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 | 2499 | 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 | 2283 | 2018 |
The starcraft multi-agent challenge M Samvelyan, T Rashid, CS De Witt, G Farquhar, N Nardelli, TGJ Rudner, ... arXiv preprint arXiv:1902.04043, 2019 | 1043 | 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 | 758 | 2017 |
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 | 362 | 2020 |
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 | 306 | 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 | 150 | 2017 |
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, 2019 | 114* | 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 | 95 | 2018 |
Transient non-stationarity and generalisation in deep reinforcement learning M Igl, G Farquhar, J Luketina, W Boehmer, S Whiteson arXiv preprint arXiv:2006.05826, 2020 | 80 | 2020 |
Growing action spaces G Farquhar, L Gustafson, Z Lin, S Whiteson, N Usunier, G Synnaeve International Conference on Machine Learning, 3040-3051, 2020 | 36 | 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 8, 2020 | 35 | 2020 |
Proper value equivalence C Grimm, A Barreto, G Farquhar, D Silver, S Singh Advances in neural information processing systems 34, 7773-7786, 2021 | 34 | 2021 |
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 International Conference on Machine Learning, 3305-3317, 2021 | 30 | 2021 |
Self-consistent models and values G Farquhar, K Baumli, Z Marinho, A Filos, M Hessel, HP van Hasselt, ... Advances in Neural Information Processing Systems 34, 1111-1125, 2021 | 14 | 2021 |
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 | 12 | 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 | 11 | 2017 |
Model-value inconsistency as a signal for epistemic uncertainty A Filos, E Vértes, Z Marinho, G Farquhar, D Borsa, A Friesen, ... arXiv preprint arXiv:2112.04153, 2021 | 10 | 2021 |
Loaded DiCE: Trading off bias and variance in any-order score function gradient estimators for reinforcement learning G Farquhar, S Whiteson, J Foerster Advances in Neural Information Processing Systems 32, 2019 | 10 | 2019 |
Discovering general reinforcement learning algorithms with adversarial environment design MT Jackson, M Jiang, J Parker-Holder, R Vuorio, C Lu, G Farquhar, ... Advances in Neural Information Processing Systems 36, 2024 | 6 | 2024 |