Abstract No-limit Texas Hold'em is the most popular variant of poker in the world. Heads-up
no-limit Texas Hold'em is the main benchmark challenge for AI in imperfect-information …

Imperfect-information games model strategic interactions involving multiple agents with
private information. A typical goal in this setting is to approximate an equilibrium in which all …

Approximating a Nash equilibrium is currently the best performing approach for creating
poker-playing programs. While for the simplest variants of the game, it is possible to …

Conducting additional search during test time is often used to improve the performance of
reinforcement learning algorithms. Performing search in adversarial games with imperfect …

Deep counterfactual value networks combined with continual resolving provide a way to
conduct depth-limited search in imperfect-information games. However, since their …

Extensive-form games are a powerful tool for representing complex multi-agent interactions.
Nash equilibrium strategies are commonly used as a solution concept for extensive-form …

Games have been used as a testbed for artificial intelligence research since the earliest
conceptions of computing itself. The twin goals of defeating human professional players at …

Imperfect-information games, where players have private information, pose a unique
challenge in artificial intelligence. In recent years, Heads-Up No-Limit Texas Hold'em poker …

Consequently, acting to maximize utility is challenging since an agent must gather and
exploit knowledge about how the other (potentially adaptive) agents behave. In this thesis …

Extensive-form games are a powerful framework for modeling sequential multiagent
interactions. In extensive-form games with imperfect information, Nash equilibria are …