Flappy Bird AI is developed using the NEAT (NeuroEvolution of Augmenting Topologies) algorithm, miming natural selection to train neural networks. This approach allows the AI to improve its gameplay over multiple generations, learning to navigate obstacles with increasing efficiency. NEAT evolves neural networks by selectively breeding the best-performing individuals and introducing mutations, leading to an AI that progressively gets better at playing Flappy Bird.
The game is implemented using Pygame, a popular Python library for game development.
Pygame provides essential functionalities like rendering graphics, handling user inputs, and managing game physics, making it a suitable choice for creating interactive applications. By combining NEAT with Pygame, we can visualize the AI's learning process in real-time, as the birds attempt to navigate through pipes and improve their survival rate over generations.
By leveraging NEAT and Pygame, this project demonstrates how evolutionary algorithms can be applied to game AI, enabling continuous improvement through learning and adaptation.
Check out the full project on Github : https://github.com/ArcheeSinha/AI-plays-Flappy-Bird
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