Thank you very much indeed for the explanation and the paper reference.
I just read through the Schulman’s paper. I am however unclear about certain aspects of your answers:
(i) why did you mention that I have to use the negative of the loss function as an argument when I am calling rloss.backward()?
(ii) What are best practices for choosing reward scalars if I decide against using the negative of the loss for estimating the gradients?
Would appreciate your response.
Thanks!