812 citations · 929 across the 10 of their papers we have counts for
14 papers
Learning not to Regret
David Sychrovský, Michal Šustr, Elnaz Davoodi +3
The literature on game-theoretic equilibrium finding predominantly focuses on single games or their repeated play. Nevertheless, numerous real-world scenarios feature playing a gam…
Search in Imperfect Information Games
Martin Schmid
From the very dawn of the field, search with value functions was a fundamental concept of computer games research. Turing's chess algorithm from 1950 was able to think two moves ah…
Solving Common-Payoff Games with Approximate Policy Iteration
Samuel Sokota, Edward Lockhart, Finbarr Timbers +6
For artificially intelligent learning systems to have widespread applicability in real-world settings, it is important that they be able to operate decentrally. Unfortunately, dece…
The Advantage Regret-Matching Actor-Critic
Audrūnas Gruslys, Marc Lanctot, Rémi Munos +10
Regret minimization has played a key role in online learning, equilibrium computation in games, and reinforcement learning (RL). In this paper, we describe a general model-free RL…
Sound Algorithms in Imperfect Information Games
Michal Šustr, Martin Schmid, Matej Moravčík +3
Search has played a fundamental role in computer game research since the very beginning. And while online search has been commonly used in perfect information games such as Chess a…
Approximate exploitability: Learning a best response in large games
Finbarr Timbers, Nolan Bard, Edward Lockhart +6
Researchers have demonstrated that neural networks are vulnerable to adversarial examples and subtle environment changes, both of which one can view as a form of distribution shift…
Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Trevor Davis, Martin Schmid, Michael Bowling
Extensive-form games (EFGs) are a common model of multi-agent interactions with imperfect information. State-of-the-art algorithms for solving these games typically perform full wa…
Rethinking Formal Models of Partially Observable Multiagent Decision Making
Vojtěch Kovařík, Martin Schmid, Neil Burch +2
Multiagent decision-making in partially observable environments is usually modelled as either an extensive-form game (EFG) in game theory or a partially observable stochastic game…
Revisiting CFR+ and Alternating Updates
Neil Burch, Matej Moravcik, Martin Schmid
The CFR+ algorithm for solving imperfect information games is a variant of the popular CFR algorithm, with faster empirical performance on a range of problems. It was introduced wi…
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Martin Schmid, Neil Burch, Marc Lanctot +3
Learning strategies for imperfect information games from samples of interaction is a challenging problem. A common method for this setting, Monte Carlo Counterfactual Regret Minimi…
DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker
Matej Moravčík, Martin Schmid, Neil Burch +7
Artificial intelligence has seen several breakthroughs in recent years, with games often serving as milestones. A common feature of these games is that players have perfect informa…
AIVAT: A New Variance Reduction Technique for Agent Evaluation in Imperfect Information Games
Neil Burch, Martin Schmid, Matej Moravčík +1
Evaluating agent performance when outcomes are stochastic and agents use randomized strategies can be challenging when there is limited data available. The variance of sampled outc…
Text Understanding with the Attention Sum Reader Network
Rudolf Kadlec, Martin Schmid, Ondrej Bajgar +1
Several large cloze-style context-question-answer datasets have been introduced recently: the CNN and Daily Mail news data and the Children's Book Test. Thanks to the size of these…
Improved Deep Learning Baselines for Ubuntu Corpus Dialogs
Rudolf Kadlec, Martin Schmid, Jan Kleindienst
This paper presents results of our experiments for the next utterance ranking on the Ubuntu Dialog Corpus -- the largest publicly available multi-turn dialog corpus. First, we use…