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Facebook Open Sources ReBeL, a New Reinforcement Learning Agent that Excels at Poker and Other Imperfect-Information Games
The new model tries to recreate the reinforcement learning and search methods used by AlphaZero in imperfect information scenarios.
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Poker has been considered by many the core inspiration for the formalization of game theory. John von Neuman was reportedly an avid poker fan and use many analogies of the card game while creating the foundation of game-theory. With the advent of artificial intelligence(AI) there have been many attempts to master different forms of poker, most of them with very limited results. Last year, researchers from Facebook and Carnegie Mellon University astonishing the AI world by unveiling Pluribus, an AI agent that beat elite human professional players in the most popular and widely played poker format in the world: six-player no-limit Texas Hold’em poker. Since then, a question that has hunted AI researchers is whether the skills acquired by models like Pluribus can be used in other imperfect information games. A few days ago, Facebook again used poker as the inspiration for Recursive Belief-based Learning (ReBeL), a reinforcement learning model that is able to master several imperfect-information games.
The inspiration from ReBeL comes from DeepMind’s AlphaZero. After setting up new records in the Go game with the development of AlphaGo, DeepMind expanded iits efforts to other perfect-information games such as Chess, or Shogi. The result was AlphaZero, a reinforcement agent that was able to master all these games from scratch. Of course, recreating the magic of AlphaZero in imperfect-information…
