Hi, I am new to Pytorch and I am trying to get its basics. The first project I will need it for is one involving a function approximation. My problem is the following: I have a robot that can radically change its morphology and the respective simple PID controller with the (P, I, D) gains.
Therefore, I would like to use a Neural Network (NN) that is able to approximate a function that goes from (size, inertia, … ) to (P, I, D).
I will create the dataset on which to train all in simulation, for now, therefore I can generate quite a lot of data.
I still do not have a clean dataset which has really the correspondence (size, inertia, … ) —> (P, I, D) but I am working on it.
I know that this problem might be a better fit for a reinforcement learning implementation or even just a simple regression. However, to get my hands dirty with Pytorch, I thought that it is a nice problem to start with (and I will need it for my research).
So far I followed the main Pytorch tutorials (here 2) and this one too. I would like to do some more oriented to the problem described. Could you please suggest me a path to follow?
Thanks in advance!