RMSProp in TF VS Pytorch
In Pytorch's RMSProp implementation we are given the parameter alpha which according to the documentation:
alpha (float, optional) – smoothing constant (default: 0.99)
On the other hand, TF's implementation has the parameter rho (Formally named decay):
rho    Discounting factor for the history/coming gradient. Defaults to 0.9.
Are those parameters the same with different names or are they different?
I couldn't find any information regarding the differences.

If you compare the source code of PyTorch (here) and that of Tensorflow (on a forked build), you will see that alpha and rho are indeed the same.
Although, as opposed to Tensorflow, PyTorch is clear about the underlying logic for its module:

