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>10,000 hands in an interesting number. If you search the poker forums, you'll see this is the number you'll see people throw out there for how many hands you need to see before you can analyze your play. You then make adjustments and see another 10,000 hands before you can assess those changes.

If you read the paper/facebook post[0] (no idea why this worse article is the link here) - you'll see they address this.

>Although poker is a game of skill, there is an extremely large luck component as well. It is common for top professionals to lose money even over the course of 10,000 hands of poker simply because of bad luck. To reduce the role of luck, we used a version of the AIVAT variance reduction algorithm, which applies a baseline estimate of the value of each situation to reduce variance while still keeping the samples unbiased. For example, if the bot is dealt a really strong hand, AIVAT will subtract a baseline value from its winnings to counter the good luck. This adjustment allowed us to achieve statistically significant results with roughly 10x fewer hands than would normally be needed.

0. https://ai.facebook.com/blog/pluribus-first-ai-to-beat-pros-...

>Remember that one of the most exciting events in online poker was the run of isildur1. That run was put to rest when he went bust against players who had studied thousands of his hand histories.

Perhaps more famously, Jungleman compiled hand histories from many different people while he was playing Tom Dwan in the 'durrrr' challenge (which I guess technically isn't over....)

You clearly didn’t read the additional links they posted. They mentioned why they chose 10k (AIVAT), and it goes far beyond any of the variables you mentioned.

For any number of hands, my money is on the bot.

That really doesn't address the point that was raised. It's not that the bot wins through luck and that 10k is too small a sample, it's that a good professional poker player isn't good over 10k hands, they're good over 5 years.

Any good player will have their play analyzed and responded to, so there's a feedback loop there - any good player will have their play analyzed, exploited and will have to re-adjust their strategy to respond to exploitative play. The question is: How does the AI strategy adapt over time to players who know the hand history of the AI strategy. That's an extremely important part of being a top level player. To give you an example - if you watch Daniel Negreanu's vlog about his time at the WSOP he actively talks about changing his strategy in response to his analysis of different players' profiles. This is especially important in Sit & Go where at high stakes you'll have regular grinders who build up reputations - less so in tournaments where you're less likely to meet any given player.

This will be interesting to see.

Brown and Sandholm's algorithm aims to play a Nash Equilibrium which by deifnition _cannot_ be exploited by a single opponent player as long as all players are playing the equilibrium strategy. As they note in the paper this gives you a strong optimality guarantee in the 2-player setting. It was unclear whether this would transfer to real-world winnings in the multi-player case, and while it looks like it does for now (for current strategy-profiles of human players) humans might be able to adapt to the strategy played by the bot. Given the fact that the bot wins against current human strategy-profiles in the n-player setting, it's likely (but not a sure thing) that human players will have to team-up against the bot to exploit it. That seems rather unlikely to me.