NEAT Wings: Algorithmic Strategy for Intelligent Flappy Bird Navigation

This research explores the application of the NeuroEvolution of Augmenting Topologies (NEAT) algorithm in training an artificial intelligence (AI) model to play the popular mobile game Flappy Bird. The primary objective is to investigate the effectiveness of NEAT in enabling an AI agent to learn and adapt to the dynamic challenges presented by the game environment. The Flappy Bird game serves as an ideal testing ground for evaluating the adaptability and learning capabilities of the NEAT algorithm.The methodology involves implementing the NEAT algorithm within the game framework, allowing the AI to learn from scratch without prior knowledge of the game dynamics. Various parameters and configurations are fine-tuned to optimize the learning process. The results indicate the AI’s ability to navigate through the game obstacles and improve its performance over time.In addition to detailing the NEAT algorithm and its application in the Flappy Bird context, this paper provides insights into the learning process, challenges encountered during implementation, and a discussion on the obtained results. Comparative analyses with other AI approaches highlight the unique advantages and contributions of the NEAT algorithm in the domain of gaming AI.The findings contribute to the growing body of research on AI in gaming and showcase the potential of evolutionary algorithms, specifically NEAT, in training agents for complex and dynamic environments. The implications of this research extend beyond the realm of gaming, offering valuable insights into the adaptability of evolutionary algorithms in real-world scenarios.