This is a great result, but you can see that it's more of a theoretical case because of this: "converging to perfect play as available computation time and approximation capacity increases." That is true for pretty much all current deep reinforcement learning algorithms.
The practical question is: How much computation do you need to get useful results? Alpha Go Zero is impressive mathematics, but who is willing to spend $1mio daily for months to train it? IMPALA (another Google one) can learn almost all Atari games, but you need a head node with 256 TPU cores and 1000+ evaluation workers to replicate the timings from the paper.
The practical question is: How much computation do you need to get useful results? Alpha Go Zero is impressive mathematics, but who is willing to spend $1mio daily for months to train it? IMPALA (another Google one) can learn almost all Atari games, but you need a head node with 256 TPU cores and 1000+ evaluation workers to replicate the timings from the paper.