Vinicius Zambaldi
Vinicius Zambaldi
Google Deepmind
Adresse e-mail validée de
Citée par
Citée par
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
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
Multi-agent reinforcement learning in sequential social dilemmas
JZ Leibo, V Zambaldi, M Lanctot, J Marecki, T Graepel
arXiv preprint arXiv:1702.03037, 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
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
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
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
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
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
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
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
Relational forward models for multi-agent learning
A Tacchetti, HF Song, PAM Mediano, V Zambaldi, NC Rabinowitz, ...
arXiv preprint arXiv:1809.11044, 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
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
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
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
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
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
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
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
Le système ne peut pas réaliser cette opération maintenant. Veuillez réessayer plus tard.
Articles 1–20