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Mastering the game of Stratego with model-free multiagent reinforcement learning.

In Science (New York, N.Y.)

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.

Perolat Julien, De Vylder Bart, Hennes Daniel, Tarassov Eugene, Strub Florian, de Boer Vincent, Muller Paul, Connor Jerome T, Burch Neil, Anthony Thomas, McAleer Stephen, Elie Romuald, Cen Sarah H, Wang Zhe, Gruslys Audrunas, Malysheva Aleksandra, Khan Mina, Ozair Sherjil, Timbers Finbarr, Pohlen Toby, Eccles Tom, Rowland Mark, Lanctot Marc, Lespiau Jean-Baptiste, Piot Bilal, Omidshafiei Shayegan, Lockhart Edward, Sifre Laurent, Beauguerlange Nathalie, Munos Remi, Silver David, Singh Satinder, Hassabis Demis, Tuyls Karl

2022-Dec-02