![]() ![]() And it’s a zero-sum game, so any gain by one player represents a loss of the same magnitude for their opponent. It’s a game of bluff and tactics, of information gathering and subtle manoeuvring. Stratego is a turn-based, capture-the-flag game. Our paper shows how DeepNash can be applied in situations of uncertainty and successfully balance outcomes to help solve complex problems. ![]() In pursuit of our mission of solving intelligence to advance science and benefit humanity, we need to build advanced AI systems that can operate in complex, real-world situations with limited information of other agents and people. The value of mastering Stratego goes beyond gaming. For this reason, DeepNash goes far beyond game tree search altogether. ![]() It also means that a very successful AI technique called “game tree search”, previously used to master many games of perfect information, is not sufficiently scalable for Stratego. This complexity has meant that other AI-based Stratego systems have struggled to get beyond amateur level. Unlike chess and Go, Stratego is a game of imperfect information: players cannot directly observe the identities of their opponent's pieces. So hard, in fact, that DeepNash has reached an all-time top-three ranking among human experts on the world’s biggest online Stratego platform, Gravon.īoard games have historically been a measure of progress in the field of AI, allowing us to study how humans and machines develop and execute strategies in a controlled environment. Its play style converges to a Nash equilibrium, which means its play is very hard for an opponent to exploit. Published in Science, we present DeepNash, an AI agent that learned the game from scratch to a human expert level by playing against itself.ĭeepNash uses a novel approach, based on game theory and model-free deep reinforcement learning. Stratego, the classic board game that’s more complex than chess and Go, and craftier than poker, has now been mastered. Game-playing artificial intelligence (AI) systems have advanced to a new frontier. DeepNash learns to play Stratego from scratch by combining game theory and model-free deep RL ![]()
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