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Regret minimization is a powerful tool for solving large-scale problems; it was recently used in breakthrough results for large-scale extensive-form game solving.
Efficient computation of behavior strategies
von Stengel, B · 1996
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A simple adaptive procedure leading to correlated equilibrium
Hart, S. and Mas-Colell, A · 2000
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Online convex programming and generalized infinitesimal gradient ascent
Zinkevich, M · 2003
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Convex Optimization
Boyd, S. and Vandenberghe, L · 2004
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Disciplined convex programming
Grant, M., Boyd, S., and Ye, Y · 2006
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Regret minimization in games with incomplete information
Zinkevich, M., Bowling, M., Johanson, M., and Piccione, C · 2007
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Cvx: Matlab software for disciplined convex programming, 2008
Grant, M., Boyd, S., and Ye, Y · 2008
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Monte Carlo sampling for regret minimization in extensive games
Lanctot, M., Waugh, K., Zinkevich, M., and Bowling, M · 2009
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Smoothing techniques for computing Nash equilibria of sequential games
Hoda, S., Gilpin, A., Peña, J., and Sandholm, T · 2010
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Computing a quasi-perfect equilibrium of a two-player game
Miltersen, P. B. and Sørensen, T. B · 2010
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Follow-the-regularized-leader and mirror descent: Equivalence theorems and l1 regularization
McMahan, B · 2011
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Regret transfer and parameter optimization
Brown, N. and Sandholm, T · 2014
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Solving imperfect information games using decomposition
Burch, N., Johanson, M., and Bowling, M · 2014
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Heads-up limit hold’em poker is solved
Bowling, M., Burch, N., Johanson, M., and Tammelin, O · 2015
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Endgame solving in large imperfect-information games
Ganzfried, S. and Sandholm, T · 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.
Extensive-form perfect equilibrium computation in two-player games
Farina, G. and Gatti, N · 2017
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Regret minimization in behaviorally-constrained zero-sum games
Farina, G., Kroer, C., and Sandholm, T · 2017
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Smoothing method for approximate extensive-form perfect equilibrium
Kroer, C., Farina, G., and Sandholm, T · 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.
Practical exact algorithm for trembling-hand equilibrium refinements in games
Farina, G., Gatti, N., and Sandholm, T · 2018
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Faster algorithms for extensive-form game solving via improved smoothing functions
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Solving heads-up limit Texas hold’em
Tammelin, O., Burch, N., Johanson, M., and Bowling, M · 2015
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Strategy-based warm starting for regret minimization in games
Brown, N. and Sandholm, T · 2016
Cited alongside, same era.
Refining subgames in large imperfect information games
Moravcik, M., Schmid, M., Ha, K., Hladik, M., and Gaukrodger, S · 2016
Cited alongside, same era.
Dynamic thresholding and pruning for regret minimization
Brown, N., Kroer, C., and Sandholm, T · 2017
Cited alongside, same era.
Regret-based pruning in extensive-form games
Brown, N. and Sandholm, T
Cited in the paper.
Simultaneous abstraction and equilibrium finding in games
Brown, N. and Sandholm, T
Cited in the paper.
Kroer, C., Waugh, K., Kılınç-Karzan, F., and Sandholm, T · 2018
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What game are we playing? End-to-end learning in normal and extensive form games
Ling, C. K., Fang, F., and Kolter, J. Z · 2018
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Solving imperfect-information games via discounted regret minimization
Brown, N. and Sandholm, T · 2019
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Solving large extensive-form games with strategy constraints
Davis, T., Waugh, K., and Bowling, M · 2019
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Online convex optimization for sequential decision processes and extensive-form games
Farina, G., Kroer, C., and Sandholm, T · 2019
Closest in time.