Giving a Genetic Algorithm Control in a Physics Engine

The most common/commercial application of AI is to gain insights or make predictions on a dataset. Even though I think that this is interesting, these techniques have been widely covered by others. This article covers a more experimental application of machine learning: this is allowing machine learning to manipulate a physics simulation. There is a lot of different possibilities of putting an AI into a physics environment, but all of them involve manipulating and controlling objects and forces in the environment. To get good results from this project, we need to add limitations to the AI's control in the environment.