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Relational inductive biases, deep learning, and graph networks

P. Battaglia, J. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, C. Gulcehre, F. Song, A. Ballard, J. Gilmer, G. Dahl, A. Vaswani, K. Allen, C. Nash, V. Langston, C. Dyer, N. Heess, D. Wierstra, P. Kohli, M. Botvinick, O. Vinyals, Y. Li, and R. Pascanu. (2018)cite arxiv:1806.01261.

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Martin Zambaldi

Vinícius Liebel

Vinícius Maran

Claudio Zambaldi

Felipe Zambaldi

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Relational inductive biases, deep learning, and graph networksP. Battaglia, J. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner and 1 other author(s). arXiv preprint arXiv:1806.01261, (2018)A Unified Game-Theoretic Approach to Multiagent Reinforcement LearningM. Lanctot, V. Zambaldi, A. Gruslys, A. Lazaridou, K. Tuyls, J. Perolat, D. Silver, and T. Graepel. (2017)cite arxiv:1711.00832Comment: Camera-ready copy of NIPS 2017 paper, including appendix.The Advantage Regret-Matching Actor-Critic.A. Gruslys, M. Lanctot, R. Munos, F. Timbers, M. Schmid, J. Pérolat, D. Morrill, V. Zambaldi, J. Lespiau, J. Schultz and 3 other author(s). CoRR, (2020)OpenSpiel: A Framework for Reinforcement Learning in Games.M. Lanctot, E. Lockhart, J. Lespiau, V. Zambaldi, S. Upadhyay, J. Pérolat, S. Srinivasan, F. Timbers, K. Tuyls, S. Omidshafiei and 17 other author(s). CoRR, (2019)Relational Forward Models for Multi-Agent Learning.A. Tacchetti, H. Song, P. Mediano, V. Zambaldi, J. Kramár, N. Rabinowitz, T. Graepel, M. Botvinick, and P. Battaglia. ICLR (Poster), OpenReview.net, (2019)Relational Deep Reinforcement Learning.V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart and 6 other author(s). CoRR, (2018)Compositional Imitation Learning: Explaining and executing one task at a time.T. Kipf, Y. Li, H. Dai, V. Zambaldi, E. Grefenstette, P. Kohli, and P. Battaglia. CoRR, (2018)CompILE: Compositional Imitation Learning and Execution.T. Kipf, Y. Li, H. Dai, V. Zambaldi, A. Sanchez-Gonzalez, E. Grefenstette, P. Kohli, and P. Battaglia. ICML, volume 97 of Proceedings of Machine Learning Research, page 3418-3428. PMLR, (2019)Deep reinforcement learning with relational inductive biases.V. Zambaldi, D. Raposo, A. Santoro, V. Bapst, Y. Li, I. Babuschkin, K. Tuyls, D. Reichert, T. Lillicrap, E. Lockhart and 6 other author(s). ICLR (Poster), OpenReview.net, (2019)MEMO: A Deep Network for Flexible Combination of Episodic Memories.A. Banino, A. Badia, R. Köster, M. Chadwick, V. Zambaldi, D. Hassabis, C. Barry, M. Botvinick, D. Kumaran, and C. Blundell. ICLR, OpenReview.net, (2020)

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Disambiguation of "Zambaldi, Vinicius"

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Relational inductive biases, deep learning, and graph networks

Please choose a person to relate this publication to

Martin Zambaldi

Vinícius Liebel

Vinícius Maran

Claudio Zambaldi

Felipe Zambaldi

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Zambaldi, Vinicius"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Relational inductive biases, deep learning, and graph networks

Please choose a person to relate this publication to

Martin Zambaldi

Vinícius Liebel

Vinícius Maran

Claudio Zambaldi

Felipe Zambaldi

Other publications of authors with the same name

Disambiguation

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URL
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Relational inductive biases, deep learning, and graph networks