Tableau Main Poker All In

Joseph Hebert won the ‘Domestic Tournament' of the 2020 World Series of Poker $10,000 buy-in no-limit hold'em main event Monday evening in Las Vegas. The 38-year-old poker player and part. With a full 48-in diameter table top, you can fit up to 8 people for a friendly poker game, board game, or a relaxing dinner. This durable hardwood table with a beautiful inlaid top will last for years to come. The poker table top features a plush, padded, easy-to-clean leather like black playing surface. As far as Power BI vs Tableau is concerned, both Power BI and Tableau has its own features, pros, and cons. It all depends upon the business needs and requirements. If the business requirement is to analyze the limited amount of data and functionality Power BI is the best way to opt for as it is cheaper than Tableau.

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I had a long conversation with one of my colleagues about imperfect information games and deep learning this weekend and reminded me of an article I wrote last year so I decided to republish it.

Poker has remained as one of the most challenging games to master in the fields of artificial intelligence(AI) and game theory. From the game theory-creator John Von Neumann writing about poker in his 1928 essay 'Theory of Parlor Games, to Edward Thorp masterful book 'Beat the Dealer' to the MIT Blackjack Team, poker strategies has been an obsession to mathematicians for decades. In recent years, AI has made some progress in poker environments with systems such as Libratus, defeating human pros in two-player no-limit Hold'em in 2017. Last year, a team of AI researchers from Facebook in collaboration with Carnegie Mellon University achieved a major milestone in the conquest of Poker by creating Pluribus, an AI agent that beat elite human professional players in the most popular and widely played poker format in the world: six-player no-limit Texas Hold'em poker.

The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. After all, in recent years AI researchers have made tremendous progress across different complex games such as checkers, chess, Go, two-player poker, StarCraft 2, and Dota 2. All those games are constrained to only two players and are zero-sum games (meaning that whatever one player wins, the other player loses). Other AI strategies based on reinforcement learning have been able to master multi-player games Dota 2 Five and Quake III. However, six-player, no-limit Texas Hold'em still remains one of the most elusive challenges for AI systems.

Mastering the Most Difficult Poker Game in the World

The challenge with six-player, no-limit Texas Hold'em poker can be summarized in three main aspects:

1. Dealing with incomplete information.
2. Difficulty to achieve a Nash equilibrium.
3. Success requires psychological skills like bluffing.

In AI theory, poker is classified as an imperfect-information environment which means that players never have a complete picture of the game. No other game embodies the challenge of hidden information quite like poker, where each player has information (his or her cards) that the others lack. Additionally, an action in poker in highly dependent of the chosen strategy. In perfect-information games like chess, it is possible to solve a state of the game (ex: end game) without knowing about the previous strategy (ex: opening). In poker, it is impossible to disentangle the optimal strategy of a specific situation from the overall strategy of poker.

The second challenge of poker relies on the difficulty of achieving a Nash equilibrium. Named after legendary mathematician John Nash, the Nash equilibrium describes a strategy in a zero-sum game in which a player in guarantee to win regardless of the moves chosen by its opponent. In the classic rock-paper-scissors game, the Nash equilibrium strategy is to randomly pick rock, paper, or scissors with equal probability. The challenge with the Nash equilibrium is that its complexity increases with the number of players in the game to a level in which is not feasible to pursue that strategy. In the case of six-player poker, achieving a Nash equilibrium is computationally impossible many times.

The third challenge of six-player, no-limit Texas Hold'em is related to its dependence on human psychology. The success in poker relies on effectively reasoning about hidden information, picking good action and ensuring that a strategy remains unpredictable. A successful poker player should know how to bluff, but bluffing too often reveals a strategy that can be beaten. This type of skills has remained challenging to master by AI systems throughout history.

