825 | 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 | 4363 | 2015 |
| On first-order meta-learning algorithms A Nichol, J Achiam, J Schulman arXiv preprint arXiv:1803.02999, 2018 | 3260* | 2018 |
| Concrete problems in AI safety D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané arXiv preprint arXiv:1606.06565, 2016 | 3192 | 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 | 3068 | 2021 |
| 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 | 2194 | 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 | 1260 | 2017 |
| 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 | 1247 | 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 | 1193 | 2021 |
| OpenAI Baselines P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu | 1090 | 2017 |
| 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 | 1014 | 2016 |
| 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 | 1004 | 2014 |
| 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 | 988 | 2016 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 961 | 2018 |
| Spike sorting for large, dense electrode arrays C Rossant, SN Kadir, DFM Goodman, J Schulman, MLD Hunter, ... Nature neuroscience 19 (4), 634-641, 2016 | 861 | 2016 |
