Pluribus Poker Ranges – RFI LJ

Let’s us look at the opening ranges of Pluribus when UTG/LJ, basically first to speak pre-flop; he can fold, limp, or bet… and actually never limps.

So the big surprise, if any, in the range is the bet sizes, going from 2BB to 3.5BB. Some of the other pros had a bit of variability there but not that much. Pluribus opens 18.6% from the LJ, which is a bit wider than what we’ve been used to with Snowie and others.

The only “real” surprise in actual hands is probably suited Kings, playing 100% with K8s, maybe with K7s (only 2 hands), and 2/3 of K6s. I find very odd that once each, AKo and JJ were not opened. Then more suited connectors than we usually see, with apparently a dislike for 87s. Obviously in order to open a bit wider, there are a few more offsuit broadways mixed in, like KT/JT at low frequency, and KJ at 55%.

Another surprise for me is there is not that much mixing up the hands, some, but very reasonable. Sizes however are all over the place.

Displaying here ranges with some additional data on number of times Pluribus actually got the hand, percent open with it, and average, min, max bet sizes. Because the statistical significance of frequencies is limited (although not too bad for RFI), it is important to consider how many times Pluribus was in a given situation. For instance, he got AJo 17 times, and bet each time, we can be confident he opens at a high frequency with AJo. On the opposite with T8s, he got the hand 4 times at the LJ, and bet once, so we compute 25% but that could end up being 10% or 50%, all we know is sometimes he bets it, sometimes not.

So it is important to not take percentages for granted. Still the chart gives a very good idea of how Pluribus opens.

Pluribus AI poker range RFI LJ
Pluribus AI poker range RFI LJ

Bet sizings are best looked as a distribution with percentage per sizing, in number of BBs. In general I would say sizing lower than what we’re used to, but with a lot of variation with sizes going up to 3.5BB. Either the CFR algorithms never converge on opening sizes so it stays more or less random or there is a real reason. Can’t really see what this achieves, again some of the pros did vary their sizings slightly so there must be a good reason to do it. What do you think ?

Pluribus poker bet sizing RFI LJ
Pluribus poker bet sizing RFI LJ

Pluribus Ai dominates… by losing 70K$ to top pro poker players (bonus Pluribus VPIP)

Pluribus poker AI is the greatest AI and poker news of 2019. We can find out how Pluribus plays poker. To great fanfare, Facebook and Carnegie Mellon University announced the domination of AI at 6max NLHM poker. With marketing savvy, the article in venerable Science magazine was published at the peak of the WSOP championships of poker on 11 July 2019. Domination was demonstrated in mbb/100 using AIVAT, a sophisticated variance (luck) removal algorithm that works for AI machines, not for human. So luck removed for Pluribus, maintained for humans, sort of thing.

Funny enough, the researchers omitted to publish actual results, ie 70k losses this time, but did not hesitate to publish them in their previous venture when Libratus beat pros at heads up NLHM 2 years ago. So although a bit is playing, we witness very human behaviour. It’s like any random poker player, when she wins, she brags about the results, when he loses, he blames it on variance or bad beats! I guess the difference is these guys are actually able to prove it.

Anyway the thing is, AVAIT is way beyond my understanding but widely recognised as valid in AI academic circles. So let’s admit it Pluribus AI does dominate.

It just shows the extraordinary variance of poker. In other words, considering reasonable 6 hour sessions, being the best poker player in the world, after 50 live sessions or 16 online sessions, you can easily be down 70k and worry whether it is bad play or bad luck. Yeah, don’t quit your day job.

The thing is, previous advances in AI, by University of Alberta Cepheus for limit heads-up and Deepstack for heads up no limit and CMU Libratus were a revolution for all decent poker players introducing GTO and Nash theorem to the masses and yielding innumerable threads on GTO vs exploitative strategies.

So the cool thing is CMU did post the 10k hands, and they’re all in my database, so I’ll be writing a series of posts analysing how Pluribus plays poker (and the pros play). 10k hands is not that many, but believe me there are learnings, like opening ranges, bet sizings, easy SB opening strategy, irrelevance of Cbet? Etc.

By the way, Pluribus doesn’t play GTO strictly speaking and its algos don’t converge to a Nash equilibrium. Still it’s trying not to be exploited, not to exploit, although I do have some doubts on the post blueprint searches if they’re based on hands against humans, maybe that’s slightly exploitative, but this is total speculation and the researchers say it doesn’t take other players actions into account.

In the first post, I’ll focus on LJ RFI ranges (pre-flop first to speak, what do we open with)

And for now, just one stat, VPIP (voluntarily put money in pot, ie percentage of hands where pluribus decided to play/risk money) is 26.4%, no big surprise here, many of us are around 24% and if we played postflop perfectly, sure we’d open a bit more…