[Computer-go] 9x9 is last frontier?

Dan dshawul at gmail.com
Fri Mar 2 11:28:16 PST 2018


Leela chess is here https://github.com/glinscott/leela-chess

It uses the exact MCTS algorithm as described in AlphaZero, with value and
policy networks, but performs really badly in tactics (often missing 2-3
ply shallow tactics)

To get a somewhat strong MCTS chess engine, I had to use alpha-beta
rollouts instead, as described in
https://www.microsoft.com/en-us/research/wp-content/uploads/2014/11/huang_rollout.pdf

I think this could be suitable for 9x9 Go too.

It has been a mystery for me how AlphaZero avoided into falling for shallow
traps in chess.

Daniel

On Fri, Mar 2, 2018 at 10:22 AM, Xavier Combelle <xavier.combelle at gmail.com>
wrote:

> Where is leela chess. How many games it is trained on?
>
> Le 2 mars 2018 18:20, "Dan" <dshawul at gmail.com> a écrit :
>
>> Hello Aja,
>>
>> Could you enlighten me on how AlphaZero handles tactics in chess ?
>>
>> It seems the mcts approach as described in the paper does not perform
>> well enough.
>>
>> Leela-chess is not performing well enough even though leela-go seems to
>> be doing well.
>>
>> Daniel
>>
>>
>>
>>
>>
>> On Fri, Mar 2, 2018 at 4:52 AM, Aja Huang <ajahuang at gmail.com> wrote:
>>
>>>
>>>
>>> 2018-03-02 6:50 GMT+00:00 "Ingo Althöfer" <3-Hirn-Verlag at gmx.de>:
>>>
>>>> Von: "David Doshay" <ddoshay at mac.com>
>>>> > Go is hard.
>>>> > Programming is hard.
>>>> >
>>>> > Programming Go is hard squared.
>>>> > ;^)
>>>>
>>>> And that on square boards.
>>>> Mama mia!
>>>>
>>>
>>> Go is hard for humans, but in my own opinion I think Go seems to be too
>>> easy for deep learning. So is programming Go now. :)
>>>
>>> Aja
>>>
>>>
>>>>
>>>> ;-) Ingo.
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>>
>>
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