Siqi Liu
Siqi Liu
DeepMind, University College of London
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Zitiert von
Zitiert von
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
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
Emergent Coordination Through Competition
S Liu, G Lever, J Merel, S Tunyasuvunakool, N Heess, T Graepel
International Conference on Learning Representations (ICLR 2019), 2019
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
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
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
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
Observational learning by reinforcement learning
D Borsa, B Piot, R Munos, O Pietquin
arXiv preprint arXiv:1706.06617, 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
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
NeuPL: Neural population learning
S Liu, L Marris, D Hennes, J Merel, N Heess, T Graepel
arXiv preprint arXiv:2202.07415, 2022
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
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, ...
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
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
dm_env: a Python interface for reinforcement learning environments
A Muldal, Y Doron, J Aslanides, T Harley, T Ward, S Liu
Developing, evaluating and scaling learning agents in multi-agent environments
I Gemp, T Anthony, Y Bachrach, A Bhoopchand, K Bullard, J Connor, ...
AI Communications 35 (4), 271-284, 2022
NfgTransformer: Equivariant Representation Learning for Normal-form Games
S Liu, L Marris, G Piliouras, I Gemp, N Heess
arXiv preprint arXiv:2402.08393, 2024
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
Transferring task goals via hierarchical reinforcement learning
S Xie, A Galashov, S Liu, S Hou, R Pascanu, N Heess, YW Teh
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