| 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 | 1682 | 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 | 1650 | 2016 |
| Monotonic value function factorisation for deep multi-agent reinforcement learning T Rashid, M Samvelyan, CS De Witt, G Farquhar, J Foerster, S Whiteson The Journal of Machine Learning Research 21 (1), 7234-7284, 2020 | 1584 | 2020 |
| The starcraft multi-agent challenge M Samvelyan, T Rashid, CS De Witt, G Farquhar, N Nardelli, TGJ Rudner, ... arXiv preprint arXiv:1902.04043, 2019 | 655 | 2019 |
| 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 | 637 | 2017 |
| 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 | 611 | 2014 |
| Learning with opponent-learning awareness JN Foerster, RY Chen, M Al-Shedivat, S Whiteson, P Abbeel, I Mordatch arXiv preprint arXiv:1709.04326, 2017 | 501 | 2017 |
| Lipnet: End-to-end sentence-level lipreading YM Assael, B Shillingford, S Whiteson, N De Freitas arXiv preprint arXiv:1611.01599, 2016 | 376 | 2016 |
| Evolutionary Function Approximation for Reinforcement Learning S Whiteson, P Stone Journal of Machine Learning Research 7, 877-917, 2006 | 353 | 2006 |
| Fast context adaptation via meta-learning L Zintgraf, K Shiarli, V Kurin, K Hofmann, S Whiteson International Conference on Machine Learning, 7693-7702, 2019 | 333 | 2019 |
| Maven: Multi-agent variational exploration A Mahajan, T Rashid, M Samvelyan, S Whiteson Advances in neural information processing systems 32, 2019 | 286 | 2019 |
| 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 | 282 | 2008 |
| Deep variational reinforcement learning for POMDPs M Igl, L Zintgraf, TA Le, F Wood, S Whiteson International Conference on Machine Learning, 2117-2126, 2018 | 253 | 2018 |
| 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 | 240 | 2009 |
| 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 | 221 | 2019 |
| 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 | 216 | 2020 |
| 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 | 173 | 2019 |
| Lipnet: Sentence-level lipreading YM Assael, B Shillingford, S Whiteson, N De Freitas arXiv preprint arXiv:1611.01599 2 (4), 2016 | 164 | 2016 |
| Balancing exploration and exploitation in listwise and pairwise online learning to rank for information retrieval K Hofmann, S Whiteson, M de Rijke Information Retrieval 16, 63-90, 2013 | 160 | 2013 |
| Learning to communicate to solve riddles with deep distributed recurrent q-networks JN Foerster, YM Assael, N de Freitas, S Whiteson arXiv preprint arXiv:1602.02672, 2016 | 157 | 2016 |
Jakob FoersterAssociate Professor, University of Oxford在 eng.ox.ac.uk 的电子邮件经过验证
