Human strategic decision making in parametrized games

Strong algorithms have been developed for game classes with many elements of complexity. For example, algorithms were recently able to defeat human professional players in 2-player [16, 3] and 6-player no-limit Texas hold'em [4]. These games have imperfect information, sequential actions, very large state spaces, and the latter has more than two players (solving multiplayer games is more challenging than two-player zero-sum games from a complexity-theoretic perspective). However, these algorithms all require an extremely large amount of computational resources for offline and/or online computations and for optimizing neural network hyperparameters. The algorithms also have a further limitation in that they are using all these resources just to solve for one very specific version of the game (e.g., Libratus and DeepStack assumed that all players start the hand with 200 times the big blind, and Pluribus assumed that all players start the hand with 100 times the big blind).