Proximal policy optimization algorithms J Schulman, F Wolski, P Dhariwal, A Radford, O Klimov arXiv preprint arXiv:1707.06347, 2017 | 3574 | 2017 |
Trust region policy optimization J Schulman, S Levine, P Abbeel, M Jordan, P Moritz International conference on machine learning, 1889-1897, 2015 | 3112 | 2015 |
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, 2172-2180, 2016 | 2510 | 2016 |
OpenAI Gym G Brockman, V Cheung, L Pettersson, J Schneider, J Schulman, J Tang, ... arXiv preprint arXiv:1606.01540, 2016 | 2142 | 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 | 1157 | 2015 |
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 | 1053 | 2016 |
Concrete problems in AI safety D Amodei, C Olah, J Steinhardt, P Christiano, J Schulman, D Mané arXiv preprint arXiv:1606.06565, 2016 | 917 | 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, arXiv: 1605.02688, 2016 | 631 | 2016 |
On first-order meta-learning algorithms A Nichol, J Achiam, J Schulman arXiv preprint arXiv:1803.02999, 2018 | 621* | 2018 |
OpenAI Baselines P Dhariwal, C Hesse, M Plappert, A Radford, J Schulman, S Sidor, Y Wu | 610 | 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, 1109-1117, 2016 | 446* | 2016 |
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 | 431 | 2016 |
Spike sorting for large, dense electrode arrays C Rossant, SN Kadir, DFM Goodman, J Schulman, MLD Hunter, ... Nature neuroscience 19 (4), 634-641, 2016 | 420 | 2016 |
Variational lossy autoencoder X Chen, DP Kingma, T Salimans, Y Duan, P Dhariwal, J Schulman, ... arXiv preprint arXiv:1611.02731, 2016 | 411 | 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 | 391 | 2014 |
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 | 379 | 2013 |
#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 | 345 | 2017 |
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 | 267 | 2017 |
Stable baselines A Hill, A Raffin, M Ernestus, A Gleave, R Traore, P Dhariwal, C Hesse, ... GitHub repository, 2018 | 260 | 2018 |
Gradient estimation using stochastic computation graphs J Schulman, N Heess, T Weber, P Abbeel Advances in Neural Information Processing Systems 28, 3528-3536, 2015 | 258 | 2015 |