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We extend the classic regret minimization framework for approximating equilibria in normal-form games by greedily weighing iterates based on regrets observed at runtime.
OpenSpiel: A framework for reinforcement learning in games
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A general class of adaptive strategies
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Planning in the presence of cost functions controlled by an adversary
McMahan, H. B.; Gordon, G. J.; and Blum, A. 2003 · 2003
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Tactical coordination in no-press diplomacy
Johansson, S. J.; and Håård, F. 2005 · 2005
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Prediction, learning, and games
Cesa-Bianchi, N.; and Lugosi, G. 2006 · 2006
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From external to internal regret
Blum, A.; and Mansour, Y. 2007 · 2007
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Regret minimization in games with incomplete information
Zinkevich, M.; Johanson, M.; Bowling, M.; and Piccione, C. 2008 · 2008
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Settling the complexity of computing two-player Nash equilibria
Chen, X.; Deng, X.; and Teng, S.-H. 2009 · 2009
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Fast convergence of regularized learning in games
Syrgkanis, V.; Agarwal, A.; Luo, H.; and Schapire, R. E. 2015 · 2015
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9. A SIMPLIFIED TWO-PERSON POKER
Kuhn, H. W. 2016 · 2016
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Time and Space: Why Imperfect Information Games are Hard
Burch, N. 2017 · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Moravčík, M.; Schmid, M.; Burch, N.; Lisỳ, V.; Morrill, D.; Bard, N.; Davis, T.; Waugh, K.; Johanson, M.; and Bowling, M. 2017 · 2017
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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Brown, N.; and Sandholm, T. 2018 · 2018
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Deep counterfactual regret minimization
Brown, N.; Lerer, A.; Gross, S.; and Sandholm, T. 2019 · 2019
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The complexity of computing a Nash equilibrium
Daskalakis, C.; Goldberg, P. W.; and Papadimitriou, C. H. 2009 · 2009
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Blackwell approachability and no-regret learning are equivalent
Abernethy, J.; Bartlett, P. L.; and Hazan, E. 2011 · 2011
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Bayes’ bluff: Opponent modelling in poker
Southey, F.; Bowling, M. P.; Larson, B.; Piccione, C.; Burch, N.; Billings, D.; and Rayner, C. 2012 · 2012
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Cooperation in Strategic Games Revisited*
Kalai, A.; and Kalai, E. 2013 · 2013
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Solving large imperfect information games using CFR+
Tammelin, O. 2014 · 2014
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Heads-up limit hold’em poker is solved
Bowling, M.; Burch, N.; Johanson, M.; and Tammelin, O. 2015 · 2015
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No-Press Diplomacy: Modeling Multi-Agent Gameplay
Paquette, P.; Lu, Y.; BOCCO, S. S.; Smith, M.; O-G, S.; Kummerfeld, J. K.; Pineau, J.; Singh, S.; and Courville, A. C. 2019 · 2019
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Hardness of Approximation Between P and NP
Rubinstein, A. 2019 · 2019
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Finding Friend and Foe in Multi-Agent Games
Serrino, J.; Kleiman-Weiner, M.; Parkes, D. C.; and Tenenbaum, J. 2019 · 2019
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Learning to Play No-Press Diplomacy with Best Response Policy Iteration
Anthony, T.; Eccles, T.; Tacchetti, A.; Kramár, J.; Gemp, I.; Hudson, T.; Porcel, N.; Lanctot, M.; Perolat, J.; Everett, R.; Singh, S.; Graepel, T.; and Bachrach, Y. 2020 · 2020
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Faster Game Solving via Predictive Blackwell Approachability: Connecting Regret Matching and Mirror Descent
Farina, G.; Kroer, C.; and Sandholm, T. 2021 · 2021
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Human-Level Performance in No-Press Diplomacy via Equilibrium Search
Gray, J.; Lerer, A.; Bakhtin, A.; and Brown, N. 2021 · 2021
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