  Artificial intelligence for card games has long been a popular topic in AI
research. In recent years, complex card games like Mahjong and Texas Hold'em
have been solved, with corresponding AI programs reaching the level of human
experts. However, the game of Dou Di Zhu presents significant challenges due to
its vast state/action space and unique characteristics involving reasoning
about competition and cooperation, making the game extremely difficult to
solve.The RL model DouZero, trained using the Deep Monte Carlo algorithm
framework, has shown excellent performance in DouDiZhu. However, there are
differences between its simplified game environment and the actual Dou Di Zhu
environment, and its performance is still a considerable distance from that of
human experts. This paper modifies the Deep Monte Carlo algorithm framework by
using reinforcement learning to obtain a neural network that simultaneously
estimates win rates and expectations. The action space is pruned using
expectations, and strategies are generated based on win rates. This RL model is
trained in a realistic DouDiZhu environment and achieves a state-of-the-art
level among publicly available models.
