Ist Poker für uns Menschen erledigt? Welchen Einfluss wird der eindrucksvolle Erfolg von Libratus auf das Pokerspiel haben? Dieser Artikel wird. Die "Brains Vs. Artificial Intelligence: Upping the Ante" Challenge im Rivers Casino in Pittsburgh ist beendet. Poker-Bot Libratus hat sich nach. Tuomas Sandholm und seine Mitstreiter haben Details zu ihrer Poker-KI Libratus veröffentlicht, die jüngst vier Profispieler deutlich geschlagen.
Poker-KI Pluribus schlägt menschliche Profis im Texas Hold‘em mit sechs SpielernDie 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 From Zero to Hero in 2 Years VideoAI Poker Bots Are Beating The World's Best Players (HBO)
Maybe these poker player professionals should have done something different every hand like the best poker bot known as Libratus was doing. Mixing up play continuously instead of pounding on perceived weak holes.
Who knows. Perhaps that all they could do out of frustration with the ai super computer beating them down continuously. Because these tournament poker players playing against Libratus were adaptive and winning online poker players and always used huds to win online themselves against other players.
They noticed a big hole in their abilities when they did not have a hud against Libratus to help guide them like they were used to using against other human players.
Yet Libratus is one giant poker player HUD in of itself. It analyzed its own play and found its own holes as well as collecting stats and information on the human Poker players it played against.
This ensured that every hand was played with a stack size of big blinds -- reasonably deep stacks for heads-up poker which allowed plenty of room for strategic moves in each hand.
To reduce the luck factor, which might heavily skew the results, two special rules were put in place:. All hands were mirrored. 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.
Multi-agent systems are far more complex than single-agent games. To account for this, mathematicians use the concept of the Nash equilibrium.
A Nash equilibrium is a scenario where none of the game participants can improve their outcome by changing only their own strategy.
This is because a rational player will change their actions to maximize their own game outcome. When the strategies of the players are at a Nash equilibrium, none of them can improve by changing his own.
Thus this is an equilibrium. When allowing for mixed strategies where players can choose different moves with different probabilities , Nash proved that all normal form games with a finite number of actions have Nash equilibria, though these equilibria are not guaranteed to be unique or easy to find.
While the Nash equilibrium is an immensely important notion in game theory, it is not unique. Thus, is hard to say which one is the optimal.
Such games are called zero-sum. Importantly, the Nash equilibria of zero-sum games are computationally tractable and are guaranteed to have the same unique value.
We define the maxmin value for Player 1 to be the maximum payoff that Player 1 can guarantee regardless of what action Player 2 chooses:. The minmax theorem states that minmax and maxmin are equal for a zero-sum game allowing for mixed strategies and that Nash equilibria consist of both players playing maxmin strategies.
As an important corollary, the Nash equilibrium of a zero-sum game is the optimal strategy. Crucially, the minmax strategies can be obtained by solving a linear program in only polynomial time.
While many simple games are normal form games, more complex games like tic-tac-toe, poker, and chess are not. In normal form games, two players each take one action simultaneously.
In contrast, games like poker are usually studied as extensive form games , a more general formalism where multiple actions take place one after another.
See Figure 1 for an example. All the possible games states are specified in the game tree. The good news about extensive form games is that they reduce to normal form games mathematically.
Since poker is a zero-sum extensive form game, it satisfies the minmax theorem and can be solved in polynomial time.
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.
During the tournament, Libratus was competing against the players during the days. Overnight it was perfecting its strategy on its own by analysing the prior gameplay and results of the day, particularly its losses.
Therefore, it was able to continuously straighten out the imperfections that the human team had discovered in their extensive analysis, resulting in a permanent arms race between the humans and Libratus.
It used another 4 million core hours on the Bridges supercomputer for the competition's purposes. Libratus had been leading against the human players from day one of the tournament.
I felt like I was playing against someone who was cheating, like it could see my cards. Go back. Launching Xcode If nothing happens, download Xcode and try again.
Latest commit. Git stats commits. Failed to load latest commit information. Jun 1, Jun 14, Oct 13, Major refactoring. May 31, View code. Deep mind pokerbot for pokerstars and partypoker This pokerbot plays automatically on Pokerstars and Partypoker.
Releases No releases published.Regret, of course, is a human emotion. This Nash equilibrium means: Guts, reads and intuition don't Csgo-Lounge in the end. It could achieve, at maximum, 1.