The AI doesn't adapt to the opponents, and that's still an interesting challenge for AI research. That said, at the end of the day, it was making quite a bit of money playing against elite human pros. I think that suggests the cliche is, at least in part, wrong.
Making "quite a bit of money" still leaves open the possibility that the AI is leaving a lot of money on the table by not taking opponents into consideration.
Also I would be curious to see how it performs against people that aren't "elite human pros". Would this AI win at a higher rate in a game against average recreational players compared to the rate a pro would win?
Lastly it is also possible that the pros simply didn't have enough time to adapt to the AI which would be extra important considering the AI plays unlike humans and therefore is harder to predict.
I think the bot would make a lot of money playing against average recreational players, but it's absolutely true that if you can exploit bad players' weaknesses, then you can make more money than what the bot would earn.
We played 10,000 hands over 12 days in the 5 humans + 1 AI experiment. That's quite a long time, and there's no indication that they even began to uncover any weaknesses in that time period. So I'm fairly confident the AI is robust to exploitation, and I think that's a very important quality to have in any AI system.
That 10,000 total hands number isn't particularly meaningful on the point of adaptability because the humans aren't sharing information with each other. The important number is how many hands each individual human played against the AI. Another question would be whether the pros knew which player was the AI? Because if they didn't, you are basically throwing a modified Turing Test against the pros before they can even begin to try to find tendencies in the AI. Predicting opponents is a huge part of how people play poker. If the AI plays unlike any human, pros are at huge disadvantage against an AI compared to how they would fair against a similarly skilled but more traditional human player.
None of this is meant to diminish what you all accomplished, I'm just highlighting areas of poker in which this AI would be less successful than humans even if it is more successful overall.
There was an interesting IRL poker game a few years ago. The player who was running behind started going all in on every hand without even looking at their hand (with a huge amount of success).
Out of curiosity, how does a bot deal with oddities things like this?
This is a solved problem. Open-shoving is a feature of sit-n-gos, so of course people have simulated these and compiled so called "pushbot tables". The parameters are basically pot size and winning probabilities against a random hand.
While this particular bot may not have those programmed in, a more powerful variant eventually will.
The mathematical theory explored in the paper is that if multiplayer poker isn't one of the multiplayer finite state games that pathologically fails to converge to a Nash equilibrium, then it has one, and this strategy should approximate it. Intuitions about adaptability and the advantages thereof aren't applicable in the scenario where the opponent is playing to a Nash equilibrium. You can perform equally well by participating in the other side of the Nash equilibrium, but anything else is a losing strategy. The fact that this approximation converges to a strategy that's actually really good suggests that there is a Nash equilibrium, and that the converged-upon strategy is converging on it.
You can't out-think or adapt to a rock-paper-scissors opponent who selects at random. All you can do is also select at random and accept that the two of you have even odds.
