Bridge is among the zero-sum games for which artificial intelligence has not yet outperformed expert human players. The main difficulty lies in the bidding phase of bridge, which requires cooperative decision making under partial information. Existing artificial intelligence systems for bridge bidding rely on and are thus restricted by human-designed bidding systems or features. In this work, we propose a pioneering bridge bidding system without the aid of human domain knowledge. The system is based on a novel deep reinforcement learning model, which extracts sophisticated features and learns to bid automatically based on raw card data. The model includes an upper-confidence-bound algorithm and additional techniques to achieve a balance between exploration and exploitation. Our experiments validate the promising performance of our proposed model. In particular, the model advances from having no knowledge about bidding to achieving superior performance when compared with a champion-winning computer bridge program that implements a human-designed bidding system.

本研究提出了一种新型的深度强化学习模型，不依赖于人类领域知识，基于原始卡牌数据自动学习出复杂的特征并完成桥牌叫牌，实验证明该模型在与实现人类设计叫牌系统的计算机桥牌程序进行对比时表现出了优异的性能。

基于深度强化学习的自动桥牌叫牌