  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
\url{https://github.com/Yingchaol/Douzero_Resnet.git.}
