Automatic Bridge Bidding Using Deep Reinforcement Learning
This work addresses the bottleneck of AI in bridge bidding, offering a novel approach that could enhance automated gameplay for enthusiasts and researchers, though it is incremental in advancing beyond human-designed systems.
The paper tackled the problem of bridge bidding, a cooperative decision-making challenge under partial information, by proposing a deep reinforcement learning model that learns to bid from raw card data without human domain knowledge, achieving superior performance compared to a champion-winning computer bridge program.
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.