As a challenging multi-player card game, DouDizhu has recently drawn much attention for analyzing competition and collaboration in imperfect-information games. In this paper, we propose PerfectDou, a state-of-the-art DouDizhu AI system that dominates the game, in an actor-critic framework with a proposed technique named perfect information distillation. In detail, we adopt a perfect-training-imperfect-execution framework that allows the agents to utilize the global information to guide the training of the policies as if it is a perfect information game and the trained policies can be used to play the imperfect information game during the actual gameplay. To this end, we characterize card and game features for DouDizhu to represent the perfect and imperfect information. To train our system, we adopt proximal policy optimization with generalized advantage estimation in a parallel training paradigm. In experiments we show how and why PerfectDou beats all existing AI programs, and achieves state-of-the-art performance.

PerfectDou是一个基于actor-critic框架和perfect information distillation技术的DouDizhu AI系统，采用近端策略优化和广义优势估计以并行训练的方式训练。实验证明PerfectDou能够在DouDizhu游戏中击败所有现有的AI程序并达到最先进的性能水平。

完美信息提取：在斗地主中的压倒优势