In this chapter, we learned how to implement control strategies for controllers that can maintain a stable state of a cart-pole apparatus with one or two poles mounted on top. We improved our Python skills and expanded our knowledge of the NEAT-Python library by implementing accurate simulations of physical apparatuses, which was used to define the objective functions for the experiments. Besides this, we learned about two methods for numerical approximations of differential equations, Euler's and Runge-Kutta, and implemented them in Python.
We found that the initial conditions that determine the neuroevolutionary process, such as a random seed number, have a significant impact on the performance of the algorithm. These values determine the entire sequence of numbers that will be generated by a random number generator. They serve as a random attractor that can amplify...
