Automatic Bridge Bidding Using Deep Reinforcement Learning

By Chih-Kuan Yeh, Cheng-Yu Hsieh, Hsuan-Tien Lin. 2016

46 citations recorded by the source corpus.

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 with 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 flexible and pioneering bridge-bidding system, which can learn either with or 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. We further study how different pieces of human knowledge can be exploited to assist the model. Our experiments demonstrate the promising performance of our proposed model. In particular, the model can advance from having no knowledge on bidding to achieving a superior performance compared with a champion-winning computer bridge program that implements a human-designed bidding system. In addition, further synergies can be extracted by incorporating expert knowledge into the proposed model.

Subfields: Control and Systems Engineering, Mechanics of Materials

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Mechanics of Materials

Citations in the corpus

The Hanabi Challenge: A New Frontier for AI Research (2019, background): “; communication channel: any signalling must be done through the actions played. It is therefore more similar in spirit to learning how to relay information to partners in the bidding round of bridge [76]. 5.3. Pragmatics, Beliefs, and Agent Modelling Hanabi provides AI practitioners with an interes”
Learning to Communicate Implicitly by Actions (2018, methodology): “We conduct an ablation study to better understand the importance of these elements and compare against a stateof-the-art method in PQL (Yeh and Lin 2016).”
Joint Policy Search for Multi-agent Collaboration with Imperfect Information (2020, methodology): “We use baseline16 [43], baseline19 [32] and baseline [18] as our baselines, all are neural network based methods.”