Pluribus

Like many other recent AI-game breakthroughs, Pluribus relied on reinforcement learning models to master the game of poker. The core of Pluribus's strategy was computed via self-play, in which the AI plays against copies of itself, without any data of human or prior AI play used as input. The AI starts from scratch by playing randomly, and gradually improves as it determines which actions, and which probability distribution over those actions, lead to better outcomes against earlier versions of its strategy.

Differently from other multi-player games, any given position in six-player, no-limit Texas Hold'em can have too many decision points to reason about individually. Pluribus uses a technique called abstraction to group similar actions together and eliminate others reducing the scope of the decision. The current version of Pluribus uses two types of abstractions:

• Action Abstraction: This type of abstraction reduces the number of different actions the AI needs to consider. For instance, betting $150 or $151 might not make a difference from the strategy standpoint. To balance that, Pluribus only considers a handful of bet sizes at any decision point.
• Information Abstraction: This type of abstraction groups decision points based on the information that has been revealed. For instance, a ten-high straight and a nine-high straight are distinct hands, but are nevertheless strategically similar. Pluribus uses information abstraction only to reason about situations on future betting rounds, never the betting round it is actually in.

To automate self-play training, the Pluribus team used a version of the of the iterative Monte Carlo CFR (MCCFR) algorithm. On each iteration of the algorithm, MCCFR designates one player as the 'traverser' whose current strategy is updated on the iteration. At the start of the iteration, MCCFR simulates a hand of poker based on the current strategy of all players (which is initially completely random). Once the simulated hand is completed, the algorithm reviews each decision the traverser made and investigates how much better or worse it would have done by choosing the other available actions instead. Next, the AI assesses the merits of each hypothetical decision that would have been made following those other available actions, and so on.The difference between what the traverser would have received for choosing an action versus what the traverser actually achieved (in expectation) on the iteration is added to the counterfactual regret for the action. At the end of the iteration, the traverser's strategy is updated so that actions with higher counterfactual regret are chosen with higher probability.

The outputs of the MCCFR training are known as the blueprint strategy. Using that strategy, Pluribus was able to master poker in eight days on a 64-core server and required less than 512 GB of RAM. No GPUs were used.
The blueprint strategy is too expensive to use real time in a poker game. During actual play, Pluribus improves upon the blueprint strategy by conducting real-time search to determine a better, finer-grained strategy for its particular situation. Traditional search strategies are very challenging to implement in imperfect information games in which the players can change strategies at any time. Pluribus instead uses an approach in which the searcher explicitly considers that any or all players may shift to different strategies beyond the leaf nodes of a subgame. Specifically, rather than assuming all players play according to a single fixed strategy beyond the leaf nodes, Pluribus assumes that each player may choose among four different strategies to play for the remainder of the game when a leaf node is reached. This technique results in the searcher finding a more balanced strategy that produces stronger overall performance.

Pluribus in Action

Facebook evaluated Pluribus by playing against an elite group of players that included several World Series of Poker and World Poker Tour champions. In one experiment, Pluribus played 10,000 hands of poker against five human players selected randomly from the pool. Pluribus's win rate was estimated to be about 5 big blinds per 100 hands (5 bb/100), which is considered a very strong victory over its elite human opponents (profitable with a p-value of 0.021). If each chip was worth a dollar, Pluribus would have won an average of about $5 per hand and would have made about $1,000/hour.

The following figure illustrates Pluribus' performance. On the top chart, the solid lines show the win rate plus or minus the standard error. The bottom chart shows the number of chips won over the course of the games.

Pluribus represents one of the major breakthroughs in modern AI systems. Even though Pluribus was initially implemented for poker, the general techniques can be applied to many other multi-agent systems that require both AI and human skills. Just like AlphaZero is helping to improve professional chess, its interesting to see how poker players can improve their strategies based on the lessons learned from Pluribus.

Original. Reposted with permission.

Related:

Tableau Main Poker All In Poker

You've been playing with Tableau and its sample data sets, but now you're ready to find some data that's a little more fun. Where do you start?

