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

Science. 2022 Dec 2;378(6623):990-996. doi: 10.1126/science.add4679. Epub 2022 Dec 1.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artificial Intelligence*
  • Humans
  • Reinforcement, Psychology*
  • Video Games*