[Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
jim.oflaherty.jr at gmail.com
Thu Dec 7 09:09:28 PST 2017
In 2002/Nov, I created a Go adaptation, Abchij, which I think might not be
easily conquered by these algorithms. It's funny, I did so in anticipation
of thwarting any sort of brute force algorithms that might emerge to
"solve" Go as I hated how those were the solution to Chess. If you are
interested, I would be happy to post the rules for the game.
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On Wed, Dec 6, 2017 at 8:28 AM, "Ingo Althöfer" <3-Hirn-Verlag at gmx.de>
> It seems, we are living in extremely
> heavy times ...
> I want to go to bed now and meditate for threee days.
> > DeepMind makes strongest Chess and Shogi programs with AlphaGo Zero
> > Mastering Chess and Shogi by Self-Play with a General Reinforcement
> Learning Algorithm
> > https://arxiv.org/pdf/1712.01815.pdf
> > AlphaZero(Chess) outperformed Stockfish after 4 hours,
> > AlphaZero(Shogi) outperformed elmo after 2 hours.
> It may sound strange, but at the moment my only hopes for
> games too difficult for AlphaZero might be
> * a connection game like Hex (on 19x19 board)
> * a game like Clobber (based on CGT)
> Mastering Clobber would mean that also the concept of
> combinatorial game theory would be "easily" learnable.
> Side question: Would the classic Nim game be
> a trivial nut for AlphaZero ?
> Ingo (is now starting to hope for an AlphaZero type program
> that can do "general" mathematical research).
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> Computer-go at computer-go.org
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