This project is an implementation of the NEAT algorithm by Kenneth O. Stanley, with some additional, bio-inspired features. All of the code is written in Jupyter notebook files following an object oriented design.
In short, this implementation attempts to discover topologies via a genetic algorithm, trains using tensorflow libraries and optimizes hyper-parameters via reinforcement learning. The whole project is structured adhering to Object Oriented Programming guidelines for easy readability and hopes for being accessible.
For any questions please email to donbekci@stanford.edu
For a more detailed introduction, refer to Introduction.ipynb.