A machine learning model that will learn and play flappy bird game. The model is developed using a combination of Neural networks and Genetic algorithm, combinely known as NEAT algorithm (NeuroEvolution of Augmenting Topologies).
from (wikipedia): NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks (a neuroevolution technique) developed by Ken Stanley in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation (the evolution of species) to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.
- Clone the Repository
git clone https://github.com/akhil-code/flappy-bird-neuro-evolution - Change current directory to this repository
- Run the application using command
python app.py - Upon executing the above command, a population of birds will start learning to play the game.
- Will try implementing the same algorithm on different games like Snake, 2048 etc. Stay tuned
- Will use deep learning inorder to take image as input instead of game features.
- Akhil Guttula