Enshinstein chess game algorithm based on reinforcement learning

Abstract

The invention discloses an Enshinstein chess game algorithm based on reinforcement learning. BP neural network is applied to a value evaluation method of a chessboard and an action selection strategyof a Monte Carlo tree search algorithm. The characteristics of the chessboard are learned and the network parameters are gradually adjusted by means of the self-chess-playing learning rule of the reinforcement learning method, so that the value evaluation of the BP neural network on the chessboard and the strategy calculation of the chess playing action are gradually accurate, and the performanceof the whole game algorithm is gradually improved. According to the invention, the two BP neural networks are respectively used as a value estimation function and a behavior strategy function of the Einstein chess. The reinforcement learning algorithm serves as an evolution mechanism for adjusting BP neural network parameters, the defect that the level of an existing Enshinstein chess training setis limited by the human level is overcome, and the upper limit of the game level of the Enshinstein chess is improved.

Description

technical field [0001] The invention belongs to the research field of chess game machine games, in particular to an Einstein chess game algorithm based on reinforcement learning. Background technique [0002] Artificial Neural Network (ANN) is a research hotspot in the field of artificial intelligence since the 1980s. A neural network is an operational model consisting of a large number of nodes (or called neurons) that interact with each other. Each node represents a special processing function called an activation function. Each connection between two nodes represents a weighted value for the signal passing through the connection, called weight, which is equivalent to the learning experience of the artificial neural network. The output of the network varies according to the way the network is connected, the weight value and the activation function. The network itself is usually an approximation to a certain algorithm or function in nature, or it may be an expression of ...

AI Technical Summary

Problems solved by technology

[0015] In view of the deficiencies in the prior art, the technical problem to be solved by the present invention is to provide a kind of Einstein chess game algorithm based on reinforcement learning, which solves the defect that the level of the current Einstein chess training set is limited by the human level , increasing the upper limit of Einstein chess game level

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