[leeleelee]

This was my thought too. Poker is quite "solvable" meaning, whenever you're confronted with a decisions -- there is always a "correct" answer which does not have to depend on the other players' behavior or style. And you can find that answer by simulation, or game trees, and other methods.

It's also important to keep in mind that the best AI can still lose, and the worst AI can still win (and everything in between). Poker involves randomness, obviously whereas chess/go/etc does not.

[mormegil]

That depends on what you mean by "correct". Sure, you could theoretically find Nash equilibrium of poker and by playing the equilibrium strategy, you can ensure you won't lose. But that does not mean this is the best strategy to use at a given table against the given opponents, who (being imperfect humans) almost certainly do not play the equilibrium strategy themselves. And, by playing a proper nonequilibrium strategy, suited to the specific players, you can win more.

[andrewprock]

The usual way these games are solved is to create an "abstract" game which is tractable, find the Nash equilibrium, and map state in the real game back to the "abstract" game. In the limit, the solutions for a well designed "abstract" game will converge to that of the real game.

[mormegil]

You are explaining how current algorithms try to find the (approximate) Nash equilibrium (and those algorithms are far from perfect; as noted in the recent DeepStack paper, current abstraction-based programs are beatable by over 3000 mbb/g, which is four times as large as simply folding each game). But my point is that even the (exact) equilibrium strategy would not necessarily be the best strategy against given non-equilibrium-playing players.

[andrewprock]

Yes, you are correct on every point. Opponent modeling and exploitation is significantly more difficult than coming up with a Nash equilibrium to an abstract game.

[yAnonymous]

>there is always a "correct" answer

That's wrong. Even when you're holding a good hand, your opponent could hold a better one and reading them is a key element of poker. The opponent's hand is an important variable to decide whether you hold the winning hand or not.

If you look at the experiment in detail, you'll find that it was set up in the AI's favor.

>When a hand was all-in before the river no more cards were dealt and each player received his equity in chips.

While all that is less important when you can avoid all-in situations, the main statement -that the other player's behavior is irrelevant- is still wrong.

[yazr]

>>If you look at the experiment in detail, you'll find that it was set up in the AI's favor.

Could you elaborate on this ?

[yAnonymous]

Links were posted here: https://news.ycombinator.com/item?id=13535714

As expected, the AI is good at making technically correct decisions and "draining money" from a table by playing hands with sufficient data almost perfectly.

However, in decisive all-in situations with little information available, it supposedly wouldn't do so well, regardless of all the learning, but that's what it often comes down to.

>Nash Equilibrium is a strategy which ensures that the player who is using it will, at the very least, not fare worse than a player using any other strategy.

How do you make this work for situations that can cost you the game in one hand, with little information available? Without observing the opponent's behavior you can't, and for the AI that means it can be forced into making bad calls by playing aggressively, unless the game mode allows for avoiding such decisions, which was the case in this test.