| Learning to communicate with deep multi-agent reinforcement learning J Foerster, IA Assael, N De Freitas, S Whiteson Advances in neural information processing systems, 2137-2145, 2016 | 532 | 2016 |
| Counterfactual Multi-Agent Policy Gradients J Foerster, G Farquhar, T Afouras, N Nardelli, S Whiteson AAAI 2018, Outstanding Student Paper Award, 2017 | 409 | 2017 |
| Stabilising experience replay for deep multi-agent reinforcement learning J Foerster, N Nardelli, G Farquhar, P Torr, P Kohli, S Whiteson International Conference on Machine Learning, 2017, 2017 | 240 | 2017 |
| Learning with opponent-learning awareness J Foerster, RY Chen, M Al-Shedivat, S Whiteson, P Abbeel, I Mordatch Proceedings of the 17th International Conference on Autonomous Agents and …, 2018 | 191 | 2018 |
| QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning T Rashid, M Samvelyan, CS de Witt, G Farquhar, J Foerster, S Whiteson Proceedings of the 35th International Conference on Machine Learning, 2018 | 183 | 2018 |
| Three-dimensional head-direction coding in the bat brain A Finkelstein, D Derdikman, A Rubin, JN Foerster, L Las, N Ulanovsky Nature 517 (7533), 159-164, 2015 | 128 | 2015 |
| Hotword recognition M Sharifi, JN Foerster US Patent 9,747,926, 2017 | 126 | 2017 |
| The Mechanics of n-Player Differentiable Games D Balduzzi, S Racaniere, J Martens, J Foerster, K Tuyls, T Graepel Proceedings of the 35th International Conference on Machine Learning, 2018 | 102 | 2018 |
| Learning to communicate to solve riddles with deep distributed recurrent q-networks JN Foerster, YM Assael, N de Freitas, S Whiteson IJCAI 2016 Deep Learning Workshop, 2016 | 79 | 2016 |
| Control of vocal and respiratory patterns in birdsong: dissection of forebrain and brainstem mechanisms using temperature AS Andalman, JN Foerster, MS Fee PloS one 6 (9), e25461, 2011 | 57 | 2011 |
| The hanabi challenge: A new frontier for ai research N Bard, JN Foerster, S Chandar, N Burch, M Lanctot, HF Song, E Parisotto, ... Artificial Intelligence 280, 103216, 2020 | 43 | 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 | 43 | 2019 |
| Pommerman: A multi-agent playground C Resnick, W Eldridge, D Ha, D Britz, J Foerster, J Togelius, K Cho, ... NeurIPS 2018 Competition track, 2018 | 39 | 2018 |
| Hotword detection on multiple devices JN Foerster, AH Gruenstein US Patent 9,424,841, 2016 | 39 | 2016 |
| Bayesian action decoder for deep multi-agent reinforcement learning JN Foerster, F Song, E Hughes, N Burch, I Dunning, S Whiteson, ... ICML 2019, 2018 | 34 | 2018 |
| Input switched affine networks: An rnn architecture designed for interpretability JN Foerster, J Gilmer, J Sohl-Dickstein, J Chorowski, D Sussillo International Conference on Machine Learning, 1136-1145, 2017 | 32* | 2017 |
| On the pitfalls of measuring emergent communication R Lowe, J Foerster, YL Boureau, J Pineau, Y Dauphin arXiv preprint arXiv:1903.05168, 2019 | 31 | 2019 |
| DiCE: The Infinitely Differentiable Monte-Carlo Estimator J Foerster, G Farquhar, M Al-Shedivat, T Rocktäschel, EP Xing, ... Proceedings of the 35th International Conference on Machine Learning, 2018 | 27 | 2018 |
| Stable Opponent Shaping in Differentiable Games A Letcher, J Foerster, D Balduzzi, T Rocktäschel, S Whiteson Proceedings International Conference on Learning Representations 2019, 2018 | 23 | 2018 |
| Multi-agent common knowledge reinforcement learning CA Schroeder de Witt, JN Foerster, G Farquhar, PHS Torr, W Boehmer, ... arXiv, arXiv: 1810.11702, 2018 | 23* | 2018 |
Shimon WhitesonProfessor of Computer Science, University of OxfordVerified email at cs.ox.ac.uk
