  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).
