The propose of this paper is to develop and study an artificial intelligence-based optimization algorithm for the famous “Flappy Bird”, which benefits to a better game score. We first create an agent that learns how to optimally play the game by safely dodging all the barriers and flapping its wings through them. In order to expand the feasible solution space and generalization ability, a multi-gene genetic algorithm based on the Genetic Algorithm and Neural Network is proposed. We carry out detailed experiments to prove the proposed multi-gene genetic algorithm to be useful and efficient. The quantitative results show that the multi-gene genetic can significantly improve the game score.
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A multi-gene genetic algorithm for the Flappy Bird game based on neural network
Semantic Scholar · Computer Science · 2021
Abstract
The propose of this paper is to develop and study an artificial intelligence-based optimization algorithm for the famous “Flappy Bird”, which benefits to a better game score. We first create an agent that learns how to optimally play the game by safely dodging all the barriers and flapping its wings through them. In order to expand the feasible solution space and generalization ability, a multi-gene genetic algorithm based on the Genetic Algorithm and Neural Network is proposed. We carry out detailed experiments to prove the proposed multi-gene genetic algorithm to be useful and efficient. The quantitative results show that the multi-gene genetic can significantly improve the game score.