| 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 | 618 | 2018 |
| Qmix: Monotonic value function factorisation for deep multi-agent reinforcement learning T Rashid, M Samvelyan, C Schroeder, G Farquhar, J Foerster, S Whiteson International Conference on Machine Learning, 4295-4304, 2018 | 334 | 2018 |
| 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 | 329 | 2017 |
| The starcraft multi-agent challenge M Samvelyan, T Rashid, CS De Witt, G Farquhar, N Nardelli, TGJ Rudner, ... arXiv preprint arXiv:1902.04043, 2019 | 106 | 2019 |
| Treeqn and atreec: Differentiable tree-structured models for deep reinforcement learning G Farquhar, T Rocktäschel, M Igl, S Whiteson arXiv preprint arXiv:1710.11417, 2017 | 78 | 2017 |
| 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 | 54 | 2019 |
| Dice: The infinitely differentiable monte carlo estimator J Foerster, G Farquhar, M Al-Shedivat, T Rocktäschel, E Xing, S Whiteson International Conference on Machine Learning, 1529-1538, 2018 | 37 | 2018 |
| Multi-agent common knowledge reinforcement learning C Schroeder de Witt, J Foerster, G Farquhar, P Torr, W Boehmer, ... Advances in Neural Information Processing Systems 32, 9927-9939, 2019 | 35 | 2019 |
| Counterfactual multi-agent policy gradients. CoRR abs/1705.08926 (2017) JN Foerster, G Farquhar, T Afouras, N Nardelli, S Whiteson arXiv preprint arXiv:1705.08926, 2017 | 8 | 2017 |
| Weighted QMIX: Expanding Monotonic Value Function Factorisation T Rashid, G Farquhar, B Peng, S Whiteson arXiv preprint arXiv:2006.10800, 2020 | 7 | 2020 |
| The impact of non-stationarity on generalisation in deep reinforcement learning M Igl, G Farquhar, J Luketina, W Boehmer, S Whiteson arXiv preprint arXiv:2006.05826, 2020 | 7 | 2020 |
| A baseline for any order gradient estimation in stochastic computation graphs J Mao, J Foerster, T Rocktäschel, M Al-Shedivat, G Farquhar, S Whiteson International Conference on Machine Learning, 4343-4351, 2019 | 6 | 2019 |
| Growing action spaces G Farquhar, L Gustafson, Z Lin, S Whiteson, N Usunier, G Synnaeve International Conference on Machine Learning, 3040-3051, 2020 | 4 | 2020 |
| Loaded dice: Trading off bias and variance in any-order score function gradient estimators for reinforcement learning G Farquhar, S Whiteson, J Foerster | 2 | 2019 |
| Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Estimators for Reinforcement Learning G Farquhar, S Whiteson, J Foerster arXiv preprint arXiv:1909.10549, 2019 | 1 | 2019 |
| Multi-agent common knowledge reinforcement learning CA Schroeder, J Foerster, G Farquhar, P Torr, W Boehmer, S Whiteson | 1 | 2019 |
| PsiPhi-Learning: Reinforcement Learning with Demonstrations using Successor Features and Inverse Temporal Difference Learning A Filos, C Lyle, Y Gal, S Levine, N Jaques, G Farquhar arXiv preprint arXiv:2102.12560, 2021 | | 2021 |
| Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi− Agent Reinforcement Learning S Whiteson | | 2020 |
| 35TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING, ICML 2018 T Rashid, CS De Witt, G Farquhar, J Foerster, S Whiteson, M Samvelyan, ... | | 2018 |
| TRANSIENT NON-STATIONARITY AND GENERALISA-TION IN DEEP REINFORCEMENT LEARNING M Igl, G Farquhar, J Luketina, W Böhmer, S Whiteson | | |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Head of Research, Waymo UKVahvistettu sähköpostiosoite verkkotunnuksessa cs.ox.ac.uk
