4 ms·
To expand on this - an LLM will try to play (and reason) like a person would, while a solver simply crunches the possibility space for the mathematically optima
by cowthulhu 9mo ago
To expand on this - an LLM will try to play (and reason) like a person would, while a solver simply crunches the possibility space for the mathematically optimal move.
It’s similar to how an LLM can sometimes play chess on a reasonably high (but not world-class) level, while Stockfish (the chess solver) can easily crush even the best human player in the world.
- postpriorx 9mo agoHow does a poker solver select bet size? Doesn't this depend on posteriors on the opponent's 'policy' + hand estimation?
- boscillator 9mo agoNo, I'm not super certain, but I believe most solvers are trained to be game theory optimal (GTO), which means they assume every other player is also playing GTO. This means there is no strategy which beats them in the long run, but they may not be playing the absolute best strategy.
- sejje 9mo agoTypically when you run a simulation on a hand, you give it some bet size options. To limit the scope of what it has to simulate. It's unlikely they're perfect, but there's very small differences in EV betting 100% vs 101.6% or whatever.
- meep_morp 9mo agoNot only to limit the scope of what it has to simulate, but only a certain number of bet sizes is practical for a human to implement in their strategy.
- Reason077 9mo agoGTO (“game theory optimal”) poker solvers are based around a decision tree with pre-set bet sizes (eg: check, bet small, bet large, all in), which are adjusted/optimized for stack depth and position. This simplifies the problem space: including arbitrary bet sizes would make the tree vastly larger and increase computational cost exponentially.
- iberator 9mo agoNash equilibrium. Optimal strategy for online poker has been known for like literally 20 years right now
- bogzz 9mo agoHow would an LLM play like a human would? I kind of doubt that there is enough recounting of poker hands or transcription of filmed poker games in the training data to imbue a human-like decision pattern.
- DiscourseFan 9mo agoUnlike Chess, in poker you don’t have perfect information, so there’s no real way to optimize it.
- tim-kt 9mo agoYou can still optimize for the expectation value, which is also essentially poker strategy.
- DiscourseFan 9mo agoAnybody who plays poker “optimally” is bound to lose money when they come up against anyone with skill. Once you know the strategy your opponent is employing you can play like you have anything. I believe I’ve won with 7,2 offsuite more than any other hand, because I played like I had the nuts.
- cowthulhu 9mo agoThis is completely wrong - the entire point of the Nash equilibrium solution (in the context of poker, at least) is that it is, at worst, EV-neutral even when your opponent has perfect knowledge of your strategy. Your 72o comment indicates you are either playing with very weak players, or have gotten lucky, as in reasonably competitive games playing (and then full bluffing) 72o will be significantly negative EV. Try grinding that strategy at a public 10/20 table and you will be quickly butchered and sent back to the ATM.
- DiscourseFan 9mo agoThere are numerous videos of high level professional poker players winning large hands with incredible bluffs, this whole "Nash equilibrium solution" is nothing more than a conjecture with some symbols thrown in. I will re-iterate, there is no such thing as perfect knowledge when you have imperfect information. If you play "optimally," you will get bluffed out of all your money the moment everyone else at the table figures out what you're doing.
- FergusArgyll 9mo agoYou are of course correct but to be pedantic: Stockfish isn't really a solver it's a neural net based engine