Board games allow us to gauge and evaluate how humans and machines develop and execute strategies in a controlled environment. Progress in Artificial Intelligence (AI) has been measured via mastery of board games since the inception of the field. 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.ĭeepNash 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. DeepNash uses a game-theoretic, model-free deep reinforcement learning method, without search, that learns to master Stratego via self-play. Decisions in Stratego are made over a large number of discrete actions with no obvious link between action and outcome.Įpisodes 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.įor 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. It has the additional complexity of requiring decision-making under imperfect information, similar to Texas hold’em poker, which has a significantly smaller game tree (on the order of 10 164 nodes). This popular game has an enormous game tree on the order of 10 535 nodes, i.e., 10 175 times larger than that of Go. Stratego is one of the few iconic board games that Artificial Intelligence (AI) has not yet mastered. from scratch, up to a human expert level. We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego 3 3 3 Stratego is a trademark of Jumbo Diset Group, and is used in this publication for information purposes only.

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