| Reinforcement Learning with Deep Energy-Based Policies H Tang, T Haarnoja, P Abbeel, S Levine arXiv preprint arXiv:1702.08165, 2017 | 818* | 2017 |
| # exploration: A study of count-based exploration for deep reinforcement learning H Tang, R Houthooft, D Foote, A Stooke, OAI Xi Chen, Y Duan, ... Advances in neural information processing systems 30, 2017 | 550 | 2017 |
| Why does hierarchy (sometimes) work so well in reinforcement learning? O Nachum, H Tang, X Lu, S Gu, H Lee, S Levine arXiv preprint arXiv:1909.10618, 2019 | 46 | 2019 |
| Modular architecture for starcraft ii with deep reinforcement learning D Lee, H Tang, JO Zhang, H Xu, T Darrell, P Abbeel Fourteenth Artificial Intelligence and Interactive Digital Entertainment …, 2018 | 43 | 2018 |
| Systems and methods for robotic picking Y Duan, X Chen, M Rohaninejad, N Mishra, YX Liu, AA Vaziri, T Haoran, ... US Patent App. 17/014,545, 2021 | 2 | 2021 |
| Hierarchical deep reinforcement learning agent with counter self-play on competitive games H Xu, K Paster, Q Chen, H Tang, P Abbeel, T Darrell, S Levine | 2 | 2018 |
| Trajectory optimization using neural networks T Haoran, X Chen, Y Duan, N Mishra, S Wu, M Sieb, Y Shentu US Patent App. 17/193,870, 2021 | | 2021 |
| Training artificial networks for robotic picking Y Duan, T Haoran, Y Shentu, N Mishra, X Chen US Patent App. 17/014,558, 2021 | | 2021 |
| Towards Informed Exploration for Deep Reinforcement Learning H Tang University of California, Berkeley, 2019 | | 2019 |
Pieter AbbeelUC Berkeley | Covariant.AIVerified email at cs.berkeley.edu
