Poker Learner: Reinforcement Learning Applied to Texas Hold'em Poker

Bibliografia: p. 61-66Tese de Mestrado Integrado. Engenharia Informática e Computação. Universidade do Porto. Faculdade de Engenharia.. 201

Using a high-level language to build a poker playing agent

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200

Building a computer poker agent with emphasis on opponent modeling

Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (pages 53-54).In this thesis, we present a computer agent for the game of no-limit Texas Hold'em Poker for two players. Poker is a partially observable, stochastic, multi-agent, sequential game. This combination of characteristics makes it a very challenging game to master for both human and computer players. We explore this problem from an opponent modeling perspective, using data mining to build a database of player styles that allows our agent to quickly model the strategy of any new opponent. The opponent model is then used to develop a robust counter strategy. A simpler version of this agent modified for a three player game was able to win the 2011 MIT Poker Bot Competition.by Jian Huang.M. Eng

Building a poker playing agent based on game logs using supervised learning

Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 201

Poker as a testbed for machine intelligence research

ABSTRACT For years, games researchers have used chess, checkers and other board games as a testbed for machine intelligence research. The success of world-championship-caliber programs for these games has resulted in a number of interesting games being overlooked. Specifically, we show that poker can serve as a better testbed for machine intelligence research related to decision making problems. Poker is a game of imperfect knowledge, where multiple competing agents must deal with risk management, agent modeling, unreliable information and deception, much like decision-making applications in the real world. The heuristic search and evaluation methods successfully employed in chess are not helpful here. This paper outlines the difficulty of playing strong poker, and describes our first steps towards building a world-class poker-playing program

HoldemML: A framework to generate No Limit Hold'em Poker agents from human player strategies

Developing computer programs that play Poker at human level is considered to be challenge to the A.I research community, due to its incomplete information and stochastic nature. Due to these characteristics of the game, a competitive agent must manage luck and use opponent modeling to be successful at short term and therefore be profitable. In this paper we propose the creation of No Limit Hold'em Poker agents by copying strategies of the best human players, by analyzing past games between them. To accomplish this goal, first we determine the best players on a set of game logs by determining which ones have higher winning expectation. Next, we define a classification problem to represent the player strategy, by associating a game state with the performed action. To validate and test the defined player model, the HoldemML framework was created. This framework generates agents by classifying the data present on the game logs with the goal to copy the best human player tactics. The created agents approximately follow the tactics from the counterpart human player, thus validating the defined player model. However, this approach proved to be insufficient to create a competitive agent, since the generated strategies were static, which means that they are easy prey to opponents that can perform opponent modeling. This issue can be solved by combining multiple tactics from different players. This way, the agent switches the tactic from time to time, using a simple heuristic, in order to confuse the opponent modeling mechanisms

Machine learning applied to the context of Poker

A combinação de princípios da teoria de jogo e metodologias de machine learning aplicados ao contexto de formular estratégias ótimas para jogos está a angariar interesse por parte de uma porção crescentemente significativa da comunidade científica, tornando-se o jogo do Poker num candidato de estudo popular devido à sua natureza de informação imperfeita. Avanços nesta área possuem vastas aplicações em cenários do mundo real, e a área de investigação de inteligência artificial demonstra que o interesse relativo a este objeto de estudo está longe de desaparecer, com investigadores do Facebook e Carnegie Mellon a apresentar, em 2019, o primeiro agente de jogo autónomo de Poker provado como ganhador num cenário com múltiplos jogadores, uma conquista relativamente à anterior especificação do estado da arte, que fora desenvolvida para jogos de apenas 2 jogadores. Este estudo pretende explorar as características de jogos estocásticos de informação imperfeita, recolhendo informação acerca dos avanços nas metodologias disponibilizados por parte de investigadores de forma a desenvolver um agente autónomo de jogo que se pretende inserir na classificação de "utility-maximizing decision-maker".The combination of game theory principles and machine learning methodologies applied to encountering optimal strategies for games is garnering interest from an increasing large portion of the scientific community, with the game of Poker being a popular study subject due to its imperfect information nature. Advancements in this area have a wide array of applications in real-world scenarios, and the field of artificial intelligent studies show that the interest regarding this object of study is yet to fade, with researchers from Facebook and Carnegie Mellon presenting, in 2019, the world’s first autonomous Poker playing agent that is proven to be profitable while confronting multiple players at a time, an achievement in relation to the previous state of the art specification, which was developed for two player games only. This study intends to explore the characteristics of stochastic games of imperfect information, gathering information regarding the advancements in methodologies made available by researchers in order to ultimately develop an autonomous agent intended to adhere to the classification of a utility-maximizing decision-maker

A Study on Cognitive Biases in Gambling: Hot Hand and Gamblers' Fallacy

People who appear to believe in the hot hand expect winning streaks to continue whereas those suffering from the gamblers’ fallacy unreasonably expect losing streaks to reverse. 565,915 sports bets made by 776 online gamblers in 2010 were used for analysis. People who won were more likely to win again whereas those who lost were more likely to lose again. However, selection of safer odds after winning and riskier ones after losing indicates that online sports gamblers expected their luck to reverse: they suffered from the gamblers’ fallacy. By following in the gamblers’ fallacy, they created their own hot hands. Some gamblers consistently outperformed their peers. They also consistently made higher profits or lower losses. They show real expertise. The key of real expertise is the ability to control loss