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• W4310705874 abstract "We introduce DeepNash, an autonomous agent that plays the imperfect information game Stratego at a human expert level. Stratego is one of the few iconic board games that artificial intelligence (AI) has not yet mastered. It is a game characterized by a twin challenge: It requires long-term strategic thinking as in chess, but it also requires dealing with imperfect information as in poker. The technique underpinning DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego through self-play from scratch. DeepNash beat existing state-of-the-art AI methods in Stratego and achieved a year-to-date (2022) and all-time top-three ranking on the Gravon games platform, competing with human expert players." @default.
• W4310705874 created "2022-12-16" @default.
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• W4310705874 date "2022-12-02" @default.
• W4310705874 modified "2023-10-10" @default.
• W4310705874 title "Mastering the game of Stratego with model-free multiagent reinforcement learning" @default.
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• W4310705874 doi "https://doi.org/10.1126/science.add4679" @default.
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