[Computer-go] CLeelaz9Test on CGOS 9x9

Hi, It seems CLeelaz9Test rating has changed from 1700 to 2500. http://www.yss-aya.com/cgos/9x9/cross/CLeelaz9Test.html Could CLeelaz9Test author change its name when program get stronger? like CLeelaz9_1 or CLeelaz9_2. CGOS users may want to know correct rating quickly. Same program name but

Re: [Computer-go] MCTS with win-draw-loss scores

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"

Re: [Computer-go] MCTS with win-draw-loss scores

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

Re: [Computer-go] MCTS with win-draw-loss scores

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

Re: [Computer-go] MCTS with win-draw-loss scores

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

Re: [Computer-go] MCTS with win-draw-loss scores

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

Re: [Computer-go] MCTS with win-draw-loss scores

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

Re: [Computer-go] MCTS with win-draw-loss scores

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

[Computer-go] MCTS with win-draw-loss scores

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.