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The CFR framework has been a powerful tool for solving large-scale extensive-form games in practice.
A simplified two-person poker
Kuhn, H. W · 1950
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Efficient computation of behavior strategies
von Stengel, B · 1996
Earlier work this paper cites.
Online convex programming and generalized infinitesimal gradient ascent
Zinkevich, M · 2003
Earlier work this paper cites.
Prox-method with rate of convergence O(1/t) for variational inequalities with Lipschitz continuous monotone operators and smooth convex-concave saddle point problems
Nemirovski, A · 2004
Earlier work this paper cites.
Excessive gap technique in nonsmooth convex minimization
Nesterov, Y · 2005
Earlier work this paper cites.
Prediction, learning, and games
Cesa-Bianchi, N. and Lugosi, G · 2006
Earlier work this paper cites.
Regret minimization in games with incomplete information
Zinkevich, M., Bowling, M., Johanson, M., and Piccione, C · 2007
Earlier work this paper cites.
Monte Carlo sampling for regret minimization in extensive games
Lanctot, M., Waugh, K., Zinkevich, M., and Bowling, M · 2009
Cited alongside, same era.
Smoothing techniques for computing Nash equilibria of sequential games
Hoda, S., Gilpin, A., Peña, J., and Sandholm, T · 2010
Cited alongside, same era.
Online optimization with gradual variations
Chiang, C.-K., Yang, T., Lee, C.-J., Mahdavi, M., Lu, C.-J., Jin, R., and Zhu, S · 2012
Cited alongside, same era.
Heads-up limit hold’em poker is solved
Bowling, M., Burch, N., Johanson, M., and Tammelin, O · 2015
Cited alongside, same era.
Faster first-order methods for extensive-form game solving
Kroer, C., Waugh, K., Kılınç-Karzan, F., and Sandholm, T · 2015
Cited alongside, same era.
Fast convergence of regularized learning in games
Syrgkanis, V., Agarwal, A., Luo, H., and Schapire, R. E · 2015
Cited alongside, same era.
Time and Space: Why Imperfect Information Games are Hard
Burch, N · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Moravčík, M., Schmid, M., Burch, N., Lisý, V., Morrill, D., Bard, N., Davis, T., Waugh, K., Johanson, M., and Bowling, M · 2017
Later among the works it cites.
Depth-limited solving for imperfect-information games
Brown, N., Sandholm, T., and Amos, B · 2018
Later among the works it cites.
Composability of Regret Minimizers
Farina, G., Kroer, C., and Sandholm, T · 2018
Later among the works it cites.
Acceleration through optimistic no-regret dynamics
Wang, J.-K. and Abernethy, J. D · 2018
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Solving imperfect-information games via discounted regret minimization
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Solving heads-up limit Texas hold’em
Tammelin, O., Burch, N., Johanson, M., and Bowling, M · 2015
Cited alongside, same era.
Safe and nested subgame solving for imperfect-information games
Brown, N. and Sandholm, T
Cited in the paper.
Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Brown, N. and Sandholm, T
Cited in the paper.
Solving large sequential games with the excessive gap technique
Kroer, C., Farina, G., and Sandholm, T
Cited in the paper.
Faster algorithms for extensive-form game solving via improved smoothing functions
Kroer, C., Waugh, K., Kılınç-Karzan, F., and Sandholm, T
Cited in the paper.
Online learning with predictable sequences
Rakhlin, A. and Sridharan, K
Cited in the paper.
Brown, N. and Sandholm, T · 2019
Closest in time.
Online convex optimization for sequential decision processes and extensive-form games
Farina, G., Kroer, C., and Sandholm, T · 2019
Closest in time.