  Games are abstractions of the real world, where artificial agents learn to
compete and cooperate with other agents. While significant achievements have
been made in various perfect- and imperfect-information games, DouDizhu (a.k.a.
Fighting the Landlord), a three-player card game, is still unsolved. DouDizhu
is a very challenging domain with competition, collaboration, imperfect
information, large state space, and particularly a massive set of possible
actions where the legal actions vary significantly from turn to turn.
Unfortunately, modern reinforcement learning algorithms mainly focus on simple
and small action spaces, and not surprisingly, are shown not to make
satisfactory progress in DouDizhu. In this work, we propose a conceptually
simple yet effective DouDizhu AI system, namely DouZero, which enhances
traditional Monte-Carlo methods with deep neural networks, action encoding, and
parallel actors. Starting from scratch in a single server with four GPUs,
DouZero outperformed all the existing DouDizhu AI programs in days of training
and was ranked the first in the Botzone leaderboard among 344 AI agents.
Through building DouZero, we show that classic Monte-Carlo methods can be made
to deliver strong results in a hard domain with a complex action space. The
code and an online demo are released at https://github.com/kwai/DouZero with
the hope that this insight could motivate future work.
