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

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

An intelligent poker-agent for Texas Hold'em

Estágio realizado em Budapest University of Technology and Economics e orientado por Dániel László KovácsTese de mestrado integrado. Engenharia Informátca e Computação. Faculdade de Engenharia. Universidade do Porto. 200

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

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

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

Building a no limit Texas hold'em poker agent based on game logs using supervised learning

The development of competitive artificial Poker players is a challenge to Artificial Intelligence (AI) because the agent must deal with unreliable information and deception which make it essential to model the opponents to achieve good results. In this paper we propose the creation of an artificial Poker player through the analysis of past games between human players, with money involved. To accomplish this goal, we defined a classification problem that associates a given game state with the action that was performed by the player. To validate and test the defined player model, an agent that follows the learned tactic was created. The agent approximately follows the tactics from the human players, thus validating this model. However, this approach alone is insufficient to create a competitive agent, as generated strategies are static, meaning that they can't adapt to different situations. To solve this problem, we created an agent that uses a strategy that combines several tactics from different players. By using the combined strategy, the agentgreatly improved its performance against adversaries capable of modeling opponents

Applying machine learning techniques to an imperfect information game

The game of poker presents a challenging game to Artificial Intelligence researchers because it is a complex asymmetric information game. In such games, a player can improve his performance by inferring the private information held by the other players from their prior actions. A novel connectionist structure was designed to play a version of poker (multi-player limit Hold‟em). This allows simple reinforcement learning techniques to be used which previously not been considered for the game of multi-player hold‟em. A related hidden Markov model was designed to be fitted to records of poker play without using any private information. Belief vectors generated by this model provide a more convenient and flexible representation of an opponent‟s action history than alternative approaches. The structure was tested in two settings. Firstly self-play simulation was used to generate an approximation to a Nash equilibrium strategy. A related, but slower, rollout strategy that uses Monte-Carlo samples was used to evaluate the performance. Secondly the structure was used to model and hence exploit a population of opponents within a relatively small number of games. When and how to adapt quickly to new opponents are open questions in poker AI research. A opponent model with a small number of discrete types is used to identify the largest differences in strategy between members of the population. A commercial software package (Poker Academy) was used to provide a population of sophisticated opponents to test against. A series of experiments was conducted to compare adaptive and static systems. All systems showed positive results but surprisingly the adaptive systems did not show a significant improvement over similar static systems. The possible reasons for this result are discussed. This work formed the basis of a series of entries to the computer poker competition hosted at the annual conferences of the Association for the Advancement of Artificial Intelligence (AAAI). Its best rankings were 3rd in the 2006 6-player limit hold‟em competition and 2nd in the 2008 3-player limit hold‟em competition