| 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 | 1758 | 2019 |
| 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 | 1684 | 2017 |
| Learning dexterous in-hand manipulation OAIM Andrychowicz, B Baker, M Chociej, R Jozefowicz, B McGrew, ... The International Journal of Robotics Research 39 (1), 3-20, 2020 | 1640 | 2020 |
| Gpt-4 technical report J Achiam, S Adler, S Agarwal, L Ahmad, I Akkaya, FL Aleman, D Almeida, ... arXiv preprint arXiv:2303.08774, 2023 | 1567 | 2023 |
| Openai baselines P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ... | 1005 | 2017 |
| Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, A Kanervisto, R Traore, P Dhariwal, ... | 878 | 2018 |
| Parameter space noise for exploration M Plappert, R Houthooft, P Dhariwal, S Sidor, RY Chen, X Chen, T Asfour, ... arXiv preprint arXiv:1706.01905, 2017 | 714 | 2017 |
| Emergent complexity via multi-agent competition T Bansal, J Pachocki, S Sidor, I Sutskever, I Mordatch arXiv preprint arXiv:1710.03748, 2017 | 465 | 2017 |
| Schema networks: Zero-shot transfer with a generative causal model of intuitive physics K Kansky, T Silver, DA Mély, M Eldawy, M Lázaro-Gredilla, X Lou, ... International conference on machine learning, 1809-1818, 2017 | 276 | 2017 |
| Ucb exploration via q-ensembles RY Chen, S Sidor, P Abbeel, J Schulman arXiv preprint arXiv:1706.01502, 2017 | 131 | 2017 |
| Dota 2 with large scale deep reinforcement learning CB OpenAI, G Brockman, B Chan, V Cheung, P Debiak, C Dennison, ... arXiv preprint arXiv:1912.06680 2, 2019 | 108 | 2019 |
| Tensor programs v: Tuning large neural networks via zero-shot hyperparameter transfer G Yang, EJ Hu, I Babuschkin, S Sidor, X Liu, D Farhi, N Ryder, J Pachocki, ... arXiv preprint arXiv:2203.03466, 2022 | 81 | 2022 |
| Tuning large neural networks via zero-shot hyperparameter transfer G Yang, E Hu, I Babuschkin, S Sidor, X Liu, D Farhi, N Ryder, J Pachocki, ... Advances in Neural Information Processing Systems 34, 17084-17097, 2021 | 69 | 2021 |
| Evolution strategies as a scalable alternative to reinforcement learning. arXiv 2017 T Salimans, J Ho, X Chen, S Sidor, I Sutskever arXiv preprint arXiv:1703.03864, 2017 | 61 | 2017 |
| Openai baselines (2017) P Dhariwal, C Hesse, O Klimov, A Nichol, M Plappert, A Radford, ... URL https://github. com/openai/baselines, 2016 | 60 | 2016 |
| UCB and infogain exploration via q-ensembles RY Chen, J Schulman, P Abbeel, S Sidor arXiv preprint arXiv:1706.01502 9, 2017 | 29 | 2017 |
| OpenAI baselines C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu | 19 | 2017 |
| Reinforcement learning with natural language signals S Sidor Massachusetts Institute of Technology, 2016 | 6 | 2016 |
| Time resource networks S Sidor, P Yu, C Fang, B Williams arXiv preprint arXiv:1602.03203, 2016 | 2 | 2016 |
| Occam's gates J Raiman, S Sidor arXiv preprint arXiv:1506.08251, 2015 | 1 | 2015 |
