Alert button
Picture for Martin Schmid

Martin Schmid

Alert button

Learning not to Regret

Mar 02, 2023
David Sychrovsky, Michal Sustr, Elnaz Davoodi, Marc Lanctot, Martin Schmid

Figure 1 for Learning not to Regret
Figure 2 for Learning not to Regret
Figure 3 for Learning not to Regret
Figure 4 for Learning not to Regret
Viaarxiv icon

Player of Games

Dec 06, 2021
Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, Zach Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling

Figure 1 for Player of Games
Figure 2 for Player of Games
Figure 3 for Player of Games
Figure 4 for Player of Games
Viaarxiv icon

Search in Imperfect Information Games

Nov 10, 2021
Martin Schmid

Figure 1 for Search in Imperfect Information Games
Figure 2 for Search in Imperfect Information Games
Figure 3 for Search in Imperfect Information Games
Figure 4 for Search in Imperfect Information Games
Viaarxiv icon

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

Figure 1 for Solving Common-Payoff Games with Approximate Policy Iteration
Figure 2 for Solving Common-Payoff Games with Approximate Policy Iteration
Figure 3 for Solving Common-Payoff Games with Approximate Policy Iteration
Figure 4 for Solving Common-Payoff Games with Approximate Policy Iteration
Viaarxiv icon

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

Figure 1 for The Advantage Regret-Matching Actor-Critic
Figure 2 for The Advantage Regret-Matching Actor-Critic
Figure 3 for The Advantage Regret-Matching Actor-Critic
Figure 4 for The Advantage Regret-Matching Actor-Critic
Viaarxiv icon

Approximate exploitability: Learning a best response in large games

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

Figure 1 for Approximate exploitability: Learning a best response in large games
Figure 2 for Approximate exploitability: Learning a best response in large games
Figure 3 for Approximate exploitability: Learning a best response in large games
Figure 4 for Approximate exploitability: Learning a best response in large games
Viaarxiv icon

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

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

Figure 1 for Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Figure 2 for Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Figure 3 for Low-Variance and Zero-Variance Baselines for Extensive-Form Games
Viaarxiv icon

Rethinking Formal Models of Partially Observable Multiagent Decision Making

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

Figure 1 for Rethinking Formal Models of Partially Observable Multiagent Decision Making
Figure 2 for Rethinking Formal Models of Partially Observable Multiagent Decision Making
Figure 3 for Rethinking Formal Models of Partially Observable Multiagent Decision Making
Figure 4 for Rethinking Formal Models of Partially Observable Multiagent Decision Making
Viaarxiv icon

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

Figure 1 for Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Figure 2 for Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Figure 3 for Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Figure 4 for Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Viaarxiv icon

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

Viaarxiv icon