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Search in Imperfect Information Games


Nov 10, 2021
Martin Schmid

* doctoral thesis 

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Solving Common-Payoff Games with Approximate Policy Iteration


Jan 11, 2021
Samuel Sokota, Edward Lockhart, Finbarr Timbers, Elnaz Davoodi, Ryan D'Orazio, Neil Burch, Martin Schmid, Michael Bowling, Marc Lanctot

* AAAI 2021 

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The Advantage Regret-Matching Actor-Critic


Aug 27, 2020
Audrūnas Gruslys, Marc Lanctot, Rémi Munos, Finbarr Timbers, Martin Schmid, Julien Perolat, Dustin Morrill, Vinicius Zambaldi, Jean-Baptiste Lespiau, John Schultz, Mohammad Gheshlaghi Azar, Michael Bowling, Karl Tuyls


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Approximate exploitability: Learning a best response in large games


Apr 20, 2020
Finbarr Timbers, Edward Lockhart, Martin Schmid, Marc Lanctot, Michael Bowling


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Low-Variance and Zero-Variance Baselines for Extensive-Form Games


Jul 22, 2019
Trevor Davis, Martin Schmid, Michael Bowling

* Under review for NeurIPS 2019 

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Rethinking Formal Models of Partially Observable Multiagent Decision Making


Jun 26, 2019
Vojtěch Kovařík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý


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Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines


Sep 09, 2018
Martin Schmid, Neil Burch, Marc Lanctot, Matej Moravcik, Rudolf Kadlec, Michael Bowling


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DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker


Mar 03, 2017
Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling


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AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games


Jan 19, 2017
Neil Burch, Martin Schmid, Matej Moravčík, Michael Bowling

* To appear at AAAI-17 Workshop on Computer Poker and Imperfect Information Games 

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Text Understanding with the Attention Sum Reader Network


Jun 24, 2016
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar, Jan Kleindienst

* Presented at ACL 2016 

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Improved Deep Learning Baselines for Ubuntu Corpus Dialogs


Nov 03, 2015
Rudolf Kadlec, Martin Schmid, Jan Kleindienst

* Accepted to Machine Learning for SLU & Interaction NIPS 2015 Workshop 

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