Learning to bid in bridge
Asaf Amit,Shaul Markovitch +1 more
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A new decision-making algorithm that allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information is presented.Abstract:
Bridge bidding is considered to be one of the most difficult problems for game-playing programs. It involves four agents rather than two, including a cooperative agent. In addition, the partial observability of the game makes it impossible to predict the outcome of each action. In this paper we present a new decision-making algorithm that is capable of overcoming these problems. The algorithm allows models to be used for both opponent agents and partners, while utilizing a novel model-based Monte Carlo sampling method to overcome the problem of hidden information. The paper also presents a learning framework that uses the above decision-making algorithm for co-training of partners. The agents refine their selection strategies during training and continuously exchange their refined strategies. The refinement is based on inductive learning applied to examples accumulated for classes of states with conflicting actions. The algorithm was empirically evaluated on a set of bridge deals. The pair of agents that co-trained significantly improved their bidding performance to a level surpassing that of the current state-of-the-art bidding algorithm.read more
Citations
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Book ChapterDOI
Reinforcement Learning in Games
TL;DR: The basic reinforcement learning algorithms are rarely sufficient for high-level gameplay, so it is essential to discuss the additional ideas, ways of inserting domain knowledge, implementation decisions that are necessary for scaling up.
Recent Advances in Machine Learning and Game Playing
TL;DR: The most important achievements of this field are highlighted and some important recent advance in game-playing applications are summarized.
Proceedings ArticleDOI
Bridge Bidding with Imperfect Information
Lori L. DeLooze,J. Downey +1 more
TL;DR: It is shown that a special form of a neural network, called a self-organizing map (SOM), can be used to effectively bid no trump hands and is an ideal mechanism for modeling the imprecise and ambiguous nature of the game.
Posted Content
Automatic Bridge Bidding Using Deep Reinforcement Learning
Chih-Kuan Yeh,Hsuan-Tien Lin +1 more
TL;DR: In this paper, a deep reinforcement learning model was proposed to learn to bid automatically based on the raw card data for bridge zero-sum games without the aid of human domain knowledge.
Proceedings Article
Contract Bridge Bidding by Learning.
Chun-Yen Ho,Hsuan-Tien Lin +1 more
TL;DR: A novel learning framework to let a computer program learn its own bidding decisions is proposed and it is found that it performs competitively to the champion computer bridge program that mimics human bidding decisions.
References
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