Abstract
Artificial Intelligence has seen several breakthroughs in two-player
perfect information game. Nevertheless, Doudizhu, a three-player
imperfect information game, is still quite challenging. In this paper,
we present a Doudizhu AI by applying deep reinforcement learning from
games of self-play. The algorithm combines an asymmetric MCTS on nodes
of information set of each player, a policy-value network that
approximates the policy and value on each decision node, and inference
on unobserved hands of other players by given policy. Our results show
that self-play can significantly improve the performance of our agent in
this multi-agent imperfect information game. Even starting with a weak
AI, our agent can achieve human expert level after days of self-play
and training.
Keywords
- Heuristic Search and Game Playing: Game Playing and Machine Learning
- Machine Learning: Reinforcement Learning
- Uncertainty in AI: Approximate Probabilistic Inference
