| Improved image captioning via policy gradient optimization of spider S Liu, Z Zhu, N Ye, S Guadarrama, K Murphy Proceedings of the IEEE international conference on computer vision, 873-881, 2017 | 533* | 2017 |
| 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 | 246 | 2020 |
| Emergent Coordination Through Competition S Liu, G Lever, J Merel, S Tunyasuvunakool, N Heess, T Graepel International Conference on Learning Representations (ICLR 2019), 2019 | 145 | 2019 |
| Hierarchical visuomotor control of humanoids J Merel, A Ahuja, V Pham, S Tunyasuvunakool, S Liu, D Tirumala, ... International Conference on Learning Representations (ICLR 2019), 2018 | 107 | 2018 |
| 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, ... International Conference on Learning Representations 2019, 2019 | 91 | 2019 |
| A generalized training approach for multiagent learning P Muller, S Omidshafiei, M Rowland, K Tuyls, J Perolat, S Liu, D Hennes, ... arXiv preprint arXiv:1909.12823, 2019 | 88 | 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 | 84 | 2022 |
| Observational learning by reinforcement learning D Borsa, B Piot, R Munos, O Pietquin arXiv preprint arXiv:1706.06617, 2017 | 60 | 2017 |
| Reinforcement learning agents acquire flocking and symbiotic behaviour in simulated ecosystems P Sunehag, G Lever, S Liu, J Merel, N Heess, JZ Leibo, E Hughes, ... Artificial life conference proceedings, 103-110, 2019 | 27 | 2019 |
| Pick your battles: Interaction graphs as population-level objectives for strategic diversity M Garnelo, WM Czarnecki, S Liu, D Tirumala, J Oh, G Gidel, ... arXiv preprint arXiv:2110.04041, 2021 | 19 | 2021 |
| Launchpad: a programming model for distributed machine learning research F Yang, G Barth-Maron, P Stańczyk, M Hoffman, S Liu, M Kroiss, A Pope, ... arXiv preprint arXiv:2106.04516, 2021 | 16 | 2021 |
| NeuPL: Neural population learning S Liu, L Marris, D Hennes, J Merel, N Heess, T Graepel arXiv preprint arXiv:2202.07415, 2022 | 14 | 2022 |
| The body is not a given: Joint agent policy learning and morphology evolution D Banarse, Y Bachrach, S Liu, C Fernando, N Heess, P Kohli, G Lever, ... | 13 | 2018 |
| Simplex Neural Population Learning: Any-Mixture Bayes-Optimality in Symmetric Zero-sum Games S Liu, M Lanctot, L Marris, N Heess International Conference on Machine Learning, 13793-13806, 2022 | 6 | 2022 |
| dm_env: a Python interface for reinforcement learning environments A Muldal, Y Doron, J Aslanides, T Harley, T Ward, S Liu | 5 | 2019 |
| Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers L Marris, I Gemp, T Anthony, A Tacchetti, S Liu, K Tuyls arXiv preprint arXiv:2210.09257, 2022 | 4 | 2022 |
| Revisiting Gaussian mixture critics in off-policy reinforcement learning: a sample-based approach B Shahriari, A Abdolmaleki, A Byravan, A Friesen, S Liu, JT Springenberg, ... arXiv preprint arXiv:2204.10256, 2022 | 3 | 2022 |
| Developing, evaluating and scaling learning agents in multi-agent environments I Gemp, T Anthony, Y Bachrach, A Bhoopchand, K Bullard, J Connor, ... AI Communications, 1-14, 2022 | 3 | 2022 |
| Transferring task goals via hierarchical reinforcement learning S Xie, A Galashov, S Liu, S Hou, R Pascanu, N Heess, YW Teh | 2 | 2018 |
Nicolas HeessDeepMindGeverifieerd e-mailadres voor google.com
