Games are a simplified model of reality and often serve as a favored platform for Artificial Intelligence (AI) research. Much of the research is concerned with game-playing agents and their decision making processes. The game of Guandan (literally, "throwing eggs") is a challenging game where even professional human players struggle to make the right decision at times. In this paper we propose a framework named GuanZero for AI agents to master this game using Monte-Carlo methods and deep neural networks. The main contribution of this paper is about regulating agents' behavior through a carefully designed neural network encoding scheme. We then demonstrate the effectiveness of the proposed framework by comparing it with state-of-the-art approaches.

翻译：游戏是现实世界的简化模型，常作为人工智能研究的重要平台。相关研究主要关注游戏智能体及其决策过程。掼蛋作为一种具有挑战性的纸牌游戏，即便是专业人类玩家也时常难以做出正确决策。本文提出名为GuanZero的框架，使AI智能体能够通过蒙特卡洛方法与深度神经网络掌握该游戏。本文的主要贡献在于通过精心设计的神经网络编码方案对智能体行为进行调控。通过与国际领先方法的对比实验，我们验证了所提框架的有效性。