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DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

arxiv.org

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Re: DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

#3

Post by falcolas »

lawn wrote: post #2

I think I can wrap my head around neural nets being superior at games with perfect information like chess or go. But how would you teach bluffing and randomness to a neural net?

I imagine it's mostly just playing the percentages. Bet when it has a high percentage of winning, fold when it doesn't. It doesn't need to read its opponents if it can play the percentages perfectly.

Re: DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

#4

Post by tomarr »

falcolas wrote: post #3
lawn wrote: post #2

I think I can wrap my head around neural nets being superior at games with perfect information like chess or go. But how would you teach bluffing and randomness to a neural net?

I imagine it's mostly just playing the percentages. Bet when it has a high percentage of winning, fold when it doesn't. It doesn't need to read its opponents if it can play the percentages perfectly.

I don't think this is true? At least for small blinds. If you know your opponent is playing the percentages you could heighten your threshold.

Re: DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

#7

Post by splike »

falcolas wrote: post #3
lawn wrote: post #2

I think I can wrap my head around neural nets being superior at games with perfect information like chess or go. But how would you teach bluffing and randomness to a neural net?

I imagine it's mostly just playing the percentages. Bet when it has a high percentage of winning, fold when it doesn't. It doesn't need to read its opponents if it can play the percentages perfectly.

This is a very naive interpretation of the game of poker. Professional human players are already very good at calculating the percentages, and any joe playing from his computer has access to a calculator.

The reason why simply playing the numbers fails is that if I know an opponent is playing this way, I'll just fold every time he decides to play.

Re: DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

#8

Post by ChuckMcM »

I love it, research that pays for itself :-) I think of poker and other card games as imperfect but predictable information. So while you don't know what cards the other players have you can certainly estimate the likelyhood of what they have and prune your choices that way. Think single deck card counting in Blackjack.

Re: DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker

#9

Post by philosopheer »

most people (including here on HN) are complete n00bs when it comes to understanding how poker is played and how computers can play it, so just to straighten y'all out at the git-go here:

computers are better at bluffing and randomness than humans are. Bluffing is an important optimizing strategy in playing poker well, and it entails tracking the expected value of a pot (which includes cost expectations, don't forget) and it entails randomness, necessary to obfuscate patterns of betting that could give away evidence of your bluffing strategy. Like chess and go, we may not be "there" yet with computers, but n00bs need to understand the theory.

What computers can't do is read "tells", so if you are a master poker player via tells (whether it's unconscious or conscious thinking on your part) then you will beat other humans better than a computer will; but, by the same token, the computer will not give you tells to read nor be fooled by your fake tells. I think the mistake in thinking newbies (even highly experienced ones) make is mixing together "the psychology" of the game with the mathematics of the game.

So to give an oversimplified concrete example of a poker bluffing strategy (inspired by Nesmith Ankeny's book), if odds of you drawing one of the cards you need to win a showdown are 1 out of 4 but the expected payoff is 20x then you not only need to stay in purely on expected value, but it is also an optimal time to bluff if you don't get your card. It is informationally better to have a bluffing strategy that masquerades as an "I have good cards" strategy and gives random information after the showdown rather than "bluffing" being something you do sheerly when you have shit cards. And to enforce a random strategy on yourself, he recommends using a system of the cards in your hand as the random number generator to tell you whether to bluff or not: as you can see, his strategy designed for human players is more perfectly implemented by a computer.

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