Hi jomavera, thanks so much for the example. However, i have a follow-up question.

will this affect the discrete parameters also? because in the original paper if I’m not mistaken, the bounded action space is only for the continuous parameter.
What if each parameter has different bound or limits? how do we track it?
I think both of these questions are greatly influenced by my limited knowledge of how gradient storing and backpropagation works in pytorch.

However, I know that we can treat all action as bounded by -1 to 1, even the discrete, and for the parameters we can then map them to their respective limit. But will this approach be valid?
Thank you so much beforehand