Nothing in the components they used is novel. All approaches have been explored. Checking their references you will notice many researchers doing similar work. The novelty was the pipeline they followed and the combination of model-free and model-based Reinforcement Learning approaches. I will try to give you a non technical different perspective on what they captured.
Model-free approaches usually attempt to approximate functions such as Value functions (representing how good it is to be in a particular state - board configuration - in terms of future reward) or parametrized policy functions (probabilities of selecting an action given a state. Briefly, your model gains some kind of 'intuition' on which moves are relative good - something similar to the intuition professional Go players have, when they declare that they do a move because it 'feels' good. This is very important at the early stage of the game when planning is inefficient to use.
Model-based approaches attempt to simulate every single possible trajectory of the game in the form of a decision tree. Thus they are useful for planning (before you actually make a move in the game you check and evaluate all possible contingencies and then you decide which move to take from your current position). The MCTS is such an algorithm, creates a decision tree over possible future courses of the game from the current board position, and evaluates these heuristics according to some criteria. The best algorithms in Go so far were based in this algorithm (and is considered as a RL algorithm).
So in terms of novelty, with few words: combination of planning and intuition, which means combination of MCTS algorithm with function approximators for evaluation of the simulated game trajectories. In this case they used very deep convolutional neural nets for the 'intuition' part. In addition to this, the whole model is data-driven as it was first trained on human expert moves (this could be useful in applications in many other domains apart from gaming). If you examine every single component, there is nothing novel...but the whole process to combine effectively all these elements and achieve Mastery in that complex domain is something novel. Hope it helps!