| 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 | 455 | 2018 |
| Deepmimic: Example-guided deep reinforcement learning of physics-based character skills XB Peng, P Abbeel, S Levine, M van de Panne ACM Transactions on Graphics (TOG) 37 (4), 1-14, 2018 | 334 | 2018 |
| Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning XB Peng, G Berseth, KK Yin, M Van De Panne ACM Transactions on Graphics (TOG) 36 (4), 1-13, 2017 | 323 | 2017 |
| Terrain-adaptive locomotion skills using deep reinforcement learning XB Peng, G Berseth, M Van de Panne ACM Transactions on Graphics (TOG) 35 (4), 1-12, 2016 | 208 | 2016 |
| Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow XB Peng, A Kanazawa, S Toyer, P Abbeel, S Levine arXiv preprint arXiv:1810.00821, 2018 | 88 | 2018 |
| Sfv: Reinforcement learning of physical skills from videos XB Peng, A Kanazawa, J Malik, P Abbeel, S Levine ACM Transactions On Graphics (TOG) 37 (6), 1-14, 2018 | 82 | 2018 |
| Learning locomotion skills using deeprl: Does the choice of action space matter? XB Peng, M van de Panne Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation …, 2017 | 65 | 2017 |
| Dynamic terrain traversal skills using reinforcement learning XB Peng, G Berseth, M Van de Panne ACM Transactions on Graphics (TOG) 34 (4), 1-11, 2015 | 56 | 2015 |
| MCP: Learning composable hierarchical control with multiplicative compositional policies XB Peng, M Chang, G Zhang, P Abbeel, S Levine arXiv preprint arXiv:1905.09808, 2019 | 40 | 2019 |
| Learning agile robotic locomotion skills by imitating animals XB Peng, E Coumans, T Zhang, TW Lee, J Tan, S Levine arXiv preprint arXiv:2004.00784, 2020 | 31 | 2020 |
| Advantage-weighted regression: Simple and scalable off-policy reinforcement learning XB Peng, A Kumar, G Zhang, S Levine arXiv preprint arXiv:1910.00177, 2019 | 26 | 2019 |
| On learning symmetric locomotion F Abdolhosseini, HY Ling, Z Xie, XB Peng, M van de Panne Motion, Interaction and Games, 1-10, 2019 | 9 | 2019 |
| Reinforcement learning with competitive ensembles of information-constrained primitives A Goyal, S Sodhani, J Binas, XB Peng, S Levine, Y Bengio arXiv preprint arXiv:1906.10667, 2019 | 5 | 2019 |
| Reward-conditioned policies A Kumar, XB Peng, S Levine arXiv preprint arXiv:1912.13465, 2019 | 3 | 2019 |
| Terrain RL simulator G Berseth, XB Peng, M van de Panne arXiv preprint arXiv:1804.06424, 2018 | 3 | 2018 |
| Offline Meta-Reinforcement Learning with Advantage Weighting E Mitchell, R Rafailov, XB Peng, S Levine, C Finn arXiv preprint arXiv:2008.06043, 2020 | | 2020 |
| Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation S Song, Ł Kidziński, XB Peng, C Ong, JL Hicks, S Levine, C Atkeson, ... bioRxiv, 2020 | | 2020 |
| 利用信息約束基元的競爭集合強化學習 A Goyal, S Sodhani, J Binas, XB Peng, S Levine, Y Bengio1y | | |
| Dynamic Locomotion Skills for Obstacle Sequences Using Reinforcement Learning XB Peng, G Berseth, M van de Panne | | |
Michiel van de PanneProfessor, Computer Science, University of British ColumbiaVerified email at cs.ubc.ca
