Author: Vincent Diepeveen
Date: 18:22:40 07/06/03
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On July 06, 2003 at 17:52:59, Sune Fischer wrote: >On July 06, 2003 at 17:38:01, Vincent Diepeveen wrote: > >>If there was money to earn by programming a backgammon engine, i am sure some >>guys who are good in forward pruning algorithms like Johan de Koning would win >>every event there. It's like making a tictactoe program and then claiming that >>an ANN is going to work. >> >>As we have a saying here: "In the land of the blind, one eyed is King". >> >>That's why i focus upon chess. >> >>In contradiction to you, i know how to do it with ANNs (just like many others >>do), i just don't have 10^1000 system time to actually let the learning >>algorithm finish ;) >> >>Any approximation in the meantime will be playing very lousy chess... >> >>Hell, with 10^1000 runs, even TD learning might be correctly finding the right >>parameter optimization :) >> >>TD learning is randomly flipping a few parameters each time. It's pretty close >>to GA's in that respect. > >There might be tricks to speed that up, certainly can't rule it out before it >has been seriously attempted. how do you plan to speed it up without the learning having domain knowledge on chess? it just has n parameters. Let's say 15000 parameters to not make it too hard for it. It flips a few parameters. then it scores a bit better. then it flips a few other parameters. it scores worse. And so on. So it can at most do a logarithmic optimalisation for each parameter when it would be brute forcing. However it isn't brute forcing over each parameter but it tries to modify a bunch of parameters at a time. So you still are basically busy with a randomness with the 'luck' factor that it will find the optimal parameterization for each parameter in log(2)(n). that makes it on average (log n / log 2) ^ 15000 Now if you are lucky there are many optimizations which work well. For example it might not matter too much whether a pawn on a2 is given 0.022 of a pawn or 0.021 of a pawn. So there is a reduction there. However the finetuning is a major problem. not a single optimization algorithm is created having in mind a very good fine tuning. So a very rude tuning it will find pretty soon, but that will work worse than a hand tuning. A better tuning than you can do by hand however will take that x ^ 15000 where x is for sure > 2. Best regards, Vincent >For example one could try and do some kind of "anti-aliasing" on the board, ie. >smear the whole thing, to make the patterns more general. This would require >only a fraction of the full network and make it faster to train. > >I'm sure the ideas will come if people try it, what you see as problem others >may see as challenge :) > >-S.
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