Recently, Katago has reached similar levels of strength using a small fraction of the resources: https://arxiv.org/abs/1902.10565
It depends on what you mean by "more efficient." The significance of AlphaZero was that you can reach good results in a variety of domains even without human expert knowledge to provide supervised learning data or engineer features. It's efficient in terms of engineering resources.
A precisely tailored approach can always get better results.
I have the same question. Not sure I have an answer yet, but this paper includes some pseudocode that implements the algorithm: https://arxiv.org/src/1911.08265v1/anc/pseudocode.py
I'm planning on trying to train something simple like TicTacToe to both see if it works and understand how it works.
If I'm interested in building a toy version following the Deepmind spec, which can be trained to reach super-human capabilities on a particular board game (Reversi, Chess, checkers, possibly even Go given enough compute), which of these "versions" of the project would be the easiest for me to understand/implement? (assume I have a basic understanding of the high-level concepts and lots of enthusiasm, but I'm not an expert).
My understanding is, AlphaZero is not just stronger than AlphaGo, but architecturally simpler and more efficient. That's what I'm looking for -- the implementation with the highest result/difficulty ratio.