| Relational inductive biases, deep learning, and graph networks PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 3621 | 2018 |
| Value-decomposition networks for cooperative multi-agent learning P Sunehag, G Lever, A Gruslys, WM Czarnecki, V Zambaldi, M Jaderberg, ... arXiv preprint arXiv:1706.05296, 2017 | 1688 | 2017 |
| Multi-agent reinforcement learning in sequential social dilemmas JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel arXiv preprint arXiv:1702.03037, 2017 | 882 | 2017 |
| A unified game-theoretic approach to multiagent reinforcement learning M Lanctot, V Zambaldi, A Gruslys, A Lazaridou, K Tuyls, J Pérolat, D Silver, ... Advances in neural information processing systems 30, 2017 | 730 | 2017 |
| Deep reinforcement learning with relational inductive biases V Zambaldi, D Raposo, A Santoro, V Bapst, Y Li, I Babuschkin, K Tuyls, ... International conference on learning representations, 2019 | 484* | 2019 |
| OpenSpiel: A framework for reinforcement learning in games M Lanctot, E Lockhart, JB Lespiau, V Zambaldi, S Upadhyay, J Pérolat, ... arXiv preprint arXiv:1908.09453, 2019 | 253 | 2019 |
| A multi-agent reinforcement learning model of common-pool resource appropriation J Perolat, JZ Leibo, V Zambaldi, C Beattie, K Tuyls, T Graepel Advances in neural information processing systems 30, 2017 | 214 | 2017 |
| Relational inductive biases, deep learning, and graph networks. arXiv 2018 PW Battaglia, JB Hamrick, V Bapst, A Sanchez-Gonzalez, V Zambaldi, ... arXiv preprint arXiv:1806.01261, 2018 | 195 | 2018 |
| Actor-critic policy optimization in partially observable multiagent environments S Srinivasan, M Lanctot, V Zambaldi, J Pérolat, K Tuyls, R Munos, ... Advances in neural information processing systems 31, 2018 | 161 | 2018 |
| Dawn of the selfie era: The whos, wheres, and hows of selfies on Instagram F Souza, D de Las Casas, V Flores, SB Youn, M Cha, D Quercia, ... Proceedings of the 2015 ACM on conference on online social networks, 221-231, 2015 | 137 | 2015 |
| Compile: Compositional imitation learning and execution T Kipf, Y Li, H Dai, V Zambaldi, A Sanchez-Gonzalez, E Grefenstette, ... International Conference on Machine Learning, 3418-3428, 2019 | 124 | 2019 |
| Relational forward models for multi-agent learning A Tacchetti, HF Song, PAM Mediano, V Zambaldi, NC Rabinowitz, ... arXiv preprint arXiv:1809.11044, 2018 | 85 | 2018 |
| Memo: A deep network for flexible combination of episodic memories A Banino, AP Badia, R Köster, MJ Chadwick, V Zambaldi, D Hassabis, ... arXiv preprint arXiv:2001.10913, 2020 | 36 | 2020 |
| The spatial memory pipeline: a model of egocentric to allocentric understanding in mammalian brains B Uria, B Ibarz, A Banino, V Zambaldi, D Kumaran, D Hassabis, C Barry, ... BioRxiv, 2020.11. 11.378141, 2020 | 35 | 2020 |
| The advantage regret-matching actor-critic A Gruslys, M Lanctot, R Munos, F Timbers, M Schmid, J Perolat, D Morrill, ... arXiv preprint arXiv:2008.12234, 2020 | 24 | 2020 |
| Graph neural network systems for behavior prediction and reinforcement learning in multple agent environments H Song, A Tacchetti, PW Battaglia, V Zambaldi US Patent App. 17/054,632, 2021 | 23 | 2021 |
| Compositional imitation learning: Explaining and executing one task at a time T Kipf, Y Li, H Dai, V Zambaldi, E Grefenstette, P Kohli, P Battaglia arXiv preprint arXiv:1812.01483, 2018 | 18 | 2018 |
| Lightweight contextual ranking of city pictures: urban sociology to the rescue V Zambaldi, J Pesce, D Quercia, V Almeida Proceedings of the International AAAI Conference on Web and Social Media 8 …, 2014 | 14 | 2014 |
| Reinforcement learning using a relational network for generating data encoding relationships between entities in an environment Y Li, VC Bapst, V Zambaldi, DN Raposo, AA Santoro US Patent App. 18/168,123, 2023 | | 2023 |
| Generating spatial embeddings by integrating agent motion and optimizing a predictive objective B Uria-Martínez, A Banino, BI Gabardos, V Zambaldi, C Blundell US Patent App. 17/914,066, 2023 | | 2023 |
Thore GraepelGlobal Lead Computational Science, AI & ML at Altos Labs and Chair of Machine Learning, UCL在 ucl.ac.uk 的电子邮件经过验证
