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    • That's where we bump against the limitations of this approach. Soccer (or any other real-world games) has immeasurably more play states than chess, go, or other board games with discrete positions (how many positions are there for a soccer player?). Not only that, but the players' ability is wildly different (and changes over time!). Modelling such games in a fashion that would be usable for AlphaZero would be extremely hard. Besides, you would even have trouble with the first phase of the process: it would be really hard to find two teams willing to play a couple of billion of games with each player doing totally random moves. :-)

    • sounds like a perfectly reasonable human response (I was thinking the exact same thing)...

      maybe Alpha Zero would say no problem let's just rule out 90% of the actions that we know won't result in a win like putting 10 players on one side of the field etc. It probably would start with the standard framework coaches start with and "learn".

      Could Alpha Zero "watch" games by analyzing the movements of players from previously recorded games over the past 10 years? I'm sure they can turn the players movements into some kind of data that might look a bit like chess except all the players are the queen that can move in any direction?

    • Actually, that's an interesting idea. Place GPS devices on the players and the ball with some player metrics like weight and height into a data stream and feed that data to AlphaSoccer. In the past that would have been called statistics and people like Billy Beane would have become famous for it in MoneyBall.

      Baseball has always leant itself to statistics, but if you had player and ball position data in soccer plus AI, whoa.

    • Yes, it can analyse the recording of existing games, but that would only get us up to AlphaGo, which learned Go by analysing human played games. The real breakthroughs come from forgoing the human experience and practice in particular game and starting from tabula rasa.