Paul Muller
Paul Muller
Research Scientist, Deepmind
Verified email at
Cited by
Cited by
OpenSpiel: A framework for reinforcement learning in games
M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ...
arXiv preprint arXiv:1908.09453, 2019
Mastering the game of Stratego with model-free multiagent reinforcement learning
J Perolat, B De Vylder, D Hennes, E Tarassov, F Strub, V de Boer, ...
Science 378 (6623), 990-996, 2022
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, ...
ICLR2020, 2019
Game Plan: What AI can do for Football, and What Football can do for AI
K Tuyls, S Omidshafiei, P Muller, Z Wang, J Connor, D Hennes, I Graham, ...
Journal of Artificial Intelligence Research 71, 41-88, 2021
Navigating the landscape of multiplayer games
S Omidshafiei, K Tuyls, WM Czarnecki, FC Santos, M Rowland, J Connor, ...
Nature communications 11 (1), 5603, 2020
Multi-agent training beyond zero-sum with correlated equilibrium meta-solvers
L Marris, P Muller, M Lanctot, K Tuyls, T Graepel
International Conference on Machine Learning, 7480-7491, 2021
Scalable deep reinforcement learning algorithms for mean field games
M Laurière, S Perrin, S Girgin, P Muller, A Jain, T Cabannes, G Piliouras, ...
International Conference on Machine Learning, 12078-12095, 2022
Learning equilibria in mean-field games: Introducing mean-field PSRO
P Muller, M Rowland, R Elie, G Piliouras, J Perolat, M Lauriere, R Marinier, ...
arXiv preprint arXiv:2111.08350, 2021
Multiagent off-screen behavior prediction in football
S Omidshafiei, D Hennes, M Garnelo, Z Wang, A Recasens, E Tarassov, ...
Scientific reports 12 (1), 8638, 2022
Combining tree-search, generative models, and Nash bargaining concepts in game-theoretic reinforcement learning
Z Li, M Lanctot, KR McKee, L Marris, I Gemp, D Hennes, P Muller, ...
arXiv preprint arXiv:2302.00797, 2023
Learning correlated equilibria in mean-field games
P Muller, R Elie, M Rowland, M Lauriere, J Perolat, S Perrin, M Geist, ...
arXiv preprint arXiv:2208.10138, 2022
Temporal difference and return optimism in cooperative multi-agent reinforcement learning
M Rowland, S Omidshafiei, D Hennes, W Dabney, A Jaegle, P Muller, ...
Workshop on Adaptive Learning Agents (ALA) at AAMAS, 2021
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
Time-series imputation of temporally-occluded multiagent trajectories
S Omidshafiei, D Hennes, M Garnelo, E Tarassov, Z Wang, R Elie, ...
arXiv preprint arXiv:2106.04219, 2021
Search-improved game-theoretic multiagent reinforcement learning in general and negotiation games
Z Li, M Lanctot, KR McKee, L Marris, I Gemp, D Hennes, K Larson, ...
Proceedings of the 2023 International Conference on Autonomous Agents and …, 2023
Jointly updating agent control policies using estimated best responses to current control policies
LC Marris, PFM Muller, M Lanctot, TKH Graepel
US Patent App. 18/275,881, 2024
Automated offset well analysis
C Jeong, FJ Gomez, M Ringer, P Bolchover, P Muller
US Patent App. 18/354,017, 2023
Automated offset well analysis
C Jeong, FJ Gomez, M Ringer, P Bolchover, P Muller
US Patent 11,747,502, 2023
Controller optimization via reinforcement learning on asset avatar
Z Li, P Muller, P Nirgudkar
US Patent 11,674,384, 2023
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