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At the start of the iteration, MCCFR simulates a hand of poker based on the current strategy of all players which is initially completely random.
Once the simulated hand is completed, the algorithm reviews each decision the traverser made and investigates how much better or worse it would have done by choosing the other available actions instead.
Next, the AI assesses the merits of each hypothetical decision that would have been made following those other available actions, and so on.
The difference between what the traverser would have received for choosing an action versus what the traverser actually achieved in expectation on the iteration is added to the counterfactual regret for the action.
Using that strategy, Pluribus was able to master poker in eight days on a core server and required less than GB of RAM.
No GPUs were used. The blueprint strategy is too expensive to use real time in a poker game. During actual play, Pluribus improves upon the blueprint strategy by conducting real-time search to determine a better, finer-grained strategy for its particular situation.
Traditional search strategies are very challenging to implement in imperfect information games in which the players can change strategies at any time.
Pluribus instead uses an approach in which the searcher explicitly considers that any or all players may shift to different strategies beyond the leaf nodes of a subgame.
Specifically, rather than assuming all players play according to a single fixed strategy beyond the leaf nodes, Pluribus assumes that each player may choose among four different strategies to play for the remainder of the game when a leaf node is reached.
This technique results in the searcher finding a more balanced strategy that produces stronger overall performance.