Ah, right, the cases where you and your opponent's interests are not
perfectly anti-aligned make things a bit trickier, possibly introducing
some game theory into the mix. Then I don't know. :)
My first instinct is to say that in principle you should provide the neural
net both "must-win" paramete
The exact meaning of the result MCTS returns is irrelevant. The
net should just learn it.
___
Computer-go mailing list
Computer-go@computer-go.org
http://computer-go.org/mailman/listinfo/computer-go
Do you intend to use the same draw values for both sides in the self-play
games? They can be independent:
- in a 3/1/0 scenario, neither player is especially happy with a draw (and
in fact would rather each throw a game to each other in a two-game match
than make two draws, but that's a separate i
Actually this pretty much solves the whole issue right? Of course the proof
would be to actually test it out, but it seems to me a pretty
straightforward solution, not nontrivial at all.
On Feb 13, 2018 10:52 AM, "David Wu" wrote:
Seems to me like you could fix that in the policy too by providin
Seems to me like you could fix that in the policy too by providing an input
feature plane that indicates the value of a draw, whether 0 as normal, or
-1 for must-win, or -1/3 for 3/1/0, or 1 for only-need-not-lose, etc.
Then just play games with a variety of values for this parameter in your
self-
On 13-02-18 16:05, "Ingo Althöfer" wrote:
> Hello,
>
> what is known about proper MCTS procedures for games
> which do not only have wins and losses, but also draws
> (like chess, Shogi or Go with integral komi)?
>
> Should neural nets provide (win, draw, loss)-probabilities
> for positions in su
The AlphaZero paper says that they just assign values 1, 0, and -1 to wins,
draws, and losses respectively. This is fine for maximizing your expected
value over an infinite number of games given the way that chess tournaments
(to pick the example that I'm familiar with) are typically scored, where
Hello,
what is known about proper MCTS procedures for games
which do not only have wins and losses, but also draws
(like chess, Shogi or Go with integral komi)?
Should neural nets provide (win, draw, loss)-probabilities
for positions in such games?
Ingo.
_