There are countless sources of data you can bring in to Tableau - some easily, and others that require data cleaning and shaping. In this post we'll highlight a few favorites, but we'll provide a more extensive list for you to explore too. If you're looking for links to data sources or data cleaning, skip down to the bottom of the post.

Tableau Public

This might be obvious, but in addition to the sample data sets, did you know you can download Tableau workbooks directly and get started vizzing with Tableau-ready data? It's true. Spend time looking through vizzes by our incredible Featured Authors or in our Viz of the Day and Greatest Hits galleries.

While you're getting inspired, scroll to the bottom of the viz in the viz toolbar and click Download. If Tableau Workbook is available, then you can open the workbook directly and start vizzing! Don't worry, you won't change anything about the original viz - this is now your copy to use as a learning tool. One tip: if you download someone else's work in order to learn, don't forget to include an attribution somewhere on the viz so credit goes where credit's due!

Tableau Main Poker All In Order

Data is Plural

Jeremy Singer-Vine's weekly tiny letter has been a treasure trove of cool data since 2015. Sign up and you'll get data delivered to your inbox, forcing you to choose each week which data set will finally get your attention. I've seen data about refugee routes, retiree language preferences (courtesy of the Social Security administration!), linguistics, movie ratings..more data than I could viz in a lifetime.

data.world

data.world is a social data platform where users upload data sets to encourage collaboration. Members of data.world's community improves the data through the addition of supplemental content, data cleaning, or by adding visualizations. It's rich with diverse data sets and users, and an excellent platform for collaborative analysis.

Research Pipeline

This data wiki and accompanying blog is a passion project from researcher Lyndie Chiou, and is a bonanza of science data. Science has its own vocabulary and each subject its own tools, so be warned: data cleaning will be required. But any list that includes data for the wooly mammoth genome on the same page as the Hubble data archive is worth a second look.

Pluribus

Like many other recent AI-game breakthroughs, Pluribus relied on reinforcement learning models to master the game of poker. The core of Pluribus's strategy was computed via self-play, in which the AI plays against copies of itself, without any data of human or prior AI play used as input. The AI starts from scratch by playing randomly, and gradually improves as it determines which actions, and which probability distribution over those actions, lead to better outcomes against earlier versions of its strategy.

Differently from other multi-player games, any given position in six-player, no-limit Texas Hold'em can have too many decision points to reason about individually. Pluribus uses a technique called abstraction to group similar actions together and eliminate others reducing the scope of the decision. The current version of Pluribus uses two types of abstractions:

• Action Abstraction: This type of abstraction reduces the number of different actions the AI needs to consider. For instance, betting $150 or $151 might not make a difference from the strategy standpoint. To balance that, Pluribus only considers a handful of bet sizes at any decision point.
• Information Abstraction: This type of abstraction groups decision points based on the information that has been revealed. For instance, a ten-high straight and a nine-high straight are distinct hands, but are nevertheless strategically similar. Pluribus uses information abstraction only to reason about situations on future betting rounds, never the betting round it is actually in.

To automate self-play training, the Pluribus team used a version of the of the iterative Monte Carlo CFR (MCCFR) algorithm. On each iteration of the algorithm, MCCFR designates one player as the 'traverser' whose current strategy is updated on the iteration. At the start of the iteration, MCCFR simulates a hand of poker based on the current strategy of all players (which is initially completely random). Once the simulated hand is completed, the algorithm reviews each decision the traverser made and investigates how much better or worse it would have done by choosing the other available actions instead. Next, the AI assesses the merits of each hypothetical decision that would have been made following those other available actions, and so on.The difference between what the traverser would have received for choosing an action versus what the traverser actually achieved (in expectation) on the iteration is added to the counterfactual regret for the action. At the end of the iteration, the traverser's strategy is updated so that actions with higher counterfactual regret are chosen with higher probability.

