outputs from mgpr.predict_on_noisy_inputs and pilco.propagate confusion

[mcyip]

Hi,

Thanks for some great code. I'm trying to read through it and make descriptive comments since theres a lot of dense code and several layers of wrappers of abstractions to TF :)

My question is to try to nail down the behavior of mgpr.predict_on_noisy_inputs (namely mgpr.predict_given_factorizations)

If I reference PILCO.py's propagate function i see the return is a delta value. (since the immediate next line to predict_on_noisy_inputs is
M_x = M_dx + m_x

However, its not clear to me where this delta is performed or predicted. From the description of mgpr.predict_given_factorizations, it says to return a mean and variance of X, and I also don't really see the line where it might only returning a "change" in X. It makes sense to me that only X is returned, since both the system model and the controller seem to be of the form x(t+1)=f(x(t),u(t)) and u(t)=g(x(t)) respectively.

Thanks!

[nrontsis]

Hi,

Thanks for the interest. If you spend time on writing comments, we would be very happy if you were to make a PR so as to incorporate (some of) them in the main repo. We plan on adding more documentation & examples soon, and while writing them it would be helpful to get feedback from other people.

The delta comes because the datasets X, Y you provide to PILCO should be of the form:

X[i] = [state at time i;
       control action at time i]
Y[i] = (state at time i + 1) - (state at time i).

So the underlying GPs are trained to provide delta as their output.

See here how we create X and Y on the example.

[mcyip]

Thanks! This is a silly mistake on my part. Thanks for the clarification. Will do a PR.