Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating

• URL: http://arxiv.org/abs/2402.13582v1
• Date: Wed, 21 Feb 2024 07:26:06 GMT
• Title: Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
• Authors: Yifan Yanggong, Hao Pan, Lei Wang
• Abstract summary: We propose a framework named GuanZero for AI agents to master the game of Guandan. The main contribution of this paper is about regulating agents' behavior through a carefully designed neural network encoding scheme.
• Score: 16.718186690675164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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