Deep reinforcement learning has made significant progress in games with
imperfect information, but its performance in the card game Doudizhu (Chinese
Poker/Fight the Landlord) remains unsatisfactory. Doudizhu is different from
conventional games as it involves three players and combines elements of
cooperation and confrontation, resulting in a large state and action space. In
2021, a Doudizhu program called DouZero\cite{zha2021douzero} surpassed previous
models without prior knowledge by utilizing traditional Monte Carlo methods and
multilayer perceptrons. Building on this work, our study incorporates residual
networks into the model, explores different architectural designs, and conducts
multi-role testing. Our findings demonstrate that this model significantly
improves the winning rate within the same training time. Additionally, we
introduce a call scoring system to assist the agent in deciding whether to
become a landlord. With these enhancements, our model consistently outperforms
the existing version of DouZero and even experienced human players.
\footnote{The source code is available at
https://github.com/Yingchaol/Douzero_Resnet.git.

本研究中，我们将残差网络引入到 DouZero 模型中，探索不同的架构设计并进行多角色测试，发现该模型在相同训练时间内显著提高了胜率。我们还引入了一个呼叫得分系统来辅助代理决定是否成为地主，通过这些改进，我们的模型始终优于现有版本的 DouZero 甚至有经验的人类玩家。