  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.
