On Mon, Feb 27, 2017 at 8:05 AM, Erik van der Werf wrote:
> On Mon, Feb 27, 2017 at 4:30 PM, Darren Cook wrote:
>
>> > But those video games have a very simple optimal policy. Consider Super
>> Mario:
>> > if you see an enemy, step on it; if you see a whole, jump over it; if
>> you see a
>> > pi
On Mon, Feb 27, 2017 at 4:30 PM, Darren Cook wrote:
> > But those video games have a very simple optimal policy. Consider Super
> Mario:
> > if you see an enemy, step on it; if you see a whole, jump over it; if
> you see a
> > pipe sticking up, also jump over it; etc.
>
> A bit like go? If you se
> But those video games have a very simple optimal policy. Consider Super
> Mario:
> if you see an enemy, step on it; if you see a whole, jump over it; if you see
> a
> pipe sticking up, also jump over it; etc.
A bit like go? If you see an unsettled group, make it live. If you have
a ko, play
On Sat, Feb 25, 2017 at 12:30 AM, Brian Sheppard via Computer-go <
computer-go@computer-go.org> wrote:
> In retrospect, I view Schradolph’s paper as evidence that neural networks
> have always been surprisingly successful at Go. Like Brugmann’s paper about
> Monte Carlo, which was underestimated f
-go.org
Subject: Re: [Computer-go] dealing with multiple local optima
TD-gammon is regarded as a special case from the stochastic characteristics of
the backgammon game; it smoothens the search space for the value function and
the value function itself to a great degree compared to those 's
NEAT and hyperNEAT are awesome when "evolving" fairly simple networks with
a very limited number of input and output dimensions. However, without
access to some serious computational power, scaling the NEAT method up to
the kind of level you would need for the current encoding methods for the
input
"seeing" is complex when the input is just a bunch of pixels. Terry McIntyre
Unix/Linux Systems Administration Taking time to do
it right saves having to do it twice.
On Friday, February 24, 2017 12:32 PM, Minjae Kim wrote:
But those video games have a very simple optimal policy. Con
I should point out that Reinforcement Learning is a relatively unimportant
part of AlphaGo, according to the paper. They only used it to turn the
move-prediction network into a stronger player (presumably increasing the
weights of the layer before SoftMax would do most of the job, by making the
hig
But those video games have a very simple optimal policy. Consider Super
Mario: if you see an enemy, step on it; if you see a whole, jump over it;
if you see a pipe sticking up, also jump over it; etc.
On Sat, Feb 25, 2017 at 12:36 AM, Darren Cook wrote:
> > ...if it is hard to have "the good sta
Tesauro backgammon” and you should be able to
> find a paper.
>
>
>
> I don’t know NEAT and HyperNEAT; I will look them up. Thank you for the
> reference.
>
>
>
> Best,
>
> Brian
>
>
>
> *From:* Computer-go [mailto:computer-go-boun...@computer-go.org] *On
>
> ...if it is hard to have "the good starting point" such as a trained
> policy from human expert game records, what is a way to devise one.
My first thought was to look at the DeepMind research on learning to
play video games (which I think either pre-dates the AlphaGo research,
or was done in pa
know NEAT and HyperNEAT; I will look them up. Thank you for the
reference.
Best,
Brian
From: Computer-go [mailto:computer-go-boun...@computer-go.org] On Behalf Of
Minjae Kim
Sent: Friday, February 24, 2017 3:39 AM
To: computer-go@computer-go.org
Subject: [Computer-go] dealing with multiple
I've recently viewed the paper of AlphaGo, which has done gradient-based
reinforcement learning to get stronger. The learning was successful enough
to beat a human master, but in this case, supervised learning with a large
database of master level human games was preceded the reinforcement
learning
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