User:MuthanaAlhadrab/sandbox

= ReBeL = ReBel (Recursive Belief-based Learning) is an algorithm that encompasses the realm of imperfect-information games, particularly in the game of poker. It combines Deep Reinforcement Learning and search to converge to a Nash equilibrium in two-player zero-sum games, to achieve superhuman performance in poker and other imperfect-information games. The algorithm was introduced by Noam Brown, Anton Bakhtin, and other members of the Facebook AI Research group in their 2020 paper "Combining Deep Reinforcement Learning and Search for Imperfect-Information Games".

Background
will be some background and success of ReBel.

How ReBel Works
ReBel's combination of deep reinforcement learning and search is also similar to the AlphaZero algorithm, which was developed by DeepMind and achieved superhuman performance in the games of chess, shogi, and Go. However, unlike AlphaZero, ReBel is designed specifically for imperfect-information games, where players do not have complete information about their opponent's cards or strategies.

ReBel is based on the Counterfactual Regret Minimization (CFR) algorithm, which is a powerful algorithm for solving imperfect-information games, but it can be computationally expensive for large games. ReBel improves on CFR by using a neural network to learn an approximate strategy and then refining it with search. This makes ReBel significantly more efficient than CFR and able to solve larger games.

ReBel's deep learning component is based on the concept of partial observability, and partially observable systems, which is well-established in the field of artificial intelligence. Partial observability refers to situations where an agent does not have complete information about the state of the environment and must make decisions based on probabilistic beliefs about the state. This concept is central to the theory of partially observable stochastic domains.

One of the novel aspects of ReBel is its incorporation of a comprehensive critic. This critic helps improve the stability and performance of self-play reinforcement learning by providing valuable feedback on the agent's decisions. The comprehensive critic can evaluate the quality of a state or action in various aspects, such as immediate rewards, long-term outcomes, and strategic implications.