| 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 | 3803 | 2020 |
| 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 | 3086 | 2018 |
| 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 | 2549 | 2016 |
| The starcraft multi-agent challenge M Samvelyan, T Rashid, CS De Witt, G Farquhar, N Nardelli, TGJ Rudner, ... arXiv preprint arXiv:1902.04043, 2019 | 1544 | 2019 |
| 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 | 987 | 2014 |
| 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 | 896 | 2017 |
| Learning with opponent-learning awareness JN Foerster, RY Chen, M Al-Shedivat, S Whiteson, P Abbeel, I Mordatch arXiv preprint arXiv:1709.04326, 2017 | 753 | 2017 |
| 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 | 615 | 2020 |
| Lipnet: End-to-end sentence-level lipreading YM Assael, B Shillingford, S Whiteson, N De Freitas arXiv preprint arXiv:1611.01599, 2016 | 602 | 2016 |
| 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 | 561 | 2020 |
| Maven: Multi-agent variational exploration A Mahajan, T Rashid, M Samvelyan, S Whiteson Advances in neural information processing systems 32, 2019 | 544 | 2019 |
| Fast context adaptation via meta-learning L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson International conference on machine learning, 7693-7702, 2019 | 529 | 2019 |
| 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 | 401 | 2021 |
| Deep variational reinforcement learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson International conference on machine learning, 2117-2126, 2018 | 391 | 2018 |
| Evolutionary Function Approximation for Reinforcement Learning S Whiteson, P Stone Journal of Machine Learning Research 7, 877-917, 2006 | 375 | 2006 |
| 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 | 372 | 2019 |
| 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 | 367 | 2019 |
| 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 | 338 | 2009 |
| Multiagent reinforcement learning for urban traffic control using coordination graphs L Kuyer, S Whiteson, B Bakker, N Vlassis Joint European conference on machine learning and knowledge discovery in …, 2008 | 335 | 2008 |
| 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 | 322 | 2020 |
Jakob FoersterAssociate Professor, University of OxfordVerified email at eng.ox.ac.uk
