Quick Notes on Poker Solver

Science published a review of poker solver algorithm.

Researchers focus on two-player limit Texas Hold’em. Their grand scheme is this:

  1. Transform a poker game to an abstract game that has a smaller state space.
  2. Apply an equilibrium solver algorithm on the abstract game.
  3. Map the equilibrium back to the original game.

There are lossy and lossless transformations. A finer-grained lossy transformation is usually but not always better than a coarse-grained lossy transformation.

There are two popular families of equilibrium solver:

  • General purpose linear programming solver.
  • Counterfactual regret (CFR) minimization.

I am not sure what kind of equilibrium they are looking for. Subgame-perfect equilibrium? Sequential equilibrium? Or just simple Nash equilibrium?

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