>... my value network was trained to tell me the game is balanced at the
>beginning...
:-)
The best training policy is to select positions that correct errors.
I used the policies below to train a backgammon NN. Together, they reduced the
expected loss of the network by 50% (cut the error
Finally found the problem. In the end, it was as stupid as expected:
When I pick a game for the batch creation I select randomly a limited
number of moves inside the game. In the case of the value network I use
like 8-16 moves to not overfit the data (I can't take 1 or then the I/O
operations
This is what have been thinking about, yet unable to find an error.
Currently, I'm working with:
- SGF Database: fuseki info Tygem -> http://tygem.fuseki.info/index.php
(until recently I was working with games of all level from KGS)
- The data is then analyzed by a script which extracts all
On 19-06-17 17:38, Vincent Richard wrote:
> During my research, I’ve trained a lot of different networks, first on
> 9x9 then on 19x19, and as far as I remember all the nets I’ve worked
> with learned quickly (especially during the first batches), except the
> value net which has always been
Hello everyone,
For my master thesis, I have built an AI that has a strategical approach
to the game. It doesn’t play but simply describe the strategy behind all
possible move for a given strategy ("enclosing this group", "making life
for this group", "saving these stones", etc). My main idea
