1 code implementation • 6 Jan 2017 • Matej Moravčík, Martin Schmid, Neil Burch, Viliam Lisý, Dustin Morrill, Nolan Bard, Trevor Davis, Kevin Waugh, Michael Johanson, Michael Bowling
Poker is the quintessential game of imperfect information, and a longstanding challenge problem in artificial intelligence.
1 code implementation • 20 Nov 2017 • Jaromír Janisch, Tomáš Pevný, Viliam Lisý
We study a classification problem where each feature can be acquired for a cost and the goal is to optimize a trade-off between the expected classification error and the feature cost.
no code implementations • 31 May 2019 • Vojtěch Kovařík, Dominik Seitz, Viliam Lisý, Jan Rudolf, Shuo Sun, Karel Ha
We provide a formal definition of depth-limited games together with an accessible and rigorous explanation of the underlying concepts, both of which were previously missing in imperfect-information games.
no code implementations • 26 Jun 2019 • Vojtěch Kovařík, Martin Schmid, Neil Burch, Michael Bowling, Viliam Lisý
A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition.
2 code implementations • 5 Sep 2019 • Jaromír Janisch, Tomáš Pevný, Viliam Lisý
This work focuses on a specific classification problem, where the information about a sample is not readily available, but has to be acquired for a cost, and there is a per-sample budget.
1 code implementation • 20 Nov 2019 • Jaromír Janisch, Tomáš Pevný, Viliam Lisý
In this work, we extend an existing deep reinforcement learning-based algorithm with hierarchical deep sets and hierarchical softmax, so that it can directly process this data.
1 code implementation • 25 Sep 2020 • Jaromír Janisch, Tomáš Pevný, Viliam Lisý
We focus on reinforcement learning (RL) in relational problems that are naturally defined in terms of objects, their relations, and object-centric actions.
no code implementations • 30 Sep 2020 • David Milec, Jakub Černý, Viliam Lisý, Bo An
This paper aims to analyze and propose scalable algorithms for computing effective and robust strategies against a quantal opponent in normal-form and extensive-form games.
no code implementations • 20 Dec 2021 • Vojtěch Kovařík, David Milec, Michal Šustr, Dominik Seitz, Viliam Lisý
Recent advancements in algorithms for sequential decision-making under imperfect information have shown remarkable success in large games such as limit- and no-limit poker.
no code implementations • 4 Aug 2022 • Tomáš Pevný, Viliam Lisý, Branislav Bošanský, Petr Somol, Michal Pěchouček
Learning from raw data input, thus limiting the need for feature engineering, is a component of many successful applications of machine learning methods in various domains.
2 code implementations • 26 May 2023 • Jaromír Janisch, Tomáš Pevný, Viliam Lisý
Current frameworks for training offensive penetration testing agents with deep reinforcement learning struggle to produce agents that perform well in real-world scenarios, due to the reality gap in simulation-based frameworks and the lack of scalability in emulation-based frameworks.
no code implementations • 28 Nov 2023 • David Milec, Viliam Lisý, Christopher Kiekintveld
Attackers in the real world are predominantly human actors, and the protection methods often incorporate opponent models to improve the performance when facing humans.