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Deep counterfactual value networks combined with continual resolving provide a way to conduct depth-limited search in imperfect-information games.
Equilibrium points in n-person games
John F Nash et al · 1950
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Iterative solution of games by fictitious play
George W Brown · 1951
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Robust estimation of a location parameter
Peter J Huber · 1992
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TD-Gammon, a self-teaching backgammon program, achieves master-level play
Gerald Tesauro · 1994
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Deep Blue
Murray Campbell, A Joseph Hoane Jr, and Feng-hsiung Hsu · 2002
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Pseudo-optimal strategies in no-limit poker
Rickard Andersson · 2006
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Generalised weakened fictitious play
David S Leslie and Edmund J Collins · 2006
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A heads-up no-limit texas hold’em poker player: discretized betting models and automatically generated equilibrium-finding programs
Andrew Gilpin, Tuomas Sandholm, and Troels Bjerre Sørensen · 2008
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Regret minimization in games with incomplete information
Martin Zinkevich, Michael Johanson, Michael Bowling, and Carmelo Piccione · 2008
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Probabilistic state translation in extensive games with large action sets
David Schnizlein, Michael Bowling, and Duane Szafron · 2009
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Smoothing techniques for computing nash equilibria of sequential games
Samid Hoda, Andrew Gilpin, Javier Pena, and Tuomas Sandholm · 2010
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Finding optimal abstract strategies in extensive-form games
Michael Johanson, Nolan Bard, Neil Burch, and Michael Bowling · 2012
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Case-based strategies in computer poker
Jonathan Rubin and Ian Watson · 2012
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Action translation in extensive-form games with large action spaces: axioms, paradoxes, and the pseudo-harmonic mapping
Sam Ganzfried and Tuomas Sandholm · 2013
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Slumbot nl: Solving large games with counterfactual regret minimization using sampling and distributed processing
Eric Griffin Jackson · 2013
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Measuring the size of large no-limit poker games
Michael Johanson · 2013
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Evaluating state-space abstractions in extensive-form games
Michael Johanson, Neil Burch, Richard Valenzano, and Michael Bowling · 2013
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Decentralized stochastic control with partial history sharing: A common information approach
Ashutosh Nayyar, Aditya Mahajan, and Demosthenis Teneketzis · 2013
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Asymmetric abstractions for adversarial settings
Nolan Bard, Michael Johanson, and Michael Bowling · 2014
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Solving imperfect information games using decomposition
Neil Burch, Michael Johanson, and Michael Bowling · 2014
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A time and space efficient algorithm for approximately solving large imperfect information games
Eric Griffin Jackson · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2017
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Eqilibrium approximation quality of current no-limit poker bots
Viliam Lisy and Michael Bowling · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisỳ, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
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Mastering the game of go without human knowledge
David Silver, Julian Schrittwieser, Karen Simonyan, Ioannis Antonoglou, Aja Huang, Arthur Guez, Thomas Hubert, Lucas Baker, Matthew Lai, Adrian Bolton, et al · 2017
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Depth-limited solving for imperfect-information games
Noam Brown, Tuomas Sandholm, and Brandon Amos · 2018
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Solving large sequential games with the excessive gap technique
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Oskari Tammelin · 2014
Cited alongside, same era.
Heads-up limit hold’em poker is solved
Michael Bowling, Neil Burch, Michael Johanson, and Oskari Tammelin · 2015
Cited alongside, same era.
Hierarchical abstraction, distributed equilibrium computation, and post-processing, with application to a champion no-limit texas hold’em agent
Noam Brown, Sam Ganzfried, and Tuomas Sandholm · 2015
Cited alongside, same era.
Endgame solving in large imperfect-information games
Sam Ganzfried and Tuomas Sandholm · 2015
Cited alongside, same era.
Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
Cited alongside, same era.
Solving heads-up limit texas hold’em
Oskari Tammelin, Neil Burch, Michael Johanson, and Michael Bowling · 2015
Cited alongside, same era.
Solving games with functional regret estimation
Kevin Waugh, Dustin Morrill, James Andrew Bagnell, and Michael Bowling · 2015
Cited alongside, same era.
Christian Kroer, Gabriele Farina, and Tuomas Sandholm · 2018
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Faster algorithms for extensive-form game solving via improved smoothing functions
Christian Kroer, Kevin Waugh, Fatma Kılınç-Karzan, and Tuomas Sandholm · 2018
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A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
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Deep counterfactual regret minimization
Noam Brown, Adam Lerer, Sam Gross, and Tuomas Sandholm · 2019
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Solving imperfect-information games via discounted regret minimization
Noam Brown and Tuomas Sandholm · 2019
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Superhuman AI for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
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Revisiting cfr+ and alternating updates
Neil Burch, Matej Moravcik, and Martin Schmid · 2019
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Single deep counterfactual regret minimization
Eric Steinberger · 2019
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https://www.piosolver.com/
Piosolver · 2020
Closest in time.
https://simplepoker.com
Simple postflop · 2020
Closest in time.
Double neural counterfactual regret minimization
Hui Li, Kailiang Hu, Shaohua Zhang, Yuan Qi, and Le Song · 2020
Closest in time.