论文标题
多人游戏不完美信息游戏中的对手建模
Opponent Modeling in Multiplayer Imperfect-Information Games
论文作者
论文摘要
在许多现实世界中，代理商与可以采用各种策略的多个相对代理进行战略互动。为这种设置设计代理的标准方法是计算或近似相关的游戏理论解决方案概念，例如NASH平衡，然后遵循规定的策略。但是，这种策略忽略了对反对者比赛的任何观察结果，这可能表明可以利用的缺点。我们在多人游戏不完美的信息游戏中提出了一种对手建模的方法，在该游戏中，我们通过反复的互动来收集对手游戏的观察。我们对三人库恩扑克中的各种真实对手和确切的纳什均衡策略进行了实验，并表明我们的算法显着胜过所有代理，包括确切的NASH平衡策略。
In many real-world settings agents engage in strategic interactions with multiple opposing agents who can employ a wide variety of strategies. The standard approach for designing agents for such settings is to compute or approximate a relevant game-theoretic solution concept such as Nash equilibrium and then follow the prescribed strategy. However, such a strategy ignores any observations of opponents' play, which may indicate shortcomings that can be exploited. We present an approach for opponent modeling in multiplayer imperfect-information games where we collect observations of opponents' play through repeated interactions. We run experiments against a wide variety of real opponents and exact Nash equilibrium strategies in three-player Kuhn poker and show that our algorithm significantly outperforms all of the agents, including the exact Nash equilibrium strategies.
