[Computer-go] 9x9 is last frontier?

Brian Sheppard sheppardco at aol.com
Tue Mar 6 12:17:39 PST 2018


Well, AlphaZero did fine at chess tactics, and the papers are clear on the details. There must be an error in your deductions somewhere.

 

From: Computer-go [mailto:computer-go-bounces at computer-go.org] On Behalf Of Dan
Sent: Tuesday, March 6, 2018 1:46 PM
To: computer-go at computer-go.org
Subject: Re: [Computer-go] 9x9 is last frontier?

 

I am pretty sure it is an MCTS problem and I suspect not something that could be easily solved with a policy network (could be wrong hree). My opinon is that DCNN is not

a miracle worker (as somebody already mentioned here) and it is going to fail  resolving tactics.  I would be more than happy with it if it has same power as a qsearch to be honest.

 

Search traps are the major problem with games like Chess, and what makes transitioning the success of DCNN from Go to Chess non trivial.

The following paper discusses shallow traps that are prevalent in chess. ( https://www.aaai.org/ocs/index.php/ICAPS/ICAPS10/paper/download/1458/1571 )

They mention traps make MCTS very inefficient.  Even if the MCTS is given 50x more time is needed by an exhaustive minimax tree, it could fail to find a level-5 or level-7 trap.

It will spend, f.i, 95% of its time searching an asymetric tree of depth > 7 when a shallow trap of depth-7 exists, thus, missing to find the level-7 trap.

This is very hard to solve even if you have unlimited power.

 

The plain MCTS as used by AlphaZero is the most ill-suited MCTS version in my opinion and i have hard a hard time seeing how it can be competitive with Stockfish tactically.

 

My MCTS chess engine with  AlphaZero like MCTS was averaging was missing a lot of tactics. I don't use policy or eval networks but qsearch() for eval, and the policy is basically

choosing which ever moves leads to a higher eval.

 

a) My first improvement to the MCTS is to use minimax backups instead of averaging. This was an improvmenet but not something that would solve the traps

 

b) My second improvment is to use alphabeta rollouts. This is a rollouts version that can do nullmove and LMR etc... This is a huge improvment and none of the MCTS

versons can match it. More on alpha-beta rollouts here ( https://www.microsoft.com/en-us/research/wp-content/uploads/2014/11/huang_rollout.pdf )

 

So AlphaZero used none of the above improvements and yet it seems to be tactically strong. Leela-Zero suffered from tactical falls left and right too as I expected.

 

So the only explanation left is the policy network able to avoid traps which I find hard to believe it can identify more than a qsearch level tactics.

 

All I am saying is that my experience (as well as many others) with MCTS for tactical dominated games is bad, and there must be some breakthrough in that regard in AlphaZero

for it to be able to compete with Stockfish on a tactical level.

 

I am curious how Remi's attempt at Shogi using AlphaZero's method will turnout.

 

regards,

Daniel

 

 

 

 

 

 

 

 

On Tue, Mar 6, 2018 at 9:41 AM, Brian Sheppard via Computer-go <computer-go at computer-go.org <mailto:computer-go at computer-go.org> > wrote:

Training on Stockfish games is guaranteed to produce a blunder-fest, because there are no blunders in the training set and therefore the policy network never learns how to refute blunders.

 

This is not a flaw in MCTS, but rather in the policy network. MCTS will eventually search every move infinitely often, producing asymptotically optimal play. But if the policy network does not provide the guidance necessary to rapidly refute the blunders that occur in the search, then convergence of MCTS to optimal play will be very slow.

 

It is necessary for the network to train on self-play games using MCTS. For instance, the AGZ approach samples next states during training games by sampling from the distribution of visits in the search. Specifically: not by choosing the most-visited play!

 

You see how this policy trains both search and evaluation to be internally consistent? The policy head is trained to refute the bad moves that will come up in search, and the value head is trained to the value observed by the full tree. 

 

From: Computer-go [mailto:computer-go-bounces at computer-go.org <mailto:computer-go-bounces at computer-go.org> ] On Behalf Of Dan
Sent: Monday, March 5, 2018 4:55 AM
To: computer-go at computer-go.org <mailto:computer-go at computer-go.org> 
Subject: Re: [Computer-go] 9x9 is last frontier?

 

Actually prior to this it was trained with hundreds of thousands of stockfish games and didn’t do well on tactics (the games were actually a blunder fest). I believe this is a problem of the MCTS used and not due to for lack of training. 

 

Go is a strategic game so that is different from chess that is full of traps.     

I m not surprised Lela zero did well in go.

 

On Mon, Mar 5, 2018 at 2:16 AM Gian-Carlo Pascutto <gcp at sjeng.org <mailto:gcp at sjeng.org> > wrote:

On 02-03-18 17:07, Dan wrote:
> Leela-chess is not performing well enough

I don't understand how one can say that given that they started with the
random network last week only and a few clients. Of course it's bad!
That doesn't say anything about the approach.

Leela Zero has gotten strong but it has been learning for *months* with
~400 people. It also took a while to get to 30 kyu.

--
GCP
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