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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

M. 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.

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Neural Replicator Dynamics: Multiagent Learning via Hedging Policy Gradients.D. Hennes, D. Morrill, S. Omidshafiei, R. Munos, J. Pérolat, M. Lanctot, A. Gruslys, J. Lespiau, P. Parmas, E. Duéñez-Guzmán and 1 other author(s). AAMAS, page 492-501. International Foundation for Autonomous Agents and Multiagent Systems, (2020)Neural Replicator Dynamics.S. Omidshafiei, D. Hennes, D. Morrill, R. Munos, J. Pérolat, M. Lanctot, A. Gruslys, J. Lespiau, and K. Tuyls. CoRR, (2019)The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning.A. Gruslys, W. Dabney, M. Azar, B. Piot, M. Bellemare, and R. Munos. ICLR (Poster), OpenReview.net, (2018)A New Fast Accurate Nonlinear Medical Image Registration Program Including Surface Preserving Regularization.A. Gruslys, J. Acosta-Cabronero, P. Nestor, G. Williams, and R. Ansorge. IEEE Trans. Medical Imaging, 33 (11): 2118-2127 (2014)Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning.J. Pérolat, B. Vylder, D. Hennes, E. Tarassov, F. Strub, V. de Boer, P. Muller, J. Connor, N. Burch, T. Anthony and 24 other author(s). CoRR, (2022)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)Deep Q-learning From Demonstrations.T. Hester, M. Vecerík, O. Pietquin, M. Lanctot, T. Schaul, B. Piot, D. Horgan, J. Quan, A. Sendonaris, I. Osband and 4 other author(s). AAAI, page 3223-3230. AAAI Press, (2018)Fast computation of Nash Equilibria in Imperfect Information Games.R. Munos, J. Pérolat, J. Lespiau, M. Rowland, B. Vylder, M. Lanctot, F. Timbers, D. Hennes, S. Omidshafiei, A. Gruslys and 3 other author(s). ICML, volume 119 of Proceedings of Machine Learning Research, page 7119-7129. PMLR, (2020)Quantile Credit Assignment.T. Mesnard, W. Chen, A. Saade, Y. Tang, M. Rowland, T. Weber, C. Lyle, A. Gruslys, M. Valko, W. Dabney and 3 other author(s). ICML, volume 202 of Proceedings of Machine Learning Research, page 24517-24531. PMLR, (2023)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.

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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning

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A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning