Mastering the game of Stratego with model-free multiagent reinforcement learning
Mastering the game of Stratego with model-free multiagent reinforcement learning
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DOI：
10.1126/science.add4679
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发表时间：
2022-12-02
期刊：
影响因子：
56.9
通讯作者：
Tuyls, Karl
中科院分区：
文献类型：
--
作者：
Perolat, Julien;De Vylder, Bart;Tuyls, Karl
我们介绍DeepNash，一个自治代理，在人类专家水平上玩不完美信息游戏。围棋是人工智能（AI）尚未掌握的少数标志性棋盘游戏之一。这是一个具有双重挑战的游戏：它需要像国际象棋那样的长期战略思维，但也需要像扑克那样处理不完美的信息。支撑DeepNash的技术使用了一种基于博弈论的无模型深度强化学习方法，无需搜索，可以从头开始学习掌握自我游戏。DeepNash在Gravon游戏平台上击败了现有的最先进的AI方法，并在Gravon游戏平台上取得了今年迄今为止（2022年）和历史上的前三名，与人类专家玩家竞争。
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 stateof-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.