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hubie [soylentnews.org] writes:

DeepNash learns to play Stratego from scratch by combining game theory and model-free deep RL [deepmind.com]:

Game-playing artificial intelligence (AI) systems have advanced to a new frontier. Stratego, the classic board game that's more complex than chess and Go, and craftier than poker, has now been mastered. Published in Science [science.org], we present DeepNash, an AI agent that learned the game from scratch to a human expert level by playing against itself.

DeepNash uses a novel approach, based on game theory and model-free deep reinforcement learning. Its play style converges to a Nash equilibrium, which means its play is very hard for an opponent to exploit. 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.

Board 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. Unlike chess and Go, Stratego is a game of imperfect information: players cannot directly observe the identities of their opponent's pieces.

[...] The value of mastering Stratego goes beyond gaming. 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. Our paper shows how DeepNash can be applied in situations of uncertainty and successfully balance outcomes to help solve complex problems.

[...] While we developed DeepNash for the highly defined world of Stratego, our novel R-NaD method can be directly applied to other two-player zero-sum games of both perfect or imperfect information. R-NaD has the potential to generalise far beyond two-player gaming settings to address large-scale real-world problems, which are often characterised by imperfect information and astronomical state spaces.

[...] In creating a generalisable AI system that's robust in the face of uncertainty, we hope to bring the problem-solving capabilities of AI further into our inherently unpredictable world.

Journal Reference:
Julien Perolat, Bart De Vylder, Daniel Hennes, et al., Mastering the game of Stratego with model-free multiagent reinforcement learning, Science, 378, 2022. https://doi.org/10.1126/science.add4679 [doi.org]

Original Submission