You may or may not have noticed that I've been working for DeepMind for a while, causing me to get exposed to lots and lots of cutting edge machine learning research. Most of that I can't share here, but there's plenty that's already public.
Firstly, yes, that's what all the fuzz is about. There's a great book by Sutton and Barto, [cached]Reinforcement Learning: An Introduction, with an in-progress version of the second edition available for free from their website!
The book is very good at introducing and explaining RL itself, but does not cover how to combine it with function approximation (neural networks). The basic idea is very simple - just implement the action value function with a neural network - but doing it in practice is trickier.