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Counterfactual Regret Minimization (CRF) is a fundamental and effective technique for solving Imperfect Information Games (IIG).
A course in game theory , volume 1
Martin J. Osborne and Rubinstein Ariel · 1994
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Neural mechanisms of selective visual attention
Robert Desimone and John Duncan · 1995
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Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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A simple adaptive procedure leading to correlated equilibrium
Sergiu Hart and Andreu Mas-Colell · 2000
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No-regret algorithms for structured prediction problems
Geoffrey J. Gordon · 2005
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Monte carlo sampling for regret minimization in extensive games
Marc Lanctot, Waugh Kevin, Zinkevich Martin, and Michael Bowling · 2009
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Bayes’ bluff: Opponent modelling in poker
Finnegan Southey, Michael P. Bowling, Bryce Larson, Carmelo Piccione, Neil Burch, Darse Billings, and Chris Rayner · 2012
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Regret minimization in games with incomplete information
Martin Zinkevich, Johanson Michael, Bowling Michael, and Carmelo Piccione · 2012
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Learning phrase representations using rnn encoder-decoder for statistical machine translation
Kyunghyun Cho, Bart Van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio · 2014
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2014
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Solving large imperfect information games using cfr+
Oskari Tammelin · 2014
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Describing multimedia content using attention-based encoder–decoder networks
Kyunghyun Cho, Aaron Courville, and Yoshua Bengio · 2015
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Solving games with functional regret estimation
Kevin Waugh, Dustin Morrill, James Andrew Bagnell, and Michael Bowling · 2015
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Strategy-based warm starting for regret minimization in games
Noam Brown and Tuomas Sandholm · 2016
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Deep reinforcement learning from self-play in imperfect-information games
Johannes Heinrich and David Silver · 2016
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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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Time and space: Why imperfect information games are hard
Neil Burch · 2017
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Deepstack: Expert-level artificial intelligence in heads-up no-limit poker
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Fictitious self-play in extensive-form games
Johannes Heinrich, Marc Lanctot, and David Silver · 2015
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Human-level control through deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al · 2015
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Digital design and computer architecture (2nd ed.) , volume ISBN 978-0-12-394424-5
David Harris and Sarah Harris
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Matej Moravcik, Schmid Martin, Burch Neil, Lisý Viliam, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, and Michael Bowling · 2017
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An overview of gradient descent optimization algorithms
Sebastian Ruder · 2017
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