# BridgeHand2Vec Bridge Hand Representation

arXiv: https://arxiv.org/abs/2310.06624 · HTML: https://mlsanity.com/paper/2310.06624/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 29 references that are in the map.
- First posted on arXiv 10 Oct 2023.

## Builds on (most cited first, 8 of 8)

- [Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm](https://mlsanity.com/paper/1712.01815/) (2017)
- [DeepStack: Expert-Level Artificial Intelligence in No-Limit Poker](https://mlsanity.com/paper/1701.01724/) (2017)
- [Joint Policy Search for Multi-agent Collaboration with Imperfect Information](https://mlsanity.com/paper/2008.06495/) (2020)
- [Learning to Communicate Implicitly By Actions](https://mlsanity.com/paper/1810.04444/) (2018)
- [Human-Agent Cooperation in Bridge Bidding](https://mlsanity.com/paper/2011.14124/) (2020)
- [Automatic Bridge Bidding Using Deep Reinforcement Learning](https://mlsanity.com/paper/1607.03290/) (2016)
- [Competitive Bridge Bidding with Deep Neural Networks](https://mlsanity.com/paper/1903.00900/) (2019)
- [Bidding in Spades](https://mlsanity.com/paper/1912.11323/) (2019)

## Abstract (arXiv)

Contract bridge is a game characterized by incomplete information, posing an exciting challenge for artificial intelligence methods. This paper proposes the BridgeHand2Vec approach, which leverages a neural network to embed a bridge player's hand (consisting of 13 cards) into a vector space. The resulting representation reflects the strength of the hand in the game and enables interpretable distances to be determined between different hands. This representation is derived by training a neural network to estimate the number of tricks that a pair of players can take. In the remainder of this paper, we analyze the properties of the resulting vector space and provide examples of its application in reinforcement learning, and opening bid classification. Although this was not our main goal, the neural network used for the vectorization achieves SOTA results on the DDBP2 problem (estimating the number of tricks for two given hands).