Gobang Game Algorithm Based on Reinforcement Learning

Li, Xiali; Zhang, Wei; Chen, Junren; Wu, Licheng; Cairangdanghzhou,

doi:10.1007/978-981-16-9247-5_36

Cited by 2 publications

(1 citation statement)

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“…Furthermore, in order to enhance the effectiveness of the evaluation function, deep learning algorithm was incorporated into the strategy creation and situation evaluation processes of the game decision-making mechanisms in [8]. A composite visual field network based on deep reinforcement learning that improves the network's thorough perception of both local and global chess shapes is proposed in the study [9].…”

Section: Related Workmentioning

confidence: 99%

An Efficient Approach for the Implementation of the Gobang Game Using Artificial Intelligence Methods

Dospanova

Kusdavletov

Kalikova

2023

sjaitu

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Gobang is one of the most ancient abstract strategy games for two players. The game is traditionally played on a board with black and white stones, where players take turns placing a colored stone on an empty intersection. The winner is the first player to form an unbroken chain of five stones, either horizontally, vertically, or diagonally. Although the rules of Gobang seem pretty straightforward, the game tree complexity is enormous since the board state is more intuitive than in other games. In this paper, we will implement an algorithm that will solve the Gobang game using artificial intelligence (AI) methods. The program will begin learning from scratch, then use self-play to produce training data, and eventually steadily build up its strength. The present work first focuses on the implementation of the supervised learning algorithm in the identification procedure in order to identify the position of the current fallen piece. This will be achieved by utilizing image processing and a convolutional neural network. Then a Gobang game procedure will be implemented using a game search algorithm, in which the state of the game is judged by means of a human-set function. After that, the function of judging the game state in the above game search algorithm will be changed to an artificial neural network (ANN) model, since it is convenient to train a model with a small dataset. Finally, the reinforcement learning algorithm will be applied to learn the artificial neural network model so that the playing level of the Gobang game program can be continuously improved.

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Section: Related Workmentioning

confidence: 99%