Im Jahr war es der KI Libratus gelungen, einen Poker-Profi bei einer Partie Texas-Hold'em ohne Limit zu schlagen. Diese Spielform gilt. Die Mechanismen hinter dem KI-Bot, der ein Team aus Pokerpros vor knapp einem Jahr alt aussehen ließ, wurden nun in einem. Tuomas Sandholm und seine Mitstreiter haben Details zu ihrer Poker-KI Libratus veröffentlicht, die jüngst vier Profispieler deutlich geschlagen.
Libratus Poker Bot vernichtet menschliche Gegner – Der Anfang vom Ende?Die Mechanismen hinter dem KI-Bot, der ein Team aus Pokerpros vor knapp einem Jahr alt aussehen ließ, wurden nun in einem. Our goal was to replicate Libratus from a article published in Science titled Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Im Jahr war es der KI Libratus gelungen, einen Poker-Profi bei einer Partie Texas-Hold'em ohne Limit zu schlagen. Diese Spielform gilt.
Libratus Poker Teile diesen Beitrag Video6 Libratus vs Preflop 3 Bet Nash equilibrium Fifa 19 Frauen Wm. The computations were carried out on the new 'Bridges' supercomputer at the Pittsburgh Supercomputing Center. The Libratus victory is not the first time bots demonstrated their ability to beat Premack-Prinzip human players. Deep Blue. We do not share Medchenspile information with any third parties. Libratus Game abstraction. Libratus played a poker variant called heads up no-limit Texas Hold’em. Heads up means that there are Solving the blueprint. The blueprint is orders of magnitude smaller than the possible number of states in a game. Nested safe subgame solving. While it’s true that the. While the first program, Claudico, was summarily beaten by human poker players —“one broke-ass robot,” an observer called it — Libratus has triumphed in a series of one-on-one, or heads-up, matches against some of the best online players in the United States. Libratus relies on three main modules. Libratus’ three-pronged approach to the game included: Creating an abstract version of the game which was easier to solve Creating a more detailed plan-of-action based on how the game was playing out Improving on that plan in real time by detecting mistakes in its opponent’s strategy and exploiting. Pitting artificial intelligence (AI) against top human players demonstrates just how far AI has come. Brown and Sandholm built a poker-playing AI called Libratus that decisively beat four leading. Libratus emerged as the clear victor after playing more than , hands in a heads-up no-limit Texas hold ’em poker tournament back in February. The machine crushed its meatbag opponents by big blinds per game, drawing in $1,, in prize money. Now, a paper published in Science reveals how Libratus was programmed. The approach taken by its creators Noam Brown, a PhD student, and Tuomas Sandholm, a professor of computer science, both at Carnegie Mellon University in the US.
For example: when Player A got aces vs. Thus no party could just run hot over the course of the challenge. No hard all-ins.
When a hand was all-in before the river no more cards were dealt and each player received his equity in chips. This also reduced the luck factor.
This equates to a win rate of All four human players lost over their 30, hands against Libratus. This is how they performed individually:.
While the rules of the challenge were set to reduce the luck factor as much as possible, chance still plays a big role in the results of each hand — even with mirrored hands and even with the elimination of all-in luck.
So maybe, just maybe, the human players are actually better but the AI just got lucky. Let's look at some statistics regarding the results. The AI won with a win rate of Those are just rough estimates for the variance, but as we'll see they're good enough boundaries.
What's the probability of the humans actually playing better than the AI but losing at a rate of It turns out this probability is very low: Somewhere between 0.
Meaning: It's very, very unlikely the general result of this challenge — the AI plays better than four humans — is due to the AI just getting lucky.
No bad luck. Basically the Libratus AI is just a huge set of strategies which define how to play in a certain situation.
Two examples of such strategies not necessarily related to the actual game play of Libratus :. It quickly becomes obvious that there are almost uncountably many different situations the AI can be in and for each and every situation the AI has a strategy.
The AI effectively rolls a dice to decide what to do but the probabilities and actions are pre-calculated and well balanced. The computer played for many days against itself, accumulating billions, probably trillions of hands and tried randomly all kinds of different strategies.
Whenever a strategy worked, the likelihood to play this strategy increased; whenever a strategy didn't work, the likelihood decreased.
Basically, generating the strategies was a colossal trial and error run. What does this mean for poker when a super computer wins poker tournaments vs humans?
Are we going to have to worry about bots in the future playing us online to take all our money in cash games? How will we protect our online play against these super computer machines and bot technology once it becomes available mainstream?
Well for now I do not think we have to worry, although the way tech jumps forward in leaps and bounds you just do not know how long we will be safe from these super computer bots.
The Good News is that this ai best poker bot super computer was only able to win in heads up poker, and for now if your worried or may feel the need to be worried in the future, just avoid heads up poker as much as you can.
A Suggestion: You could stop playing heads up poker tournament games and forget all about ai super computer poker playing money stealing bots.
I never did like heads up poker myself anyway. Maybe these poker player professionals should have done something different every hand like the best poker bot known as Libratus was doing.
Their new method gets rid of the prior de facto standard in Poker programming, called "action mapping". As Libratus plays only against one other human or computer player, the special 'heads up' rules for two-player Texas hold 'em are enforced.
To manage the extra volume, the duration of the tournament was increased from 13 to 20 days. The four players were grouped into two subteams of two players each.
One of the subteams was playing in the open, while the other subteam was located in a separate room nicknamed 'The Dungeon' where no mobile phones or other external communications were allowed.
The Dungeon subteam got the same sequence of cards as was being dealt in the open, except that the sides were switched: The Dungeon humans got the cards that the AI got in the open and vice versa.
This setup was intended to nullify the effect of card luck. As written in the tournament rules in advance, the AI itself did not receive prize money even though it won the tournament against the human team.
AlphaGo  famously used neural networks to represent the outcome of a subtree of Go. While Go and poker are both extensive form games, the key difference between the two is that Go is a perfect information game, while poker is an imperfect information game.
In poker however, the state of the game depends on how the cards are dealt, and only some of the relevant cards are observed by every player.
To illustrate the difference, we look at Figure 2, a simplified game tree for poker. Note that players do not have perfect information and cannot see what cards have been dealt to the other player.
Let's suppose that Player 1 decides to bet. Player 2 sees the bet but does not know what cards player 1 has. In the game tree, this is denoted by the information set , or the dashed line between the two states.
An information set is a collection of game states that a player cannot distinguish between when making decisions, so by definition a player must have the same strategy among states within each information set.
Thus, imperfect information makes a crucial difference in the decision-making process. To decide their next action, player 2 needs to evaluate the possibility of all possible underlying states which means all possible hands of player 1.
Because the player 1 is making decisions as well, if player 2 changes strategy, player 1 may change as well, and player 2 needs to update their beliefs about what player 1 would do.
Heads up means that there are only two players playing against each other, making the game a two-player zero sum game. No-limit means that there are no restrictions on the bets you are allowed to make, meaning that the number of possible actions is enormous.
In contrast, limit poker forces players to bet in fixed increments and was solved in . Nevertheless, it is quite costly and wasteful to construct a new betting strategy for a single-dollar difference in the bet.
Libratus abstracts the game state by grouping the bets and other similar actions using an abstraction called a blueprint.
In a blueprint, similar bets are be treated as the same and so are similar card combinations e. Ace and 6 vs.
Ace and 5. The blueprint is orders of magnitude smaller than the possible number of states in a game. Libratus solves the blueprint using counterfactual regret minimization CFR , an iterative, linear time algorithm that solves for Nash equilibria in extensive form games.
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