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Shimon Whiteson
Shimon Whiteson
Professor of Computer Science, University of Oxford / Senior Staff Research Scientist, Waymo
Подтвержден адрес электронной почты в домене cs.ox.ac.uk - Главная страница
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Процитировано
Процитировано
Год
Monotonic value function factorisation for deep multi-agent reinforcement learning
T Rashid, M Samvelyan, CS De Witt, G Farquhar, J Foerster, S Whiteson
Journal of Machine Learning Research 21 (178), 1-51, 2020
26662020
Counterfactual multi-agent policy gradients
J Foerster, G Farquhar, T Afouras, N Nardelli, S Whiteson
Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018
23912018
Learning to communicate with deep multi-agent reinforcement learning
J Foerster, IA Assael, N De Freitas, S Whiteson
Advances in neural information processing systems 29, 2016
21562016
The starcraft multi-agent challenge
M Samvelyan, T Rashid, CS De Witt, G Farquhar, N Nardelli, TGJ Rudner, ...
arXiv preprint arXiv:1902.04043, 2019
11292019
A survey of multi-objective sequential decision-making
DM Roijers, P Vamplew, S Whiteson, R Dazeley
Journal of Artificial Intelligence Research 48, 67-113, 2014
7972014
Stabilising experience replay for deep multi-agent reinforcement learning
J Foerster, N Nardelli, G Farquhar, T Afouras, PHS Torr, P Kohli, ...
International conference on machine learning, 1146-1155, 2017
7802017
Learning with opponent-learning awareness
JN Foerster, RY Chen, M Al-Shedivat, S Whiteson, P Abbeel, I Mordatch
arXiv preprint arXiv:1709.04326, 2017
6412017
Lipnet: End-to-end sentence-level lipreading
YM Assael, B Shillingford, S Whiteson, N De Freitas
arXiv preprint arXiv:1611.01599, 2016
4842016
Fast context adaptation via meta-learning
L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson
International Conference on Machine Learning, 7693-7702, 2019
4432019
Maven: Multi-agent variational exploration
A Mahajan, T Rashid, M Samvelyan, S Whiteson
Advances in neural information processing systems 32, 2019
4362019
Weighted qmix: Expanding monotonic value function factorisation for deep multi-agent reinforcement learning
T Rashid, G Farquhar, B Peng, S Whiteson
Advances in neural information processing systems 33, 10199-10210, 2020
3782020
Is independent learning all you need in the starcraft multi-agent challenge?
CS De Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, M Sun, ...
arXiv preprint arXiv:2011.09533, 2020
3642020
Evolutionary Function Approximation for Reinforcement Learning
S Whiteson, P Stone
Journal of Machine Learning Research 7, 877-917, 2006
3622006
Deep variational reinforcement learning for POMDPs
M Igl, L Zintgraf, TA Le, F Wood, S Whiteson
International conference on machine learning, 2117-2126, 2018
3312018
A survey of reinforcement learning informed by natural language
J Luketina, N Nardelli, G Farquhar, J Foerster, J Andreas, E Grefenstette, ...
arXiv preprint arXiv:1906.03926, 2019
3212019
Multiagent reinforcement learning for urban traffic control using coordination graphs
L Kuyer, S Whiteson, B Bakker, N Vlassis
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2008
3212008
A theoretical and empirical analysis of expected sarsa
H Van Seijen, H Van Hasselt, S Whiteson, M Wiering
2009 ieee symposium on adaptive dynamic programming and reinforcement …, 2009
2992009
Varibad: A very good method for bayes-adaptive deep rl via meta-learning
L Zintgraf, K Shiarlis, M Igl, S Schulze, Y Gal, K Hofmann, S Whiteson
arXiv preprint arXiv:1910.08348, 2019
2892019
Facmac: Factored multi-agent centralised policy gradients
B Peng, T Rashid, C Schroeder de Witt, PA Kamienny, P Torr, W Böhmer, ...
Advances in Neural Information Processing Systems 34, 12208-12221, 2021
2372021
Rode: Learning roles to decompose multi-agent tasks
T Wang, T Gupta, A Mahajan, B Peng, S Whiteson, C Zhang
arXiv preprint arXiv:2010.01523, 2020
2262020
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Статьи 1–20