| Multi-agent actor-critic for mixed cooperative-competitive environments R Lowe, YI Wu, A Tamar, J Harb, OAI Pieter Abbeel, I Mordatch Advances in neural information processing systems 30, 2017 | 5678 | 2017 |
| Constrained policy optimization J Achiam, D Held, A Tamar, P Abbeel International conference on machine learning, 22-31, 2017 | 1650 | 2017 |
| Value iteration networks A Tamar, Y Wu, G Thomas, S Levine, P Abbeel Advances in neural information processing systems 29, 2016 | 784 | 2016 |
| Bayesian reinforcement learning: A survey M Ghavamzadeh, S Mannor, J Pineau, A Tamar Foundations and Trends® in Machine Learning 8 (5-6), 359-483, 2015 | 589 | 2015 |
| Model-ensemble trust-region policy optimization T Kurutach, I Clavera, Y Duan, A Tamar, P Abbeel arXiv preprint arXiv:1802.10592, 2018 | 563 | 2018 |
| Risk-sensitive and robust decision-making: a cvar optimization approach Y Chow, A Tamar, S Mannor, M Pavone Advances in neural information processing systems 28, 2015 | 403 | 2015 |
| A deep reinforcement learning perspective on internet congestion control N Jay, N Rotman, B Godfrey, M Schapira, A Tamar International Conference on Machine Learning, 3050-3059, 2019 | 377 | 2019 |
| Policy gradients with variance related risk criteria A Tamar, D Di Castro, S Mannor Proceedings of the twenty-ninth international conference on machine learning …, 2012 | 362 | 2012 |
| Learning to route A Valadarsky, M Schapira, D Shahaf, A Tamar Proceedings of the 16th ACM workshop on hot topics in networks, 185-191, 2017 | 275 | 2017 |
| Variance adjusted actor critic algorithms A Tamar, S Mannor arXiv preprint arXiv:1310.3697, 2013 | 249 | 2013 |
| Reinforcement learning on variable impedance controller for high-precision robotic assembly J Luo, E Solowjow, C Wen, JA Ojea, AM Agogino, A Tamar, P Abbeel 2019 International Conference on Robotics and Automation (ICRA), 3080-3087, 2019 | 231 | 2019 |
| Optimizing the CVaR via sampling A Tamar, Y Glassner, S Mannor Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 222 | 2015 |
| Learning plannable representations with causal infogan T Kurutach, A Tamar, G Yang, SJ Russell, P Abbeel Advances in Neural Information Processing Systems 31, 2018 | 218 | 2018 |
| Policy gradients beyond expectations: Conditional value-at-risk A Tamar, Y Glassner, S Mannor AAAI, 2015 | 198 | 2015 |
| Learning robotic assembly from cad G Thomas, M Chien, A Tamar, JA Ojea, P Abbeel 2018 IEEE International Conference on Robotics and Automation (ICRA), 3524-3531, 2018 | 194 | 2018 |
| Scaling up robust MDPs using function approximation A Tamar, S Mannor, H Xu International conference on machine learning, 181-189, 2014 | 162 | 2014 |
| Learning robotic manipulation through visual planning and acting A Wang, T Kurutach, K Liu, P Abbeel, A Tamar arXiv preprint arXiv:1905.04411, 2019 | 159 | 2019 |
| Policy gradient for coherent risk measures A Tamar, Y Chow, M Ghavamzadeh, S Mannor Advances in neural information processing systems 28, 2015 | 153 | 2015 |
| Learning generalized reactive policies using deep neural networks E Groshev, M Goldstein, A Tamar, S Srivastava, P Abbeel Proceedings of the International Conference on Automated Planning and …, 2018 | 141 | 2018 |
| Deep residual flow for out of distribution detection E Zisselman, A Tamar Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 126 | 2020 |
Pieter AbbeelUC Berkeley | Covariantcs.berkeley.edu의 이메일 확인됨
