| 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 coordination graphs W Böhmer, V Kurin, S Whiteson International Conference on Machine Learning, 980-991, 2020 | 247 | 2020 |
| 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 | 132 | 2020 |
| 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 | 126 | 2019 |
| Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control C Schroeder de Witt, B Peng, PA Kamienny, P Torr, W Böhmer, ... arXiv e-prints, arXiv: 2003.06709, 2020 | 118* | 2020 |
| Randomized entity-wise factorization for multi-agent reinforcement learning S Iqbal, CAS De Witt, B Peng, W Böhmer, S Whiteson, F Sha International Conference on Machine Learning, 4596-4606, 2021 | 116 | 2021 |
| My body is a cage: the role of morphology in graph-based incompatible control V Kurin, M Igl, T Rocktäschel, W Boehmer, S Whiteson arXiv preprint arXiv:2010.01856, 2020 | 109 | 2020 |
| Autonomous learning of state representations for control: An emerging field aims to autonomously learn state representations for reinforcement learning agents from their real … W Böhmer, JT Springenberg, J Boedecker, M Riedmiller, K Obermayer KI-Künstliche Intelligenz 29 (4), 353-362, 2015 | 98 | 2015 |
| Uneven: Universal value exploration for multi-agent reinforcement learning T Gupta, A Mahajan, B Peng, W Böhmer, S Whiteson International Conference on Machine Learning, 3930-3941, 2021 | 69 | 2021 |
| Optimistic exploration even with a pessimistic initialisation T Rashid, B Peng, W Boehmer, S Whiteson arXiv preprint arXiv:2002.12174, 2020 | 61 | 2020 |
| Generalized off-policy actor-critic S Zhang, W Boehmer, S Whiteson Advances in neural information processing systems 32, 2019 | 60 | 2019 |
| The effect of novelty on reinforcement learning A Houillon, RC Lorenz, W Böhmer, MA Rapp, A Heinz, J Gallinat, ... Progress in brain research 202, 415-439, 2013 | 58 | 2013 |
| Neural systems for choice and valuation with counterfactual learning signals MJ Tobia, R Guo, U Schwarze, W Böhmer, J Gläscher, B Finckh, ... Neuroimage 89, 57-69, 2014 | 51 | 2014 |
| 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 | 39 | 2020 |
| Exploration with unreliable intrinsic reward in multi-agent reinforcement learning W Böhmer, T Rashid, S Whiteson arXiv preprint arXiv:1906.02138, 2019 | 36 | 2019 |
| Multitask soft option learning M Igl, A Gambardella, J He, N Nardelli, N Siddharth, W Böhmer, ... Conference on uncertainty in artificial intelligence, 969-978, 2020 | 35 | 2020 |
| Deep residual reinforcement learning S Zhang, W Boehmer, S Whiteson arXiv preprint arXiv:1905.01072, 2019 | 34 | 2019 |
| Construction of approximation spaces for reinforcement learning W Böhmer, S Grünewälder, Y Shen, M Musial, K Obermayer The Journal of Machine Learning Research 14 (1), 2067-2118, 2013 | 33 | 2013 |
| Multi-agent common knowledge reinforcement learning CAS de Witt, JN Foerster, G Farquhar, PHS Torr, W Boehmer, S Whiteson arXiv preprint arXiv:1810.11702, 2018 | 31 | 2018 |
| Ai-qmix: Attention and imagination for dynamic multi-agent reinforcement learning S Iqbal, CAS de Witt, B Peng, W Böhmer, S Whiteson, F Sha arXiv preprint arXiv:2006.04222, 2020 | 27 | 2020 |
Shimon WhitesonProfessor of Computer Science, University of Oxford / Senior Staff Research Scientist, WaymoVerified email at cs.ox.ac.uk
