[Computer-go] Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
darren at dcook.org
Wed Dec 6 16:54:45 PST 2017
>> One of the changes they made (bottom of p.3) was to continuously
>> update the neural net, rather than require a new network to beat
>> it 55% of the time to be used. (That struck me as strange at the
>> time, when reading the AlphaGoZero paper - why not just >50%?)
> I read that as a simple way of establishing confidence that the
> result was statistically significant > 0. (+35 Elo over 400 games...
Brian Sheppard also:
> Requiring a margin > 55% is a defense against a random result. A 55%
> score in a 400-game match is 2 sigma.
Good point. That makes sense.
But (where A is best so far, and B is the newer network) in
A vs. B, if B wins 50.1%, there is a slightly greater than 50-50 chance
that B is better than A. In the extreme case of 54.9% win rate there is
something like a 94%-6% chance (?) that B is better, but they still
throw B away.
If B just got lucky, and A was better, well the next generation is just
more likely to de-throne B, so long-term you won't lose much.
On the other hand, at very strong levels, this might prevent
improvement, as a jump to 55% win rate in just one generation sounds
unlikely to happen. (Did I understand that right? As B is thrown away,
and A continues to be used, there is only that one generation within
which to improve on it, each time?)
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