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ReBeL was found to be effective in large scale two-player zero-sum imperfect-information games such as poker. However, it also has a few limitations.
Firstly, the amount of computational prowess of ReBeL is very high, especially in the context of certain games such as Recon Chess. It has strategic depth but very little common knowledge.
Secondly, since ReBeL depends on knowing the exact rules of the game, it may be useful for Go and poker where the rules and corresponding rewards are well known in advance.
However, the same cannot be said for real-world interactions. Author: Dane Mitrev. Leave A Reply Cancel Reply. For superior poker players who have missing all their chips on a undesirable hand it can be actually annoying to try to develop up their lender roll once again by taking part in rookie gamers in the reduced tables, they want to be right again up there enjoying from their close friends and poker gamers of a comparable skill degree.
These individuals are willing to fork out real money to reduce through the reduce tables and get back again to their elite games. Sample Page. Left sidebar.
Pluribus uses a technique called abstraction to group similar actions together and eliminate others reducing the scope of the decision.
The current version of Pluribus uses two types of abstractions:. 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.