| Mastering the game of Go with deep neural networks and tree search D Silver, A Huang, CJ Maddison, A Guez, L Sifre, G Van Den Driessche, ... nature 529 (7587), 484-489, 2016 | 13227 | 2016 |
| Mastering the game of go without human knowledge D Silver, J Schrittwieser, K Simonyan, I Antonoglou, A Huang, A Guez, ... nature 550 (7676), 354-359, 2017 | 7397 | 2017 |
| Deep reinforcement learning with double q-learning H Van Hasselt, A Guez, D Silver Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 5170 | 2016 |
| A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... Science 362 (6419), 1140-1144, 2018 | 2351 | 2018 |
| Mastering chess and shogi by self-play with a general reinforcement learning algorithm D Silver, T Hubert, J Schrittwieser, I Antonoglou, M Lai, A Guez, M Lanctot, ... arXiv preprint arXiv:1712.01815, 2017 | 1411 | 2017 |
| Mastering atari, go, chess and shogi by planning with a learned model J Schrittwieser, I Antonoglou, T Hubert, K Simonyan, L Sifre, S Schmitt, ... Nature 588 (7839), 604-609, 2020 | 913 | 2020 |
| Imagination-augmented agents for deep reinforcement learning S Racanière, T Weber, D Reichert, L Buesing, A Guez, ... Advances in neural information processing systems 30, 2017 | 317 | 2017 |
| The predictron: End-to-end learning and planning D Silver, H Hasselt, M Hessel, T Schaul, A Guez, T Harley, ... International Conference on Machine Learning, 3191-3199, 2017 | 235 | 2017 |
| Imagination-augmented agents for deep reinforcement learning T Weber, S Racaniere, DP Reichert, L Buesing, A Guez, DJ Rezende, ... arXiv preprint arXiv:1707.06203, 2017 | 207 | 2017 |
| Efficient Bayes-adaptive reinforcement learning using sample-based search A Guez, D Silver, P Dayan Advances in neural information processing systems 25, 2012 | 150 | 2012 |
| Learning values across many orders of magnitude HP van Hasselt, A Guez, M Hessel, V Mnih, D Silver Advances In Neural Information Processing Systems, 4287-4295, 2016 | 136 | 2016 |
| Increasing the action gap: New operators for reinforcement learning MG Bellemare, G Ostrovski, A Guez, P Thomas, R Munos Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 126 | 2016 |
| Adaptive Treatment of Epilepsy via Batch-mode Reinforcement Learning. A Guez, RD Vincent, M Avoli, J Pineau AAAI, 1671-1678, 2008 | 103 | 2008 |
| Woulda, coulda, shoulda: Counterfactually-guided policy search L Buesing, T Weber, Y Zwols, S Racaniere, A Guez, JB Lespiau, N Heess arXiv preprint arXiv:1811.06272, 2018 | 86 | 2018 |
| Sifre L van den Driessche G Schrittwieser J Antonoglou I Panneershelvam V Lanctot M Dieleman S Grewe D Nham J Kalchbrenner N Sutskever I Lillicrap T Leach M Kavukcuoglu K … SDHAM CJ, A Guez Nature 529 (7587), 484, 2016 | 80 | 2016 |
| Scalable and efficient Bayes-adaptive reinforcement learning based on Monte-Carlo tree search A Guez, D Silver, P Dayan Journal of Artificial Intelligence Research 48, 841-883, 2013 | 78 | 2013 |
| Treating epilepsy via adaptive neurostimulation: a reinforcement learning approach J Pineau, A Guez, R Vincent, G Panuccio, M Avoli International journal of neural systems 19 (04), 227-240, 2009 | 77 | 2009 |
| Learning to search with mctsnets A Guez, T Weber, I Antonoglou, K Simonyan, O Vinyals, D Wierstra, ... International conference on machine learning, 1822-1831, 2018 | 73 | 2018 |
| An investigation of model-free planning A Guez, M Mirza, K Gregor, R Kabra, S Racanière, T Weber, D Raposo, ... International Conference on Machine Learning, 2464-2473, 2019 | 60 | 2019 |
| Adaptive control of epileptiform excitability in an in vitro model of limbic seizures G Panuccio, A Guez, R Vincent, M Avoli, J Pineau Experimental neurology 241, 179-183, 2013 | 32 | 2013 |
David SilverDeepMind, UCLgoogle.com의 이메일 확인됨
