> One thing that is not explained is how to determine that a game is over

You'll find that very little of the literature explicitly covers this. When
I asked this question I had to search a lot of papers on MCTS which
mentioned "terminal states" before finding one which defined them.

Let me see if I can find the actual paper, but they defined it as a
position where there are no more legal moves. You're right though, that
ignores sekis, which makes me think I'm remembering wrong.
On Mon, Feb 1, 2016, 13:45 Álvaro Begué <alvaro.be...@gmail.com> wrote:

>
> Aja,
>
> I read the paper with great interest. [Insert appropriate praises here.]
>
> I am trying to understand the part where you use reinforcement learning to
> improve upon the CNN trained by imitating humans. One thing that is not
> explained is how to determine that a game is over, particularly when a
> player is simply a CNN that has a probability distribution as its output.
> Do you play until every point is either a suicide or looks like an eye? Do
> you do anything to make sure you don't play in a seki?
>
> I am sure you are a busy man these days, so please answer only when you
> have time.
>
> Thanks!
> Álvaro.
>
>
>
> On Wed, Jan 27, 2016 at 1:46 PM, Aja Huang <ajahu...@google.com> wrote:
>
>> Hi all,
>>
>> We are very excited to announce that our Go program, AlphaGo, has beaten
>> a professional player for the first time. AlphaGo beat the European
>> champion Fan Hui by 5 games to 0. We hope you enjoy our paper, published in
>> Nature today. The paper and all the games can be found here:
>>
>> http://www.deepmind.com/alpha-go.html
>>
>> AlphaGo will be competing in a match against Lee Sedol in Seoul, this
>> March, to see whether we finally have a Go program that is stronger than
>> any human!
>>
>> Aja
>>
>> PS I am very busy preparing AlphaGo for the match, so apologies in
>> advance if I cannot respond to all questions about AlphaGo.
>>
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>>
>
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