[Computer-go] Standalone DNN player support

Josef Moudrik j.moudrik at gmail.com
Wed Jun 3 05:49:13 PDT 2015


Hey,

so, after some struggling, here is an incarnation of my ideas on the
matter, a work in progress.
The toolkit has currently two main functions:
1) general GTP wrapper for turning move distribution generating bot into a
full-featured player. It can pass by using GnuGo as an pass-oracle.
Currently supporting Hugh Perkins's DeepCL convolutional network.
2) dataset generation - extract feature planes (ie. Clark & Storkey 2014)
and labels from a list of games and save them in a HDF5 file (customizable
a lot). After some research I concluded that the HDF5 is a very good format
for this purpose (large amounts of binary data) as it is a somewhat
standard format with transparent compression, featuring support in toolkits
like pylearn2. I think this is a better choice than home-brew binary
formats.

Code can be found here:

https://github.com/jmoudrik/deep-go-wrap

Regards,
Josef

On Thu, Apr 30, 2015 at 12:25 PM Josef Moudrik <j.moudrik at gmail.com> wrote:

> > I would love to have something like this.
> Great!
>
> > I would appreciate some way to configure depth levels and
> > variable branching factors for move generation as well as scoring
> > playouts using the NN.
>
> Hmm, I am not talking about MCTS integration or DNN training. Rather, I
> have a small wrapper in mind, that will make the DNN possible to be used as
> a standalone player - something like a convenient unified toolkit to which
> you would plug in your dnn model of choice (used as a black box). The
> toolkit would handle i/o (i.e. transform the board to input planes), gtp
> communication, ensured move correctness and e.g. enabled passing.
>
> Regards,
> Josef
>
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