The outputs of the MCCFR training are known as the blueprint strategy. Using that strategy, Pluribus was able to master poker in eight days on a 64-core server and required less than 512 GB of RAM. No GPUs were used.
The blueprint strategy is too expensive to use real time in a poker game. During actual play, Pluribus improves upon the blueprint strategy by conducting real-time search to determine a better, finer-grained strategy for its particular situation. Traditional search strategies are very challenging to implement in imperfect information games in which the players can change strategies at any time. Pluribus instead uses an approach in which the searcher explicitly considers that any or all players may shift to different strategies beyond the leaf nodes of a subgame. Specifically, rather than assuming all players play according to a single fixed strategy beyond the leaf nodes, Pluribus assumes that each player may choose among four different strategies to play for the remainder of the game when a leaf node is reached. This technique results in the searcher finding a more balanced strategy that produces stronger overall performance.

Pluribus in Action

Facebook evaluated Pluribus by playing against an elite group of players that included several World Series of Poker and World Poker Tour champions. In one experiment, Pluribus played 10,000 hands of poker against five human players selected randomly from the pool. Pluribus's win rate was estimated to be about 5 big blinds per 100 hands (5 bb/100), which is considered a very strong victory over its elite human opponents (profitable with a p-value of 0.021). If each chip was worth a dollar, Pluribus would have won an average of about $5 per hand and would have made about $1,000/hour.

The following figure illustrates Pluribus' performance. On the top chart, the solid lines show the win rate plus or minus the standard error. The bottom chart shows the number of chips won over the course of the games.

Pluribus represents one of the major breakthroughs in modern AI systems. Even though Pluribus was initially implemented for poker, the general techniques can be applied to many other multi-agent systems that require both AI and human skills. Just like AlphaZero is helping to improve professional chess, its interesting to see how poker players can improve their strategies based on the lessons learned from Pluribus.

Original. Reposted with permission.

Related:

Tableau Main Poker All In Poker

You've been playing with Tableau and its sample data sets, but now you're ready to find some data that's a little more fun. Where do you start?

There are countless sources of data you can bring in to Tableau - some easily, and others that require data cleaning and shaping. In this post we'll highlight a few favorites, but we'll provide a more extensive list for you to explore too. If you're looking for links to data sources or data cleaning, skip down to the bottom of the post.

Tableau Public

This might be obvious, but in addition to the sample data sets, did you know you can download Tableau workbooks directly and get started vizzing with Tableau-ready data? It's true. Spend time looking through vizzes by our incredible Featured Authors or in our Viz of the Day and Greatest Hits galleries.

While you're getting inspired, scroll to the bottom of the viz in the viz toolbar and click Download. If Tableau Workbook is available, then you can open the workbook directly and start vizzing! Don't worry, you won't change anything about the original viz - this is now your copy to use as a learning tool. One tip: if you download someone else's work in order to learn, don't forget to include an attribution somewhere on the viz so credit goes where credit's due!

Tableau Main Poker All In Order

Data is Plural

Jeremy Singer-Vine's weekly tiny letter has been a treasure trove of cool data since 2015. Sign up and you'll get data delivered to your inbox, forcing you to choose each week which data set will finally get your attention. I've seen data about refugee routes, retiree language preferences (courtesy of the Social Security administration!), linguistics, movie ratings..more data than I could viz in a lifetime.

data.world

data.world is a social data platform where users upload data sets to encourage collaboration. Members of data.world's community improves the data through the addition of supplemental content, data cleaning, or by adding visualizations. It's rich with diverse data sets and users, and an excellent platform for collaborative analysis.

Research Pipeline

This data wiki and accompanying blog is a passion project from researcher Lyndie Chiou, and is a bonanza of science data. Science has its own vocabulary and each subject its own tools, so be warned: data cleaning will be required. But any list that includes data for the wooly mammoth genome on the same page as the Hubble data archive is worth a second look.

Data Cleaning Resources

You say you already have a data set, but you need help cleaning it? Look no further. Here are some resources to get you started.

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What are your go-to data sources? Share them with the community via Twitter or Facebook and get vizzin!