Fetching the paper…
Reading the bibliography…
While fictitious play is guaranteed to converge to Nash equilibrium in certain game classes, such as two-player zero-sum games, it is not guaranteed to converge in non-zero-sum and multiplayer games.
Non-cooperative games
John Nash · 1950
Earlier work this paper cites.
Iterative solutions of games by fictitious play
George W. Brown · 1951
Earlier work this paper cites.
An iterative method of solving a game
Julia Robinson · 1951
Earlier work this paper cites.
Some topics in two-person games
Lloyd S Shapley · 1964
Earlier work this paper cites.
On the nonconvergence of fictitious play in coordination games
Dean P. Foster and H. Peyton Young · 1998
Earlier work this paper cites.
The Theory of Learning in Games
Drew Fudenberg and David Levine · 1998
Earlier work this paper cites.
Run the GAMUT: A comprehensive approach to evaluating game-theoretic algorithms
Eugene Nudelman, Jennifer Wortman, Kevin Leyton-Brown, and Yoav Shoham · 2004
Earlier work this paper cites.
Fictitious play in 2xn games
Ulrich Berger · 2005
Earlier work this paper cites.
3-Nash is PPAD-complete
Xi Chen and Xiaotie Deng · 2005
Earlier work this paper cites.
Regret minimization in games with incomplete information
Martin Zinkevich, Michael Bowling, Michael Johanson, and Carmelo Piccione · 2007
Earlier work this paper cites.
Computing an approximate jam/fold equilibrium for 3-player no-limit Texas hold ’em tournaments
Sam Ganzfried and Tuomas Sandholm · 2008
Earlier work this paper cites.
The complexity of computing a Nash equilibrium
Constantinos Daskalakis, Paul Goldberg, and Christos Papadimitriou · 2009
Earlier work this paper cites.
Computing equilibria in multiplayer stochastic games of imperfect information
Sam Ganzfried and Tuomas Sandholm · 2009
Cited alongside, same era.
Monte Carlo sampling for regret minimization in extensive games
Marc Lanctot, Kevin Waugh, Martin Zinkevich, and Michael Bowling · 2009
Cited alongside, same era.
Generalised fictitious play for a continuum of anonymous players
Zinovi Rabinovich, Enrico Gerding, Maria Polukarov, and Nicholas R. Jennings · 2009
Cited alongside, same era.
Using counterfactual regret minimization to create competitive multiplayer poker agents
Nick Abou Risk and Duane Szafron · 2010
Cited alongside, same era.
First-order algorithm with 𝒪 ( ln ( 1 / ϵ ) ) \mathcal{O}(\mathrm{ln}(1/\epsilon)) convergence for ϵ \epsilon -equilibrium in two-person zero-sum games
Andrew Gilpin, Javier Peña, and Tuomas Sandholm · 2012
Cited alongside, same era.
Superhuman AI for heads-up no-limit poker: Libratus beats top professionals
Noam Brown and Tuomas Sandholm · 2017
Later among the works it cites.
Midgame solving: A new weapon for efficient large-scale equilibrium approximation
Kailiang Hu and Sam Ganzfried · 2017
Later among the works it cites.
Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
Later among the works it cites.
Depth-limited solving for imperfect-information games
Noam Brown, Tuomas Sandholm, and Brandon Amos · 2018
Later among the works it cites.
Successful Nash equilibrium agent for a 3-player imperfect-information game
Sam Ganzfried, Austin Nowak, and Joannier Pinales · 2018
Later among the works it cites.
Deep counterfactual regret minimization
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Computing pure Bayesian-Nash equilibria in games with finite actions and continuous types
Zinovi Rabinovich, Victor Naroditskiy, Enrico H. Gerding, and Nicholas R. Jennings · 2013
Cited alongside, same era.
Regret Minimization in Games and the Development of Champion Multiplayer Computer Poker-Playing Agents
Richard Gibson · 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.
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.
Deep Reinforcement Learning from Self-Play in Imperfect-Information Games
Johannes Heinrich and David Silver · 2016
Cited alongside, same era.
Exclusion method for finding Nash equilibrium in multiplayer games
Kimmo Berg and Tuomas Sandholm · 2017
Cited alongside, same era.
Noam Brown, Adam Lerer, Sam Gross, and Tuomas Sandholm · 2019
Later among the works it cites.
Superhuman AI for multiplayer poker
Noam Brown and Tuomas Sandholm · 2019
Later among the works it cites.
Mistakes in games
Sam Ganzfried · 2019
Later among the works it cites.
Parallel algorithm for Nash equilibrium in multiplayer stochastic games with application to naval strategic planning
Sam Ganzfried, Conner Laughlin, and Charles Morefield · 2020
Closest in time.
Fictitious play with maximin initialization
Sam Ganzfried · 2022
Closest in time.
Fictitious play — Wikipedia, the free encyclopedia, 2022
Wikipedia contributors · 2022
Closest in time.