| Model-based hierarchical reinforcement learning and human action control M Botvinick, A Weinstein Philosophical Transactions of the Royal Society B: Biological Sciences 369 …, 2014 | 233 | 2014 |
| Reinforcement learning, efficient coding, and the statistics of natural tasks M Botvinick, A Weinstein, A Solway, A Barto Current opinion in behavioral sciences 5, 71-77, 2015 | 121 | 2015 |
| Sample-based planning for continuous action markov decision processes C Mansley, A Weinstein, M Littman Proceedings of the International Conference on Automated Planning and …, 2011 | 119 | 2011 |
| Intuitive physics learning in a deep-learning model inspired by developmental psychology LS Piloto, A Weinstein, P Battaglia, M Botvinick Nature human behaviour 6 (9), 1257-1267, 2022 | 118 | 2022 |
| Bandit-based planning and learning in continuous-action markov decision processes A Weinstein, M Littman Proceedings of the International Conference on Automated Planning and …, 2012 | 95 | 2012 |
| Human-centred mechanism design with Democratic AI R Koster, J Balaguer, A Tacchetti, A Weinstein, T Zhu, O Hauser, ... Nature Human Behaviour 6 (10), 1398-1407, 2022 | 86 | 2022 |
| Open-loop planning in large-scale stochastic domains A Weinstein, M Littman Proceedings of the AAAI Conference on Artificial Intelligence 27 (1), 1436-1442, 2013 | 49 | 2013 |
| Inferring the intentional states of autonomous virtual agents PC Pantelis, CL Baker, SA Cholewiak, K Sanik, A Weinstein, CC Wu, ... Cognition 130 (3), 360-379, 2014 | 43 | 2014 |
| Probing physics knowledge using tools from developmental psychology L Piloto, A Weinstein, D TB, A Ahuja, M Mirza, G Wayne, D Amos, C Hung, ... arXiv preprint arXiv:1804.01128, 2018 | 36 | 2018 |
| The cross-entropy method optimizes for quantiles S Goschin, A Weinstein, M Littman International Conference on Machine Learning, 1193-1201, 2013 | 18 | 2013 |
| Agency and rationality: Adopting the intentional stance toward evolved virtual agents. PC Pantelis, T Gerstner, K Sanik, A Weinstein, SA Cholewiak, G Kharkwal, ... Decision 3 (1), 40, 2016 | 14 | 2016 |
| Structure learning in motor control: A deep reinforcement learning model A Weinstein, MM Botvinick arXiv preprint arXiv:1706.06827, 2017 | 13 | 2017 |
| Planning in reward-rich domains via PAC bandits S Goschin, A Weinstein, ML Littman, E Chastain European Workshop on Reinforcement Learning, 25-42, 2013 | 12 | 2013 |
| Local planning for continuous Markov decision processes A Weinstein Rutgers The State University of New Jersey, School of Graduate Studies, 2014 | 11 | 2014 |
| Perception of intentions and mental states in autonomous virtual agents PC Pantelis, SA Cholewiak, P Ringstad, K Sanik, A Weinstein, CC Wu, ... Proceedings of the Annual Meeting of the Cognitive Science Society 33 (33), 2011 | 10 | 2011 |
| Sample-based planning for continuous action markov decision processes A Weinstein, C Mansley, M Littman ICML 2010 Workshop on Reinforcement Learning and Search in Very Large Spaces, 2010 | 10 | 2010 |
| Rollout-based game-tree search outprunes traditional alpha-beta A Weinstein, ML Littman, S Goschin European Workshop on Reinforcement Learning, 155-167, 2013 | 5 | 2013 |
| Hcmd-zero: Learning value aligned mechanisms from data J Balaguer, R Koster, A Weinstein, L Campbell-Gillingham, ... arXiv preprint arXiv:2202.10122, 2022 | 4 | 2022 |
| Publisher Correction: Human-centred mechanism design with Democratic AI R Koster, J Balaguer, A Tacchetti, A Weinstein, T Zhu, O Hauser, ... Nature Human Behaviour 6 (9), 1320, 2022 | 2 | 2022 |
| Publisher Correction: Intuitive physics learning in a deep-learning model inspired by developmental psychology LS Piloto, A Weinstein, P Battaglia, M Botvinick Nature Human Behaviour 6 (8), 1181, 2022 | | 2022 |
Matthew BotvinickGoogle DeepMind, Yale Law School, University College LondonVerified email at google.com
