Optimization and Improvement of Bridge Game System
This article presents the optimization and improvement of a bridge game system through reinforcement learning algorithms and the design of a rewards and punishment mechanism. The research focuses on enhancing the efficiency and intelligence of playing bridge cards. Traditional methods are compared with the new system developed for effective computer play, achieving a higher level of game structure design. The study opens up opportunities for future applications, particularly in incomplete information game theory.
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Presentation Transcript
The optimization and improvement of bridge game system Y. Song, H. Qiu, Y. Wang, X. Zheng, 33rd Chinese Control and Decision Conference, 2021 Presenter: Yu-Yen Chu Date: June 27, 2023
Abstract This article from the background of the bridge, improving the system analysis, experimental test three perspective, based on the idea of reinforcement learning, from two aspects of contract and scoring ability, through a large number of calculations and the original program and every time a new system of winning IMP value level to achieve the agreed order, accumulate experience, design a set of good rewards and punishment mechanism, greatly improve the efficiency of the bridge to play CARDS and intelligence, thus the bridge game system was optimized and improved. 2
Abstract (Cont.) In addition, the traditional methods need to manually extract the features of poor expansibility, this paper combined with reinforcement learning algorithm, the ideas of game devised a new system, under the condition of different effective play the computer, the program has also reached a higher level of the game structure design, for the incomplete information game theory provides a reasonable method, application creates opportunities to people living in the future. 3
Problem Setup Neural Network can only deal with accurate data (known information). Reinforcement Learning can set reward and punishment . Original program: Neural Network. Improved program: Deep Reinforcement Learning (Neural Network + Reinforcement Learning). 4
Experimental Result the bidding level of original program and improved program 5
Experimental Result the score of original program and improved program 6
