| Continuous control with deep reinforcement learning TP Lillicrap, JJ Hunt, A Pritzel, N Heess, T Erez, Y Tassa, D Silver, ... arXiv preprint arXiv:1509.02971, 2015 | 13880 | 2015 |
| MuJoCo: A physics engine for model-based control E Todorov, T Erez, Y Tassa IROS, 2012 | 4693 | 2012 |
| Emergence of locomotion behaviours in rich environments N Heess, D Tb, S Sriram, J Lemmon, J Merel, G Wayne, Y Tassa, T Erez, ... arXiv preprint arXiv:1707.02286, 2017 | 1000 | 2017 |
| Synthesis and Stabilization of Complex Behaviors through Online Trajectory Optimization Y Tassa, T Erez, E Todorov IROS 2012, 2012 | 865 | 2012 |
| Learning continuous control policies by stochastic value gradients N Heess, G Wayne, D Silver, T Lillicrap, T Erez, Y Tassa Advances in neural information processing systems 28, 2015 | 603 | 2015 |
| Attend, infer, repeat: Fast scene understanding with generative models SM Eslami, N Heess, T Weber, Y Tassa, D Szepesvari, GE Hinton Advances in neural information processing systems 29, 2016 | 533 | 2016 |
| Control-limited differential dynamic programming Y Tassa, N Mansard, E Todorov 2014 IEEE International Conference on Robotics and Automation (ICRA), 1168-1175, 2014 | 493 | 2014 |
| Deepmind control suite Y Tassa, Y Doron, A Muldal, T Erez, Y Li, DL Casas, D Budden, ... arXiv preprint arXiv:1801.00690, 2018 | 450 | 2018 |
| Maximum a posteriori policy optimisation A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ... arXiv preprint arXiv:1806.06920, 2018 | 424 | 2018 |
| Diego de Las Casas, David Budden, Abbas Abdolmaleki, Josh Merel, Andrew Lefrancq, et al. Deepmind control suite Y Tassa, Y Doron, A Muldal, T Erez, Y Li arXiv preprint arXiv:1801.00690 1 (3), 5, 2018 | 403* | 2018 |
| Safe exploration in continuous action spaces G Dalal, K Dvijotham, M Vecerik, T Hester, C Paduraru, Y Tassa arXiv preprint arXiv:1801.08757, 2018 | 394 | 2018 |
| Simulation Tools for Model-Based Robotics: Comparison of Bullet, Havok, MuJoCo, ODE and PhysX T Erez, Y Tassa, E Todorov ICRA 2015, 0 | 350* | |
| Data-efficient deep reinforcement learning for dexterous manipulation I Popov, N Heess, T Lillicrap, R Hafner, G Barth-Maron, M Vecerik, ... arXiv preprint arXiv:1704.03073, 2017 | 284 | 2017 |
| Whole-body model-predictive control applied to the HRP-2 humanoid J Koenemann, A Del Prete, Y Tassa, E Todorov, O Stasse, M Bennewitz, ... 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2015 | 235 | 2015 |
| dm_control: Software and tasks for continuous control S Tunyasuvunakool, A Muldal, Y Doron, S Liu, S Bohez, J Merel, T Erez, ... Software Impacts 6, 100022, 2020 | 229 | 2020 |
| Learning and transfer of modulated locomotor controllers N Heess, G Wayne, Y Tassa, T Lillicrap, M Riedmiller, D Silver arXiv preprint arXiv:1610.05182, 2016 | 216 | 2016 |
| Learning human behaviors from motion capture by adversarial imitation J Merel, Y Tassa, D TB, S Srinivasan, J Lemmon, Z Wang, G Wayne, ... arXiv preprint arXiv:1707.02201, 2017 | 214 | 2017 |
| An integrated system for real-time model predictive control of humanoid robots T Erez, K Lowrey, Y Tassa, V Kumar, S Kolev, E Todorov 2013 13th IEEE-RAS International conference on humanoid robots (Humanoids …, 2013 | 168 | 2013 |
| Receding horizon differential dynamic programming Y Tassa, T Erez, W Smart Advances in neural information processing systems 20, 2007 | 159 | 2007 |
| Stochastic differential dynamic programming E Theodorou, Y Tassa, E Todorov Proceedings of the 2010 American Control Conference, 1125-1132, 2010 | 135 | 2010 |
Tom ErezResearcher, DeepMindVerified email at google.com
