AlphaGo Zero (https://deepmind.com/blog/alphago-zero-learning-scratch/) has several key components that contribute to it's success:
- A Monte Carlo Tree Search Algorithm that allows it to better search and learn from the state space of Go
- A Deep Neural Network architecture that learns the value and policies of given states, to better inform the MCTS.
My question is, how is this Reinforcement Learning? Or rather, what aspects of this algorithm specifically make it a Reinforcement Learning problem? Couldn't this just be considered a Supervised Learning problem?