Alpha Go Zero: How and Why It Works
The main reason AlphaGo Zero learns so much faster than its predecessors is because it uses temporal-difference learning.[1] This effectively removes a <i>huge</i> amount of the value network's state space for the learning algorithm to search through, since it bakes in the assumption that a move's value ought to equal that of the best available move in the following board position, which is exactly what you'd expect for a game like Go.<p>A secondary reason for AlphaGo Zero's performance is that it combines both value and policy networks into a single network, since it's redundant to have two networks for move selection.<p>These are the two biggest distinguishing characteristics of AlphaGo Zero compared to previous AlphaGos, and the OP doesn't discuss either of them.<p>[1] <a href="https://en.wikipedia.org/wiki/Temporal_difference_learning" rel="nofollow">https://en.wikipedia.org/wiki/Temporal_difference_learning</a>