# Automatic Bridge Bidding Using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1607.03290 · HTML: https://mlsanity.com/paper/1607.03290/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jul 2016.

## Built on by (most cited first, 6 of 6)

- [Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi](https://mlsanity.com/paper/2107.07630/) (2021)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [Causal Reinforcement Learning for Complex Card Games: A Magic The Gathering Benchmark](https://mlsanity.com/paper/2605.06066/) (2026)
- [Improving Bidding and Playing Strategies in the Trick-Taking game Wizard using Deep Q-Networks](https://mlsanity.com/paper/2205.13834/) (2022)
- [BridgeHand2Vec Bridge Hand Representation](https://mlsanity.com/paper/2310.06624/) (2023)
- [A Simple, Solid, and Reproducible Baseline for Bridge Bidding AI](https://mlsanity.com/paper/2406.10306/) (2024)

## Abstract (arXiv)

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.