I did a quick test with my MCTS chess engine wth two different implementations. A standard MCTS with averaging, and MCTS with alpha-beta rollouts. The result is like a 600 elo difference
Finished game 44 (scorpio-pmcts vs scorpio-mcts): 1/2-1/2 {Draw by 3-fold repetition} Score of scorpio-mcts vs scorpio-pmcts: 41 - 1 - 2 [0.955] 44 Elo difference: 528.89 +/- nan scorpio-mcts uses alpha-beta rollouts scorpio-pmcts is "pure" mcts with averaging and UCB formula. Daniel On Tue, Mar 6, 2018 at 11:46 AM, Dan <dsha...@gmail.com> wrote: > 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@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-boun...@computer-go.org] *On >> Behalf Of *Dan >> *Sent:* Monday, March 5, 2018 4:55 AM >> *To:* computer-go@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 <g...@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 >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> >> >> _______________________________________________ >> Computer-go mailing list >> Computer-go@computer-go.org >> http://computer-go.org/mailman/listinfo/computer-go >> > >
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