Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating
<|reference_start|>Mastering the Game of Guandan with Deep Reinforcement Learning and Behavior Regulating: 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.<|reference_end|>
@article{yanggong2024mastering,
    title={Mastering the Game of Guandan with Deep Reinforcement Learning and
  Behavior Regulating},
    author={Yifan Yanggong, Hao Pan, Lei Wang},
    journal={arXiv preprint arXiv:2402.13582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13582},
    primaryClass={cs.AI cs.LG}
}