What Do You Think? What's Your Reaction? In Love. Not Sure. Shraddha Goled I am a journalist with a postgraduate degree in computer network engineering.
Who we are Mentoring. Although previous research has shown that there deep neural networks can beat humans in those games, no generic algorithm has been proposed that can play multiple games.
In a recent study, researchers from FAIR Facebook AI Research have explored the possibility of using Reinforcement Learning RL and search for developing an AI algorithm that can be competitive in both perfect-information and imperfect-information games.
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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.
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