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

Autor:	Yanggong, Yifan, Pan, Hao, Wang, Lei
Rok vydání:	2024
Předmět:	Computer Science - Artificial Intelligence Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.13582 Zobrazit plný text záznamu View this record from Arxiv