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To be fair, in the article itself we wind up criticizing "Pure RL" (defined as the basic formulation that is typically followed, in which all learning is done from just the reward signal) and not RL as a whole. We call out a lot of awesome non-pure RL work in the second part and suggest this deserves more attention and excitement over eg AlphaGo.

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Fair enough. Your article makes a lot of good points, for sure.

Here is a quote from the article I want to mention: “Trying to learn the board game 'from scratch' without explanation was absurd, right?”

No. It is hardly absurd. Sometimes it works, sometimes not. It is a great starting point, if nothing else. So, I wonder if we have different ideas of what ‘absurd’ means.

I agree that we’re in a period of hype. It requires careful work to write clearly without too much zeal or oversimplification. My opinion here is that your attempt to ‘balance’ the debate uses a lot of language that I (and others) perceive as exaggerated.

Has any of the approaches recommended in part 2 been shown to give equivalent or better results than AlphaGo Zero? Eg matching/surpassing human performance with shorter learning time or less compute resources?

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