Where do these convergence conditions come from?

Question

I have been reading a book on Reinforcement Learning and the author mentions "a well-known result in stochastic approximation theory" that gives us the conditions required to assure convergence in probability 1. Screenshot of these conditions below:

What is this well-known result? I've been trying to google around for it but am having trouble finding it.

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Edit for additional clarity

Here the $\alpha_n$ are step size parameters for processing rewards. You can see them being defined on p.25 (as it appears on the book page not page count in a PDF reader) of the book, with the below screenshot taken from p.26.

Answer 1

In a lecture from David Silver, he mentions that these are the conditions for the Robbins-Monro algorithm to converge, which is what I was looking for.