  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.
