
Multiplayer AlphaZero - hardmaru
http://arxiv.org/abs/1910.13012
======
Mageek
Nice and concise paper. All that changed was outputting a vector of predicted
game values, as we go from having a zero-sum game with two players (which can
be summarized with a single value) to a game with n players. Makes sense.

The comparison against MCTS shows strong performance from AlphaZero. Would be
curious to see the performance of AlphaZero vs. number of its own rollouts -
ie is the probability head output alone already encoding enough information to
play well, and how deep does it have to look ahead to determine good play.
Finally, for tic-tac-mo and connect 3x3, it should be possible to determine
the optimal move. How much training / lookahead is required to achieve that?
Does AlphaZero achieve perfect play for these games?

The paper's first listed contribution is "an independent reimplementation of
DeepMind's AlphaZero algorithm". Maybe I missed it, but I don't see a link to
a repo with the implementation.

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gok
Interesting results but they really only use toy examples (Tic-Tac-Toe and
Connect Four). It would be kind of surprising if it didn't work.

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jstrong
> e.g. equity trading

~~~
narrator
As much as I liked to get excited bout these kinds of papers:

"Recomputing the AlphaGo Zero weights will take about 1700 years on commodity
hardware."

[1] [https://github.com/leela-zero/leela-zero](https://github.com/leela-
zero/leela-zero)

~~~
fnbr
Yeah, but that's an embarassingly parallel task, so you can recreate it with
sufficient resources. If there's a sufficiently lucrative application,
businesses will happily pay that.

~~~
dmurray
And it's already been done for the chess version, in a community effort[0].
Well, "recomputing the weights" isn't plausible for a neural network of this
size, but computing weights that give the network a similar level of
performance.

[0] [https://lczero.org/](https://lczero.org/)

~~~
narrator
One thing I think that will be critical to the future of software is community
efforts to build deep neural nets that match the AI power of the big
companies.

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mrleiter
Anyone who's interested to see how brilliant AlphaZero played against the best
chess computer (then), with solid explanations:
[https://www.youtube.com/watch?v=lFXJWPhDsSY](https://www.youtube.com/watch?v=lFXJWPhDsSY)

~~~
dmurray
I'd recommend instead the videos made by Matthew Sadler for chess24[0]. Unlike
the guy in your videos, Sadler is a strong chess player and literally wrote
the book [1] on Alphazero. The videos are approachable for all levels of chess
ability.

[0]
[https://www.youtube.com/playlist?list=PLAwlxGCJB4NchyTBYik8F...](https://www.youtube.com/playlist?list=PLAwlxGCJB4NchyTBYik8FBbnzLXpCCO79)
[1] [https://www.newinchess.com/game-changer](https://www.newinchess.com/game-
changer)

[https://www.youtube.com/playlist?list=PLAwlxGCJB4NchyTBYik8F...](https://www.youtube.com/playlist?list=PLAwlxGCJB4NchyTBYik8FBbnzLXpCCO79)

