It seems, that they do ignore the opponent action. In the video on the DeepStack site, M. Bowling said, that the counterfactual values of the last resolve are an upper bound to the counterfactual values after the opponent action, so they can be used.

One thing, that I don't udnerstand though is, how do they innitialize the counterfactual values in the root? They said, that they initialize it to the value of being dealt the hand, but what does that mean? I kind of assume that they use counterfactual values, that they computed from a full cfr solution from one of their earlier bots or something, but on the other hands that would be weird.

With regards to the look-ahead network computed from the next public state, I totally understand. However, my comment was concerning the live resolve of the current street. I don't see how the opponent's action can be ignored there.

Lets say the opponent bets 2/3 pot on the turn. DeepStack can't possibly ignore that action and just consider the pot, stack size and resulting sub-game after the bet, or can it?

With regards to the look-ahead network computed from the next public state, I totally understand. However, my comment was concerning the live resolve of the current street. I don't see how the opponent's action can be ignored there.

Lets say the opponent bets 2/3 pot on the turn. DeepStack can't possibly ignore that action and just consider the pot, stack size and resulting sub-game after the bet, or can it?

In this case, my understanding was that they solved the entire street (the turn) with CFRM, using the look-ahead network to get regrets or value for the river. With the inputs to the simulation being the opponent's regrets, DeepStack's range, the pot and the stack size. Then after the solve, they select an action based on whatever the opponent actually did.

The value-function (the neural network precedent trained) return the counterfactual utility approximation of any possible hands for the opponents taking as input only the pot-size and the deep-stack range. So during the simulation that algorithm doesn't consider the precedent action or the size but only the pot-size.
The abstraction is implicit and continuos in the network that produce the value-function but they don't map anything in an explicit way

Excited to finally see them going for the much more scalable approach of online solving combined with trained models for look-ahead, instead of sticking to precomputed strategies.

One thing confuses me though, they say that they ignore the opponent's actual action when doing the recalc. Does that mean they ignore the opponent's bet size as well, and then just map it to one of the "2 or 3 bet/raise actions" post-recalc? Why not consider the actual size as an optional path?

Also for their own bets, I don't see any mention of bet sizing, which leads me to believe they used the same ½P, P, 2P, All-in sizings they used for training the networks(?) Again it sounds like they're leaving an unnecessary amount of chips on the table.

Either way, impressive results!

One thing confuses me though, they say that they ignore the opponent's actual action when doing the recalc. Does that mean they ignore the opponent's bet size as well, and then just map it to one of the "2 or 3 bet/raise actions" post-recalc? Why not consider the actual size as an optional path?