Info about robot arm kinematics progress 
: Thereafter a short gif giving an idea of the robot during it’s training (**see explaination at bottom).
Random data of joints angles are sent as an action to the robot arm at the beginning of the training, then as the training progresses, values from the NN replace those random values. Thus during the training, we see the robot arm more and more often finding the right position of the arm in order to reach the given target.
** (In a Forward kinematics problem, the location of the final end cap of the robot arm (Effector) in space, i.e. the position and orientation of the entire arm, is determined according to the joints angles. If you consider a position and orientation of the Effector that reach a Goal, the Inverse kinematics problem is to find the values of the joints that allow the arm to reach this given location. There are various ways to determine the solution of the Inverse kinematics problem and in Armory you have the Forward and IK Node at your disposal, but I invite you to test and verify that it’s not perfect. The NNs are capable of representing those non-linear relationships between input and output data. It’s already demonstrated with Tanks Armory/Atrap, and here the NN is learning through a try/reward mechanism that provides the link between cartesian and joints space required by the Inverse kinematics problem.