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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. Results showed that ReBeL achieves superhuman performance in playing Poker.
More details about the new method can be read in the official blog post. I had a long conversation with one of my colleagues about imperfect information games and deep learning this weekend and reminded me of an article I wrote last year so I decided to republish it.
The reasons why Pluribus represents a major breakthrough in AI systems might result confusing to many readers. All those games are constrained to only two players and are zero-sum games meaning that whatever one player wins, the other player loses.
In AI theory, poker is classified as an imperfect-information environment which means that players never have a complete picture of the game.
No other game embodies the challenge of hidden information quite like poker, where each player has information his or her cards that the others lack.
Additionally, an action in poker in highly dependent of the chosen strategy. In perfect-information games like chess, it is possible to solve a state of the game ex: end game without knowing about the previous strategy ex: opening.
In poker, it is impossible to disentangle the optimal strategy of a specific situation from the overall strategy of poker. In the classic rock-paper-scissors game, the Nash equilibrium strategy is to randomly pick rock, paper, or scissors with equal probability.
The challenge with the Nash equilibrium is that its complexity increases with the number of players in the game to a level in which is not feasible to pursue that strategy.
In the case of six-player poker, achieving a Nash equilibrium is computationally impossible many times.
The success in poker relies on effectively reasoning about hidden information, picking good action and ensuring that a strategy remains unpredictable.