Hello Oliver,
2015-03-16 11:58 GMT+00:00 Oliver Lewis :
>
> It's impressive that the same network learned to play seven games with
> just a win/lose signal. It's also interesting that both these teams are in
> different parts of Google. I assume they are aware of each other's work,
> but maybe Aj
I am also interested in unsupervised learning. When this first came up, I
found this paper: http://arxiv.org/pdf/1312.5602v1.pdf, where the authors
taught a deep convolutional network to play games from the Atari console.
They used what they call "Deep Reinforcement Learning", a variant of
Q-learni
> The important thing is that the games don't have to be played perfectly: They
>just need to be significantly better than your current model, so you can tweak
>the model to learn from them.
Thats an important incite. I hadnt thought of that.
Maybe could combine with some concept of "forgettin
The human brain is not the most powerful AI, because it fails the "A" test.
I suspect bootstrapping is not very hard. I have recently written a Spanish
checkers program starting with no knowledge and I got it to play top-human
level checkers within a few weeks.
You can build a database of games a
> To be honest, what I really want is for it to self-learn,...
I wonder if even the world's most powerful AI (i.e. the human brain)
could self-learn go to, say, strong dan level? I.e. Give a boy genius a
go board, the rules, and two years, but don't give him any books, hints,
or the chance to play
I was thinking about bootstrapping possibilities, and wondered whether
it would be possible to use a shallower mimic net for positional
evaluation playouts from a specific depth on after having generated
positions with a certain branching factor that typically allows the
actual pro move to be inclu
