| Magnetic control of tokamak plasmas through deep reinforcement learning J Degrave, F Felici, J Buchli, M Neunert, B Tracey, F Carpanese, T Ewalds, ... Nature 602 (7897), 414-419, 2022 | 933 | 2022 |
| Deepmind control suite Y Tassa, Y Doron, A Muldal, T Erez, Y Li, DL Casas, D Budden, ... arXiv preprint arXiv:1801.00690, 2018 | 623 | 2018 |
| Maximum a posteriori policy optimisation A Abdolmaleki, JT Springenberg, Y Tassa, R Munos, N Heess, ... arXiv preprint arXiv:1806.06920, 2018 | 557 | 2018 |
| Keep doing what worked: Behavioral modelling priors for offline reinforcement learning NY Siegel, JT Springenberg, F Berkenkamp, A Abdolmaleki, M Neunert, ... arXiv preprint arXiv:2002.08396, 2020 | 321 | 2020 |
| Acme: A research framework for distributed reinforcement learning MW Hoffman, B Shahriari, J Aslanides, G Barth-Maron, N Momchev, ... arXiv preprint arXiv:2006.00979, 2020 | 273 | 2020 |
| V-mpo: On-policy maximum a posteriori policy optimization for discrete and continuous control HF Song, A Abdolmaleki, JT Springenberg, A Clark, H Soyer, JW Rae, ... arXiv preprint arXiv:1909.12238, 2019 | 133 | 2019 |
| Robust reinforcement learning for continuous control with model misspecification DJ Mankowitz, N Levine, R Jeong, Y Shi, J Kay, A Abdolmaleki, ... arXiv preprint arXiv:1906.07516, 2019 | 131 | 2019 |
| From motor control to team play in simulated humanoid football S Liu, G Lever, Z Wang, J Merel, SMA Eslami, D Hennes, WM Czarnecki, ... Science Robotics 7 (69), eabo0235, 2022 | 128 | 2022 |
| Continuous-discrete reinforcement learning for hybrid control in robotics M Neunert, A Abdolmaleki, M Wulfmeier, T Lampe, T Springenberg, ... Conference on Robot Learning, 735-751, 2020 | 109 | 2020 |
| Beyond pick-and-place: Tackling robotic stacking of diverse shapes AX Lee, CM Devin, Y Zhou, T Lampe, K Bousmalis, JT Springenberg, ... 5th Annual Conference on Robot Learning, 2021 | 108 | 2021 |
| Model-based relative entropy stochastic search A Abdolmaleki, R Lioutikov, JR Peters, N Lau, L Pualo Reis, G Neumann Advances in Neural Information Processing Systems 28, 2015 | 104 | 2015 |
| A distributional view on multi-objective policy optimization A Abdolmaleki, S Huang, L Hasenclever, M Neunert, F Song, M Zambelli, ... International conference on machine learning, 11-22, 2020 | 90 | 2020 |
| Robocat: A self-improving foundation agent for robotic manipulation K Bousmalis, G Vezzani, D Rao, C Devin, AX Lee, M Bauza, T Davchev, ... arXiv preprint arXiv:2306.11706 1 (8), 2023 | 89 | 2023 |
| Relative entropy regularized policy iteration A Abdolmaleki, JT Springenberg, J Degrave, S Bohez, Y Tassa, D Belov, ... arXiv preprint arXiv:1812.02256, 2018 | 79 | 2018 |
| Value constrained model-free continuous control S Bohez, A Abdolmaleki, M Neunert, J Buchli, N Heess, R Hadsell arXiv preprint arXiv:1902.04623, 2019 | 75 | 2019 |
| Model-free trajectory optimization for reinforcement learning R Akrour, G Neumann, H Abdulsamad, A Abdolmaleki International Conference on Machine Learning, 2961-2970, 2016 | 54 | 2016 |
| Data-efficient hindsight off-policy option learning M Wulfmeier, D Rao, R Hafner, T Lampe, A Abdolmaleki, T Hertweck, ... International Conference on Machine Learning, 11340-11350, 2021 | 50 | 2021 |
| Imagined value gradients: Model-based policy optimization with tranferable latent dynamics models A Byravan, JT Springenberg, A Abdolmaleki, R Hafner, M Neunert, ... Conference on Robot Learning, 566-589, 2020 | 50 | 2020 |
| Deriving and improving cma-es with information geometric trust regions A Abdolmaleki, B Price, N Lau, LP Reis, G Neumann Proceedings of the Genetic and Evolutionary Computation Conference, 657-664, 2017 | 42 | 2017 |
| Compositional transfer in hierarchical reinforcement learning M Wulfmeier, A Abdolmaleki, R Hafner, JT Springenberg, M Neunert, ... arXiv preprint arXiv:1906.11228, 2019 | 41 | 2019 |
Martin RiedmillerDeepMindПодтвержден адрес электронной почты в домене google.com