DeepNash beats existing state-of-the-art AI methods in Stratego and achieved a yearly (2022) and all-time top-3 rank on the Gravon games platform, competing with human expert players. The army is used to steal the other flag and to protect your own flag. Each player has an army of men and some bombs that they can use during play. Each players goal is to steal the flag of their opponent and to protect their own flag from being taken. The Regularised Nash Dynamics (R-NaD) algorithm, a key component of DeepNash, converges to an approximate Nash equilibrium, instead of 'cycling' around it, by directly modifying the underlying multi-agent learning dynamics. Stratego is a game of armies and their battlefield. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Star Notifications Code Issues 0 Pull requests 0 Actions Projects 0 Security Insights tamiadeola/Stratego. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. A C++ Implementation of the board game Stratego. For these reasons, Stratego has been a grand challenge for the field of AI for decades, and existing AI methods barely reach an amateur level of play. Episodes are long, with often hundreds of moves before a player wins, and situations in Stratego can not easily be broken down into manageably-sized sub-problems as in poker. Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome. This popular game has an enormous game tree on the order of $10^$ nodes). Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. Download a PDF of the paper titled Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning, by Julien Perolat and 33 other authors Download PDF Abstract:We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level.
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